From 0ca3d4a1cc12047dd5c67a3c37b86817407e983f Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Wed, 22 May 2024 16:44:52 +0200 Subject: [PATCH 01/53] auto-fix more linting issues --- alphabase/constants/_const.py | 1 + alphabase/constants/aa.py | 11 +++--- alphabase/constants/atom.py | 6 ++-- alphabase/constants/element.py | 4 +-- alphabase/constants/isotope.py | 9 ++--- alphabase/constants/modification.py | 15 +++----- alphabase/io/hdf.py | 9 ++--- alphabase/io/tempmmap.py | 13 +++---- alphabase/peptide/fragment.py | 15 ++++---- alphabase/peptide/mass_calc.py | 15 ++++---- alphabase/peptide/mobility.py | 2 +- alphabase/peptide/precursor.py | 18 +++++----- alphabase/protein/fasta.py | 31 ++++++++--------- alphabase/protein/protein_level_decoy.py | 2 +- alphabase/psm_reader/__init__.py | 34 +++++++++++++------ alphabase/psm_reader/alphapept_reader.py | 7 ++-- alphabase/psm_reader/dia_psm_reader.py | 5 ++- alphabase/psm_reader/maxquant_reader.py | 10 +++--- alphabase/psm_reader/msfragger_reader.py | 11 +++--- alphabase/psm_reader/pfind_reader.py | 7 ++-- alphabase/psm_reader/psm_reader.py | 12 +++---- alphabase/psm_reader/sage_reader.py | 10 +++--- .../quant_reader/config_dict_loader.py | 9 ++--- .../quant_reader/longformat_reader.py | 17 ++++++---- .../quant_reader/quant_reader_manager.py | 5 ++- .../quant_reader/table_reformatter.py | 3 +- .../quant_reader/wideformat_reader.py | 4 +-- alphabase/spectral_library/base.py | 30 ++++++++-------- alphabase/spectral_library/decoy.py | 8 +++-- alphabase/spectral_library/flat.py | 8 ++--- alphabase/spectral_library/reader.py | 8 ++--- alphabase/spectral_library/translate.py | 11 +++--- alphabase/spectral_library/validate.py | 6 ++-- alphabase/utils.py | 9 ++--- docs/conf.py | 4 +-- pyproject.toml | 19 +++++++++-- release/pyinstaller/alphabase_pyinstaller.py | 5 +-- setup.py | 5 +-- 38 files changed, 209 insertions(+), 189 deletions(-) diff --git a/alphabase/constants/_const.py b/alphabase/constants/_const.py index 5fa3fed3..d7d5da91 100644 --- a/alphabase/constants/_const.py +++ b/alphabase/constants/_const.py @@ -1,4 +1,5 @@ import os + import numpy as np from alphabase.yaml_utils import load_yaml diff --git a/alphabase/constants/aa.py b/alphabase/constants/aa.py index a8c49a92..b8b27e7c 100644 --- a/alphabase/constants/aa.py +++ b/alphabase/constants/aa.py @@ -1,18 +1,17 @@ import os -import pandas as pd -import numpy as np import typing -from alphabase.yaml_utils import load_yaml +import numpy as np +import pandas as pd +from alphabase.constants._const import CONST_FILE_FOLDER from alphabase.constants.atom import ( - calc_mass_from_formula, MASS_H2O, + calc_mass_from_formula, parse_formula, reset_elements, ) - -from alphabase.constants._const import CONST_FILE_FOLDER +from alphabase.yaml_utils import load_yaml # We use all 128 ASCII code to represent amino acids for flexible extensions in the future. # The amino acid masses are stored in 128-lengh array :py:data:`AA_ASCII_MASS`. diff --git a/alphabase/constants/atom.py b/alphabase/constants/atom.py index b53b5e38..4d8e5f98 100644 --- a/alphabase/constants/atom.py +++ b/alphabase/constants/atom.py @@ -1,11 +1,11 @@ import os -import numpy as np -import numba import typing -from alphabase.yaml_utils import load_yaml +import numba +import numpy as np from alphabase.constants._const import CONST_FILE_FOLDER, common_const_dict +from alphabase.yaml_utils import load_yaml MASS_PROTON: float = common_const_dict["MASS_PROTON"] MASS_ISOTOPE: float = common_const_dict["MASS_ISOTOPE"] diff --git a/alphabase/constants/element.py b/alphabase/constants/element.py index d3803609..9d34ebaf 100644 --- a/alphabase/constants/element.py +++ b/alphabase/constants/element.py @@ -1,7 +1,7 @@ -from alphabase.constants.atom import * # noqa: F403 TODO remove in the next release - import warnings +from alphabase.constants.atom import * # noqa: F403 TODO remove in the next release + warnings.warn( "The module `alphabase.constants.element` is deprecated, " "it will be removed in alphabase>=1.3.0. " diff --git a/alphabase/constants/isotope.py b/alphabase/constants/isotope.py index a0c432ff..8feecc93 100644 --- a/alphabase/constants/isotope.py +++ b/alphabase/constants/isotope.py @@ -1,14 +1,15 @@ +import typing + import numba import numpy as np -import typing from alphabase.constants.atom import ( - MAX_ISOTOPE_LEN, - EMPTY_DIST, CHEM_ISOTOPE_DIST, CHEM_MONO_IDX, - truncate_isotope, + EMPTY_DIST, + MAX_ISOTOPE_LEN, parse_formula, + truncate_isotope, ) diff --git a/alphabase/constants/modification.py b/alphabase/constants/modification.py index a1627e7c..279ee6f0 100644 --- a/alphabase/constants/modification.py +++ b/alphabase/constants/modification.py @@ -1,17 +1,16 @@ import os +from typing import List, Union + import numba import numpy as np import pandas as pd -from typing import Union, List +from alphabase.constants._const import CONST_FILE_FOLDER from alphabase.constants.atom import ( calc_mass_from_formula, parse_formula, ) -from alphabase.constants._const import CONST_FILE_FOLDER - - #: Main entry of modification infomation (DataFrame fotmat). MOD_DF: pd.DataFrame = pd.DataFrame() @@ -138,9 +137,7 @@ def calc_modification_mass( """ masses = np.zeros(nAA) for site, mod in zip(mod_sites, mod_names): - if site == 0: - masses[site] += MOD_MASS[mod] - elif site == -1: + if site == 0 or site == -1: masses[site] += MOD_MASS[mod] else: masses[site - 1] += MOD_MASS[mod] @@ -178,9 +175,7 @@ def calc_mod_masses_for_same_len_seqs( masses = np.zeros((len(mod_names_list), nAA)) for i, (mod_names, mod_sites) in enumerate(zip(mod_names_list, mod_sites_list)): for mod, site in zip(mod_names, mod_sites): - if site == 0: - masses[i, site] += MOD_MASS[mod] - elif site == -1: + if site == 0 or site == -1: masses[i, site] += MOD_MASS[mod] else: masses[i, site - 1] += MOD_MASS[mod] diff --git a/alphabase/io/hdf.py b/alphabase/io/hdf.py index 26f12a8f..a9b41446 100644 --- a/alphabase/io/hdf.py +++ b/alphabase/io/hdf.py @@ -1,12 +1,13 @@ +import contextlib +import re +import time + import h5py import numpy as np import pandas as pd -import re -import contextlib -import time -class HDF_Object(object): +class HDF_Object: """ A generic class to access HDF components. """ diff --git a/alphabase/io/tempmmap.py b/alphabase/io/tempmmap.py index f8be53bc..7dfac298 100644 --- a/alphabase/io/tempmmap.py +++ b/alphabase/io/tempmmap.py @@ -2,16 +2,17 @@ """This module allows to create temporary mmapped arrays.""" # builtin -import os -import logging import atexit +import logging +import mmap +import os +import shutil +import tempfile + +import h5py # external import numpy as np -import mmap -import h5py -import tempfile -import shutil _TEMP_DIR = tempfile.TemporaryDirectory(prefix="temp_mmap_") TEMP_DIR_NAME = _TEMP_DIR.name diff --git a/alphabase/peptide/fragment.py b/alphabase/peptide/fragment.py index 39b5d912..ba0ff3c9 100644 --- a/alphabase/peptide/fragment.py +++ b/alphabase/peptide/fragment.py @@ -1,22 +1,21 @@ +from typing import Dict, List, Tuple, Union + +import numba as nb import numpy as np import pandas as pd -from typing import List, Union, Tuple, Dict -import numba as nb -from alphabase.constants._const import PEAK_MZ_DTYPE, PEAK_INTENSITY_DTYPE - -from alphabase.constants.modification import calc_modloss_mass +from alphabase.constants._const import PEAK_INTENSITY_DTYPE, PEAK_MZ_DTYPE from alphabase.constants.atom import ( MASS_PROTON, + calc_mass_from_formula, ) +from alphabase.constants.modification import calc_modloss_mass from alphabase.peptide.mass_calc import calc_b_y_and_peptide_masses_for_same_len_seqs from alphabase.peptide.precursor import ( - refine_precursor_df, is_precursor_refined, + refine_precursor_df, ) -from alphabase.constants.atom import calc_mass_from_formula - frag_type_representation_dict = { "c": "b+N(1)H(3)", "z": "y+N(-1)H(-2)", diff --git a/alphabase/peptide/mass_calc.py b/alphabase/peptide/mass_calc.py index b2ae9610..a1a60c70 100644 --- a/alphabase/peptide/mass_calc.py +++ b/alphabase/peptide/mass_calc.py @@ -1,17 +1,18 @@ -import numpy as np from typing import List, Tuple +import numpy as np + from alphabase.constants.aa import ( calc_AA_masses, calc_AA_masses_for_same_len_seqs, calc_sequence_masses_for_same_len_seqs, ) +from alphabase.constants.atom import MASS_H2O from alphabase.constants.modification import ( + calc_mod_masses_for_same_len_seqs, calc_modification_mass, calc_modification_mass_sum, - calc_mod_masses_for_same_len_seqs, ) -from alphabase.constants.atom import MASS_H2O def calc_diff_modification_mass( @@ -42,9 +43,7 @@ def calc_diff_modification_mass( """ masses = np.zeros(pep_len) for site, mass in zip(mass_diff_sites, mass_diffs): - if site == 0: - masses[site] += mass - elif site == -1: + if site == 0 or site == -1: masses[site] += mass else: masses[site - 1] += mass @@ -87,9 +86,7 @@ def calc_mod_diff_masses_for_same_len_seqs( zip(aa_mass_diffs_list, mod_sites_list) ): for mod_diff, site in zip(aa_mass_diffs, mod_sites): - if site == 0: - masses[i, site] += mod_diff - elif site == -1: + if site == 0 or site == -1: masses[i, site] += mod_diff else: masses[i, site - 1] += mod_diff diff --git a/alphabase/peptide/mobility.py b/alphabase/peptide/mobility.py index be4a443d..5326c9a1 100644 --- a/alphabase/peptide/mobility.py +++ b/alphabase/peptide/mobility.py @@ -1,8 +1,8 @@ import numpy as np import pandas as pd -from alphabase.peptide.precursor import update_precursor_mz from alphabase.constants.atom import common_const_dict +from alphabase.peptide.precursor import update_precursor_mz CCS_IM_COEF = common_const_dict["MOBILITY"]["CCS_IM_COEF"] IM_GAS_MASS = common_const_dict["MOBILITY"]["IM_GAS_MASS"] diff --git a/alphabase/peptide/precursor.py b/alphabase/peptide/precursor.py index 5e4e4125..c45bf968 100644 --- a/alphabase/peptide/precursor.py +++ b/alphabase/peptide/precursor.py @@ -1,17 +1,17 @@ -import pandas as pd -import numpy as np -import numba -import typing import multiprocessing as mp -from tqdm import tqdm +import typing +from functools import partial +import numba +import numpy as np +import pandas as pd +from tqdm import tqdm from xxhash import xxh64_intdigest -from functools import partial -from alphabase.constants.atom import MASS_PROTON, MASS_ISOTOPE from alphabase.constants.aa import AA_Composition -from alphabase.constants.modification import MOD_Composition +from alphabase.constants.atom import MASS_ISOTOPE, MASS_PROTON from alphabase.constants.isotope import IsotopeDistribution +from alphabase.constants.modification import MOD_Composition from alphabase.peptide.mass_calc import calc_peptide_masses_for_same_len_seqs @@ -586,7 +586,7 @@ def calc_precursor_isotope_intensity( isotope_dist = IsotopeDistribution() - col_names = ["i_{}".format(i) for i in range(max_isotope)] + col_names = [f"i_{i}" for i in range(max_isotope)] precursor_dist = np.zeros((len(precursor_df), max_isotope), dtype=np.float32) diff --git a/alphabase/protein/fasta.py b/alphabase/protein/fasta.py index a7e7f9df..174edffb 100644 --- a/alphabase/protein/fasta.py +++ b/alphabase/protein/fasta.py @@ -1,25 +1,22 @@ -import regex as re -import numpy as np -import pandas as pd -import numba -import os -import itertools import copy -import ahocorasick -from tqdm import tqdm - +import itertools +import os import warnings - -from Bio import SeqIO from typing import Union -from alphabase.yaml_utils import load_yaml -from alphabase.io.hdf import HDF_File -from alphabase.utils import explode_multiple_columns +import ahocorasick +import numba +import numpy as np +import pandas as pd +import regex as re +from Bio import SeqIO +from tqdm import tqdm from alphabase.constants._const import CONST_FILE_FOLDER - +from alphabase.io.hdf import HDF_File from alphabase.spectral_library.base import SpecLibBase +from alphabase.utils import explode_multiple_columns +from alphabase.yaml_utils import load_yaml def get_uniprot_gene_name(description: str): @@ -46,7 +43,7 @@ def read_fasta_file(fasta_filename: str = ""): protein information, {protein_id:str, full_name:str, gene_name:str, description:str, sequence:str} """ - with open(fasta_filename, "rt") as handle: + with open(fasta_filename) as handle: iterator = SeqIO.parse(handle, "fasta") while iterator: try: @@ -180,7 +177,7 @@ def cleave_sequence_with_cut_pos( return seq_list, miss_list, nterm_list, cterm_list -class Digest(object): +class Digest: def __init__( self, protease: str = "trypsin", diff --git a/alphabase/protein/protein_level_decoy.py b/alphabase/protein/protein_level_decoy.py index eadac7e6..3727c11a 100644 --- a/alphabase/protein/protein_level_decoy.py +++ b/alphabase/protein/protein_level_decoy.py @@ -1,7 +1,7 @@ import pandas as pd from alphabase.protein.fasta import SpecLibFasta -from alphabase.spectral_library.decoy import decoy_lib_provider, SpecLibDecoy +from alphabase.spectral_library.decoy import SpecLibDecoy, decoy_lib_provider class ProteinReverseDecoy(SpecLibDecoy): diff --git a/alphabase/psm_reader/__init__.py b/alphabase/psm_reader/__init__.py index 30e431ba..1c7f9579 100644 --- a/alphabase/psm_reader/__init__.py +++ b/alphabase/psm_reader/__init__.py @@ -15,38 +15,50 @@ "SageReaderParquet", ] -from alphabase.psm_reader.psm_reader import ( - psm_reader_provider, - psm_reader_yaml, - PSMReaderBase, -) from alphabase.psm_reader.alphapept_reader import ( AlphaPeptReader, +) +from alphabase.psm_reader.alphapept_reader import ( register_readers as register_ap_readers, ) from alphabase.psm_reader.dia_psm_reader import ( DiannReader, SpectronautReader, - SwathReader, SpectronautReportReader, + SwathReader, +) +from alphabase.psm_reader.dia_psm_reader import ( register_readers as register_dia_readers, ) from alphabase.psm_reader.maxquant_reader import ( MaxQuantReader, - register_readers as register_mq_readers, ) -from alphabase.psm_reader.pfind_reader import ( - pFindReader, - register_readers as register_pf_readers, +from alphabase.psm_reader.maxquant_reader import ( + register_readers as register_mq_readers, ) from alphabase.psm_reader.msfragger_reader import ( MSFragger_PSM_TSV_Reader, MSFraggerPepXML, +) +from alphabase.psm_reader.msfragger_reader import ( register_readers as register_fragger_readers, ) +from alphabase.psm_reader.pfind_reader import ( + pFindReader, +) +from alphabase.psm_reader.pfind_reader import ( + register_readers as register_pf_readers, +) +from alphabase.psm_reader.psm_reader import ( + PSMReaderBase, + psm_reader_provider, + psm_reader_yaml, +) from alphabase.psm_reader.sage_reader import ( - SageReaderTSV, SageReaderParquet, + SageReaderTSV, +) +from alphabase.psm_reader.sage_reader import ( register_readers as register_sage_readers, ) diff --git a/alphabase/psm_reader/alphapept_reader.py b/alphabase/psm_reader/alphapept_reader.py index 7b0e2ca5..0327fabc 100644 --- a/alphabase/psm_reader/alphapept_reader.py +++ b/alphabase/psm_reader/alphapept_reader.py @@ -1,8 +1,9 @@ -import numba import os -import pandas as pd -import numpy as np + import h5py +import numba +import numpy as np +import pandas as pd from alphabase.psm_reader.psm_reader import ( PSMReaderBase, diff --git a/alphabase/psm_reader/dia_psm_reader.py b/alphabase/psm_reader/dia_psm_reader.py index 0f651b2d..310f4485 100644 --- a/alphabase/psm_reader/dia_psm_reader.py +++ b/alphabase/psm_reader/dia_psm_reader.py @@ -1,9 +1,8 @@ -import pandas as pd import numpy as np - -from alphabase.psm_reader.psm_reader import psm_reader_provider, psm_reader_yaml +import pandas as pd from alphabase.psm_reader.maxquant_reader import MaxQuantReader +from alphabase.psm_reader.psm_reader import psm_reader_provider, psm_reader_yaml class SpectronautReader(MaxQuantReader): diff --git a/alphabase/psm_reader/maxquant_reader.py b/alphabase/psm_reader/maxquant_reader.py index 49e8202a..6087b02f 100644 --- a/alphabase/psm_reader/maxquant_reader.py +++ b/alphabase/psm_reader/maxquant_reader.py @@ -1,16 +1,16 @@ -import pandas as pd -import numpy as np -import numba import copy +import numba +import numpy as np +import pandas as pd + +from alphabase.constants.modification import MOD_DF from alphabase.psm_reader.psm_reader import ( PSMReaderBase, psm_reader_provider, psm_reader_yaml, ) -from alphabase.constants.modification import MOD_DF - mod_to_unimod_dict = {} for mod_name, unimod_id in MOD_DF[["mod_name", "unimod_id"]].values: unimod_id = int(unimod_id) diff --git a/alphabase/psm_reader/msfragger_reader.py b/alphabase/psm_reader/msfragger_reader.py index fde27480..56f9ef9c 100644 --- a/alphabase/psm_reader/msfragger_reader.py +++ b/alphabase/psm_reader/msfragger_reader.py @@ -1,16 +1,15 @@ import numpy as np import pandas as pd +import pyteomics.pepxml as pepxml +from alphabase.constants.aa import AA_ASCII_MASS +from alphabase.constants.atom import MASS_H, MASS_O +from alphabase.constants.modification import MOD_MASS from alphabase.psm_reader.psm_reader import ( PSMReaderBase, - psm_reader_yaml, psm_reader_provider, + psm_reader_yaml, ) -from alphabase.constants.aa import AA_ASCII_MASS -from alphabase.constants.atom import MASS_H, MASS_O -from alphabase.constants.modification import MOD_MASS - -import pyteomics.pepxml as pepxml def _is_fragger_decoy(proteins): diff --git a/alphabase/psm_reader/pfind_reader.py b/alphabase/psm_reader/pfind_reader.py index 6253d5fc..c5c61d4d 100644 --- a/alphabase/psm_reader/pfind_reader.py +++ b/alphabase/psm_reader/pfind_reader.py @@ -1,8 +1,7 @@ -import pandas as pd import numpy as np +import pandas as pd import alphabase.constants.modification as ap_mod - from alphabase.psm_reader.psm_reader import ( PSMReaderBase, psm_reader_provider, @@ -48,9 +47,7 @@ def translate_pFind_mod(mod_str): ret_mods = [] for mod in mod_str.split(";"): mod = convert_one_pFind_mod(mod) - if not mod: - return pd.NA - elif mod not in ap_mod.MOD_INFO_DICT: + if not mod or mod not in ap_mod.MOD_INFO_DICT: return pd.NA else: ret_mods.append(mod) diff --git a/alphabase/psm_reader/psm_reader.py b/alphabase/psm_reader/psm_reader.py index 53ed84f4..e57c1d92 100644 --- a/alphabase/psm_reader/psm_reader.py +++ b/alphabase/psm_reader/psm_reader.py @@ -1,13 +1,13 @@ -import os import copy -import pandas as pd -import numpy as np +import os import warnings +import numpy as np +import pandas as pd + import alphabase.peptide.mobility as mobility -from alphabase.peptide.precursor import update_precursor_mz, reset_precursor_df from alphabase.constants._const import CONST_FILE_FOLDER - +from alphabase.peptide.precursor import reset_precursor_df, update_precursor_mz from alphabase.utils import get_delimiter from alphabase.yaml_utils import load_yaml @@ -78,7 +78,7 @@ def keep_modifications(mod_str: str, mod_set: set) -> str: psm_reader_yaml = load_yaml(os.path.join(CONST_FILE_FOLDER, "psm_reader.yaml")) -class PSMReaderBase(object): +class PSMReaderBase: def __init__( self, *, diff --git a/alphabase/psm_reader/sage_reader.py b/alphabase/psm_reader/sage_reader.py index 71b7187f..9a47983d 100644 --- a/alphabase/psm_reader/sage_reader.py +++ b/alphabase/psm_reader/sage_reader.py @@ -1,17 +1,17 @@ -import numpy as np -import pandas as pd -import typing import re +import typing from functools import partial +import numpy as np +import pandas as pd + +from alphabase.constants.modification import MOD_DF from alphabase.psm_reader.psm_reader import ( PSMReaderBase, psm_reader_provider, psm_reader_yaml, ) -from alphabase.constants.modification import MOD_DF - def sage_spec_idx_from_scannr(scannr: str) -> int: """Extract the spectrum index from the scannr field in Sage output. diff --git a/alphabase/quantification/quant_reader/config_dict_loader.py b/alphabase/quantification/quant_reader/config_dict_loader.py index 70fe78e7..2cdf4aa1 100644 --- a/alphabase/quantification/quant_reader/config_dict_loader.py +++ b/alphabase/quantification/quant_reader/config_dict_loader.py @@ -1,11 +1,12 @@ +import itertools import os -import yaml -import pandas as pd import os.path import pathlib -import itertools import re +import pandas as pd +import yaml + INTABLE_CONFIG = os.path.join( pathlib.Path(__file__).parent.absolute(), "../../../alphabase/constants/const_files/quant_reader_config.yaml", @@ -57,7 +58,7 @@ def _get_seperator(input_file): def _load_config(config_yaml): - with open(config_yaml, "r") as stream: + with open(config_yaml) as stream: config_all = yaml.safe_load(stream) return config_all diff --git a/alphabase/quantification/quant_reader/longformat_reader.py b/alphabase/quantification/quant_reader/longformat_reader.py index 2301879c..88a31db7 100644 --- a/alphabase/quantification/quant_reader/longformat_reader.py +++ b/alphabase/quantification/quant_reader/longformat_reader.py @@ -1,14 +1,17 @@ -import pandas as pd +import glob import os +import os.path import shutil -import glob + import dask.dataframe as dd -import os.path +import pandas as pd -from . import config_dict_loader -from . import quantreader_utils -from . import table_reformatter -from . import plexdia_reformatter +from . import ( + config_dict_loader, + plexdia_reformatter, + quantreader_utils, + table_reformatter, +) def reformat_and_write_longtable_according_to_config( diff --git a/alphabase/quantification/quant_reader/quant_reader_manager.py b/alphabase/quantification/quant_reader/quant_reader_manager.py index 98dcb0b3..469c0821 100644 --- a/alphabase/quantification/quant_reader/quant_reader_manager.py +++ b/alphabase/quantification/quant_reader/quant_reader_manager.py @@ -1,7 +1,6 @@ import pandas as pd -from . import config_dict_loader -from . import longformat_reader -from . import wideformat_reader + +from . import config_dict_loader, longformat_reader, wideformat_reader def import_data( diff --git a/alphabase/quantification/quant_reader/table_reformatter.py b/alphabase/quantification/quant_reader/table_reformatter.py index 9f4b8bbe..70527265 100644 --- a/alphabase/quantification/quant_reader/table_reformatter.py +++ b/alphabase/quantification/quant_reader/table_reformatter.py @@ -1,6 +1,7 @@ -import pandas as pd import copy +import pandas as pd + def merge_protein_cols_and_config_dict( input_df, config_dict, use_alphaquant_format=False diff --git a/alphabase/quantification/quant_reader/wideformat_reader.py b/alphabase/quantification/quant_reader/wideformat_reader.py index b1fdaa66..52ce7cdf 100644 --- a/alphabase/quantification/quant_reader/wideformat_reader.py +++ b/alphabase/quantification/quant_reader/wideformat_reader.py @@ -1,6 +1,6 @@ import pandas as pd -from . import quantreader_utils -from . import table_reformatter + +from . import quantreader_utils, table_reformatter def reformat_and_write_wideformat_table(peptides_tsv, outfile_name, config_dict): diff --git a/alphabase/spectral_library/base.py b/alphabase/spectral_library/base.py index 972db75c..ba687112 100644 --- a/alphabase/spectral_library/base.py +++ b/alphabase/spectral_library/base.py @@ -1,31 +1,32 @@ -import pandas as pd -import numpy as np -import typing -import logging import copy -import warnings +import logging import re +import typing +import warnings + +import numpy as np +import pandas as pd +from alphabase.io.hdf import HDF_File from alphabase.peptide.fragment import ( - create_fragment_mz_dataframe, calc_fragment_count, + create_fragment_mz_dataframe, filter_fragment_number, join_left, remove_unused_fragments, ) from alphabase.peptide.precursor import ( - update_precursor_mz, - refine_precursor_df, - calc_precursor_isotope_intensity_mp, - calc_precursor_isotope_intensity, - calc_precursor_isotope_info_mp, calc_precursor_isotope_info, + calc_precursor_isotope_info_mp, + calc_precursor_isotope_intensity, + calc_precursor_isotope_intensity_mp, hash_precursor_df, + refine_precursor_df, + update_precursor_mz, ) -from alphabase.io.hdf import HDF_File -class SpecLibBase(object): +class SpecLibBase: """ Base spectral library in alphabase and alphapeptdeep. @@ -320,10 +321,9 @@ def append_decoy_sequence(self): ... ``` """ - from alphabase.spectral_library.decoy import decoy_lib_provider - # register 'protein_reverse' to the decoy_lib_provider from alphabase.protein.protein_level_decoy import register_decoy + from alphabase.spectral_library.decoy import decoy_lib_provider register_decoy() diff --git a/alphabase/spectral_library/decoy.py b/alphabase/spectral_library/decoy.py index 2f3e4dc2..76a02409 100644 --- a/alphabase/spectral_library/decoy.py +++ b/alphabase/spectral_library/decoy.py @@ -1,7 +1,9 @@ import copy +import multiprocessing as mp from typing import Any + import pandas as pd -import multiprocessing as mp + from alphabase.spectral_library.base import SpecLibBase @@ -11,7 +13,7 @@ def _batchify_series(series, mp_batch_size): yield series.iloc[i : i + mp_batch_size] -class BaseDecoyGenerator(object): +class BaseDecoyGenerator: """ Base class for decoy generator. A class is used instead of a function to make as it needs to be pickled for multiprocessing. @@ -208,7 +210,7 @@ def _remove_target_seqs(self): ) -class SpecLibDecoyProvider(object): +class SpecLibDecoyProvider: def __init__(self): self.decoy_dict = {} diff --git a/alphabase/spectral_library/flat.py b/alphabase/spectral_library/flat.py index 3257eb00..62e9db7c 100644 --- a/alphabase/spectral_library/flat.py +++ b/alphabase/spectral_library/flat.py @@ -1,11 +1,11 @@ -import pandas as pd -import numpy as np import warnings -from alphabase.spectral_library.base import SpecLibBase -from alphabase.peptide.fragment import flatten_fragments, remove_unused_fragments +import numpy as np +import pandas as pd from alphabase.io.hdf import HDF_File +from alphabase.peptide.fragment import flatten_fragments, remove_unused_fragments +from alphabase.spectral_library.base import SpecLibBase class SpecLibFlat(SpecLibBase): diff --git a/alphabase/spectral_library/reader.py b/alphabase/spectral_library/reader.py index d5d8c349..f5dd209a 100644 --- a/alphabase/spectral_library/reader.py +++ b/alphabase/spectral_library/reader.py @@ -1,15 +1,15 @@ import typing + import numpy as np import pandas as pd from tqdm import tqdm +from alphabase.constants._const import PEAK_INTENSITY_DTYPE from alphabase.peptide.mobility import mobility_to_ccs_for_df +from alphabase.psm_reader import psm_reader_provider from alphabase.psm_reader.maxquant_reader import MaxQuantReader -from alphabase.spectral_library.base import SpecLibBase from alphabase.psm_reader.psm_reader import psm_reader_yaml -from alphabase.psm_reader import psm_reader_provider - -from alphabase.constants._const import PEAK_INTENSITY_DTYPE +from alphabase.spectral_library.base import SpecLibBase class LibraryReaderBase(MaxQuantReader, SpecLibBase): diff --git a/alphabase/spectral_library/translate.py b/alphabase/spectral_library/translate.py index 5e710200..6a8f460c 100644 --- a/alphabase/spectral_library/translate.py +++ b/alphabase/spectral_library/translate.py @@ -1,14 +1,13 @@ -import pandas as pd -import numpy as np -import tqdm +import multiprocessing as mp import typing + import numba -import multiprocessing as mp +import numpy as np +import pandas as pd +import tqdm from alphabase.constants.modification import MOD_DF - from alphabase.spectral_library.base import SpecLibBase - from alphabase.utils import explode_multiple_columns diff --git a/alphabase/spectral_library/validate.py b/alphabase/spectral_library/validate.py index c4b9ac38..4c869322 100644 --- a/alphabase/spectral_library/validate.py +++ b/alphabase/spectral_library/validate.py @@ -1,7 +1,7 @@ -import pandas as pd -import numpy as np +from typing import List, Union -from typing import Union, List +import numpy as np +import pandas as pd class Column: diff --git a/alphabase/utils.py b/alphabase/utils.py index adc1d4a4..adb00faf 100644 --- a/alphabase/utils.py +++ b/alphabase/utils.py @@ -1,7 +1,8 @@ -import tqdm -import pandas as pd -import itertools import io +import itertools + +import pandas as pd +import tqdm # from alphatims @@ -39,7 +40,7 @@ def get_delimiter(tsv_file: str): line = tsv_file.readline().strip() tsv_file.seek(0) else: - with open(tsv_file, "r") as f: + with open(tsv_file) as f: line = f.readline().strip() if "\t" in line: return "\t" diff --git a/docs/conf.py b/docs/conf.py index 58851c17..597637c4 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -10,10 +10,10 @@ # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # -import os -import sys import importlib import inspect +import os +import sys sys.path.insert(0, os.path.abspath("..")) diff --git a/pyproject.toml b/pyproject.toml index 7f5d25d4..ebe33c6e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,19 @@ - [tool.ruff.lint] -select = ["E", "F"] +select = [ + # pycodestyle + "E", + # Pyflakes + "F", +# # pyupgrade +# "UP", +# # flake8-bugbear +# "B", +# # flake8-simplify +# "SIM", +# # isort +# "I", +] ignore = [ - "E501" # Line too long (ruff wraps code, but not docstrings) + "E501", # Line too long (ruff wraps code, but not docstrings) + "B028" # No explicit `stacklevel` keyword argument found (for warnings) ] diff --git a/release/pyinstaller/alphabase_pyinstaller.py b/release/pyinstaller/alphabase_pyinstaller.py index f4d40f19..74a4af7d 100644 --- a/release/pyinstaller/alphabase_pyinstaller.py +++ b/release/pyinstaller/alphabase_pyinstaller.py @@ -1,13 +1,14 @@ if __name__ == "__main__": try: - import alphabase.gui import multiprocessing + import alphabase.gui + multiprocessing.freeze_support() alphabase.gui.run() except Exception: - import traceback import sys + import traceback exc_info = sys.exc_info() # Display the *original* exception diff --git a/setup.py b/setup.py index e2859fc3..d3e36c2b 100644 --- a/setup.py +++ b/setup.py @@ -1,15 +1,16 @@ #!python # builtin -import setuptools import re +import setuptools + # local import alphabase as package2install def get_long_description(): - with open("README.md", "r") as readme_file: + with open("README.md") as readme_file: long_description = readme_file.read() return long_description From a8e12383348b4d7f2d230505b22e6becdf328cfe Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Wed, 22 May 2024 16:51:41 +0200 Subject: [PATCH 02/53] auto-fix more linting issues --- alphabase/constants/aa.py | 6 +- alphabase/constants/isotope.py | 18 +++--- alphabase/constants/modification.py | 5 +- alphabase/io/hdf.py | 5 +- alphabase/peptide/fragment.py | 59 ++++++++++--------- alphabase/peptide/precursor.py | 24 ++++---- alphabase/protein/fasta.py | 29 +++++---- alphabase/psm_reader/alphapept_reader.py | 7 +-- alphabase/psm_reader/maxquant_reader.py | 9 ++- alphabase/psm_reader/msfragger_reader.py | 5 +- alphabase/psm_reader/psm_reader.py | 22 +++---- .../quant_reader/config_dict_loader.py | 6 +- .../quant_reader/longformat_reader.py | 6 +- .../quant_reader/quantreader_utils.py | 7 ++- .../quant_reader/table_reformatter.py | 10 ++-- .../quant_reader/wideformat_reader.py | 2 +- alphabase/spectral_library/base.py | 31 ++++++---- alphabase/spectral_library/flat.py | 20 ++++--- alphabase/spectral_library/reader.py | 44 +++++++------- alphabase/spectral_library/translate.py | 2 +- alphabase/spectral_library/validate.py | 18 +++--- setup.py | 5 +- 22 files changed, 175 insertions(+), 165 deletions(-) diff --git a/alphabase/constants/aa.py b/alphabase/constants/aa.py index b8b27e7c..8cae7d7e 100644 --- a/alphabase/constants/aa.py +++ b/alphabase/constants/aa.py @@ -69,7 +69,7 @@ def reset_AA_df(): def reset_AA_Composition(): global AA_Composition AA_Composition = {} - for aa, formula, mass in AA_DF.values: + for aa, formula, _mass in AA_DF.values: AA_Composition[aa] = dict(parse_formula(formula)) return AA_Composition @@ -77,7 +77,9 @@ def reset_AA_Composition(): reset_AA_Composition() -def reset_AA_atoms(atom_replace_dict: typing.Dict = {}): +def reset_AA_atoms(atom_replace_dict: typing.Dict = None): + if atom_replace_dict is None: + atom_replace_dict = {} reset_elements() replace_atoms(atom_replace_dict) reset_AA_mass() diff --git a/alphabase/constants/isotope.py b/alphabase/constants/isotope.py index 8feecc93..82528fa0 100644 --- a/alphabase/constants/isotope.py +++ b/alphabase/constants/isotope.py @@ -155,14 +155,7 @@ def _calc_one_elem_cum_dist(element_cum_dist: np.ndarray, element_cum_mono: np.n class IsotopeDistribution: def __init__( self, - max_elem_num_dict: dict = { - "C": 2000, - "H": 5000, - "N": 1000, - "O": 1000, - "S": 200, - "P": 200, - }, + max_elem_num_dict: dict = None, ): """Faster calculation of isotope abundance distribution by pre-building isotope distribution tables for most common elements. @@ -193,6 +186,15 @@ def __init__( {element: mono position array of cumulated isotope distribution}, and mono position array is a 1-D int np.ndarray. """ + if max_elem_num_dict is None: + max_elem_num_dict = { + "C": 2000, + "H": 5000, + "N": 1000, + "O": 1000, + "S": 200, + "P": 200, + } self.element_to_cum_dist_dict = {} self.element_to_cum_mono_idx = {} for elem, n in max_elem_num_dict.items(): diff --git a/alphabase/constants/modification.py b/alphabase/constants/modification.py index 279ee6f0..6a3d9805 100644 --- a/alphabase/constants/modification.py +++ b/alphabase/constants/modification.py @@ -276,10 +276,7 @@ def calc_modloss_mass_with_importance( mod_losses = np.zeros(nAA + 2) mod_losses[mod_sites] = [MOD_LOSS_MASS[mod] for mod in mod_names] _loss_importance = np.zeros(nAA + 2) - _loss_importance[mod_sites] = [ - MOD_LOSS_IMPORTANCE[mod] if mod in MOD_LOSS_IMPORTANCE else 0 - for mod in mod_names - ] + _loss_importance[mod_sites] = [MOD_LOSS_IMPORTANCE.get(mod, 0) for mod in mod_names] # Will not consider the modloss if the corresponding modloss_importance is 0 mod_losses[_loss_importance == 0] = 0 diff --git a/alphabase/io/hdf.py b/alphabase/io/hdf.py index a9b41446..d4ffcc61 100644 --- a/alphabase/io/hdf.py +++ b/alphabase/io/hdf.py @@ -496,10 +496,7 @@ def __init__( >>> hdf_file.dfs.df1.data_from "colleagues" """ - if delete_existing: - mode = "w" - else: - mode = "a" + mode = "w" if delete_existing else "a" with h5py.File(file_name, mode): # , swmr=True): pass super().__init__( diff --git a/alphabase/peptide/fragment.py b/alphabase/peptide/fragment.py index ba0ff3c9..c65e079e 100644 --- a/alphabase/peptide/fragment.py +++ b/alphabase/peptide/fragment.py @@ -494,10 +494,12 @@ def mask_fragments_for_charge_greater_than_precursor_charge( precursor_charge_array: np.ndarray, nAA_array: np.ndarray, *, - candidate_fragment_charges: list = [2, 3, 4], + candidate_fragment_charges: list = None, ): """Mask the fragment dataframe when the fragment charge is larger than the precursor charge""" + if candidate_fragment_charges is None: + candidate_fragment_charges = [2, 3, 4] precursor_charge_array = np.repeat(precursor_charge_array, nAA_array - 1) for col in fragment_df.columns: for charge in candidate_fragment_charges: @@ -681,8 +683,8 @@ def flatten_fragments( fragment_intensity_df: pd.DataFrame, min_fragment_intensity: float = -1, keep_top_k_fragments: int = 1000, - custom_columns: list = ["type", "number", "position", "charge", "loss_type"], - custom_df: Dict[str, pd.DataFrame] = {}, + custom_columns: list = None, + custom_df: Dict[str, pd.DataFrame] = None, ) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Converts the tabular fragment format consisting of @@ -750,6 +752,10 @@ def flatten_fragments( - charge: uint8, fragment charge - loss_type: int16, fragment loss type, 0=noloss, 17=NH3, 18=H2O, 98=H3PO4 (phos), ... """ + if custom_df is None: + custom_df = {} + if custom_columns is None: + custom_columns = ["type", "number", "position", "charge", "loss_type"] if len(precursor_df) == 0: return precursor_df, pd.DataFrame() # new dataframes for fragments and precursors are created @@ -1047,25 +1053,24 @@ def create_fragment_mz_dataframe( pd.DataFrame `fragment_mz_df` with given `charged_frag_types` """ - if reference_fragment_df is None: - if "frag_start_idx" in precursor_df.columns: - # raise ValueError( - # "`precursor_df` contains 'frag_start_idx' column, "\ - # "please provide `reference_fragment_df` argument" - # ) - fragment_mz_df = init_fragment_by_precursor_dataframe( - precursor_df, - charged_frag_types, - dtype=dtype, - ) - return create_fragment_mz_dataframe( - precursor_df=precursor_df, - charged_frag_types=charged_frag_types, - reference_fragment_df=fragment_mz_df, - inplace_in_reference=True, - batch_size=batch_size, - dtype=dtype, - ) + if reference_fragment_df is None and "frag_start_idx" in precursor_df.columns: + # raise ValueError( + # "`precursor_df` contains 'frag_start_idx' column, "\ + # "please provide `reference_fragment_df` argument" + # ) + fragment_mz_df = init_fragment_by_precursor_dataframe( + precursor_df, + charged_frag_types, + dtype=dtype, + ) + return create_fragment_mz_dataframe( + precursor_df=precursor_df, + charged_frag_types=charged_frag_types, + reference_fragment_df=fragment_mz_df, + inplace_in_reference=True, + batch_size=batch_size, + dtype=dtype, + ) if "nAA" not in precursor_df.columns: # fast return create_fragment_mz_dataframe_by_sort_precursor( @@ -1255,12 +1260,10 @@ def filter_fragment_number( if not set(["frag_start_idx", "frag_stop_idx"]).issubset(precursor_df.columns): raise KeyError("frag_start_idx and frag_stop_idx not in dataframe") - for i, (start_idx, stop_idx, n_allowed_lib) in enumerate( - zip( - precursor_df["frag_start_idx"].values, - precursor_df["frag_stop_idx"].values, - precursor_df[n_fragments_allowed_column_name].values, - ) + for start_idx, stop_idx, n_allowed_lib in zip( + precursor_df["frag_start_idx"].values, + precursor_df["frag_stop_idx"].values, + precursor_df[n_fragments_allowed_column_name].values, ): _allowed = min(n_allowed_lib, n_allowed) diff --git a/alphabase/peptide/precursor.py b/alphabase/peptide/precursor.py index c45bf968..9f7ccfa3 100644 --- a/alphabase/peptide/precursor.py +++ b/alphabase/peptide/precursor.py @@ -25,18 +25,16 @@ def refine_precursor_df( """ if ensure_data_validity: df.fillna("", inplace=True) - if "charge" in df.columns: - if df.charge.dtype not in [ - "int", - "int8", - "int64", - "int32", - # np.int64, np.int32, np.int8, - ]: - df["charge"] = df["charge"].astype(np.int8) - if "mod_sites" in df.columns: - if df.mod_sites.dtype not in ["O", "U"]: - df["mod_sites"] = df.mod_sites.astype("U") + if "charge" in df.columns and df.charge.dtype not in [ + "int", + "int8", + "int64", + "int32", + # np.int64, np.int32, np.int8, + ]: + df["charge"] = df["charge"].astype(np.int8) + if "mod_sites" in df.columns and df.mod_sites.dtype not in ["O", "U"]: + df["mod_sites"] = df.mod_sites.astype("U") if "nAA" not in df.columns: df["nAA"] = df.sequence.str.len().astype(np.int32) @@ -107,7 +105,7 @@ def update_precursor_mz( # precursor_mz_idx = precursor_df.columns.get_loc( # 'precursor_mz' # ) - for nAA, big_df_group in _grouped: + for _, big_df_group in _grouped: for i in range(0, len(big_df_group), batch_size): batch_end = i + batch_size diff --git a/alphabase/protein/fasta.py b/alphabase/protein/fasta.py index 174edffb..3c5e6dc6 100644 --- a/alphabase/protein/fasta.py +++ b/alphabase/protein/fasta.py @@ -429,8 +429,8 @@ def add_single_peptide_labeling( nterm_label_mod: str, cterm_label_mod: str, ): - add_nterm_label = True if nterm_label_mod else False - add_cterm_label = True if cterm_label_mod else False + add_nterm_label = bool(nterm_label_mod) + add_cterm_label = bool(cterm_label_mod) if mod_sites: _sites = mod_sites.split(";") if "0" in _sites: @@ -478,10 +478,7 @@ def create_labeling_peptide_df( if len(peptide_df) == 0: return peptide_df - if inplace: - df = peptide_df - else: - df = peptide_df.copy() + df = peptide_df if inplace else peptide_df.copy() (label_aas, label_mod_dict, nterm_label_mod, cterm_label_mod) = parse_labels(labels) @@ -507,7 +504,7 @@ def protein_idxes_to_names(protein_idxes: str, protein_names: list): def append_special_modifications( df: pd.DataFrame, - var_mods: list = ["Phospho@S", "Phospho@T", "Phospho@Y"], + var_mods: list = None, min_mod_num: int = 0, max_mod_num: int = 1, max_peptidoform_num: int = 100, @@ -553,6 +550,8 @@ def append_special_modifications( pd.DataFrame The precursor_df with new modification added. """ + if var_mods is None: + var_mods = ["Phospho@S", "Phospho@T", "Phospho@Y"] if len(var_mods) == 0 or len(df) == 0: return df @@ -644,7 +643,7 @@ class SpecLibFasta(SpecLibBase): def __init__( self, - charged_frag_types: list = ["b_z1", "b_z2", "y_z1", "y_z2"], + charged_frag_types: list = None, *, protease: str = "trypsin", max_missed_cleavages: int = 2, @@ -654,12 +653,12 @@ def __init__( precursor_charge_max: int = 4, precursor_mz_min: float = 400.0, precursor_mz_max: float = 2000.0, - var_mods: list = ["Acetyl@Protein_N-term", "Oxidation@M"], + var_mods: list = None, min_var_mod_num: int = 0, max_var_mod_num: int = 2, - fix_mods: list = ["Carbamidomethyl@C"], + fix_mods: list = None, labeling_channels: dict = None, - special_mods: list = [], + special_mods: list = None, min_special_mod_num: int = 0, max_special_mod_num: int = 1, special_mods_cannot_modify_pep_n_term: bool = False, @@ -758,6 +757,14 @@ def __init__( include_contaminants : bool, optional If include contaminants.fasta, by default False """ + if special_mods is None: + special_mods = [] + if fix_mods is None: + fix_mods = ["Carbamidomethyl@C"] + if var_mods is None: + var_mods = ["Acetyl@Protein_N-term", "Oxidation@M"] + if charged_frag_types is None: + charged_frag_types = ["b_z1", "b_z2", "y_z1", "y_z2"] super().__init__( charged_frag_types=charged_frag_types, precursor_mz_min=precursor_mz_min, diff --git a/alphabase/psm_reader/alphapept_reader.py b/alphabase/psm_reader/alphapept_reader.py index 0327fabc..df59f8fa 100644 --- a/alphabase/psm_reader/alphapept_reader.py +++ b/alphabase/psm_reader/alphapept_reader.py @@ -18,10 +18,7 @@ def parse_ap(precursor): Parser to parse peptide strings """ items = precursor.split("_") - if len(items) == 3: - decoy = 1 - else: - decoy = 0 + decoy = 1 if len(items) == 3 else 0 modseq = items[0] charge = items[-1] @@ -81,7 +78,7 @@ def _init_modification_mapping(self): def _load_file(self, filename): with h5py.File(filename, "r") as _hdf: dataset = _hdf[self.hdf_dataset] - df = pd.DataFrame({col: dataset[col] for col in dataset.keys()}) + df = pd.DataFrame({col: dataset[col] for col in dataset}) df["raw_name"] = os.path.basename(filename)[: -len(".ms_data.hdf")] df["precursor"] = df["precursor"].str.decode("utf-8") # df['naked_sequence'] = df['naked_sequence'].str.decode('utf-8') diff --git a/alphabase/psm_reader/maxquant_reader.py b/alphabase/psm_reader/maxquant_reader.py index 6087b02f..96c5a67c 100644 --- a/alphabase/psm_reader/maxquant_reader.py +++ b/alphabase/psm_reader/maxquant_reader.py @@ -75,10 +75,7 @@ def parse_mod_seq( 0 for N-term; -1 for C-term; 1 to N for normal modifications. """ PeptideModSeq = modseq - if modseq[0] == "_": - underscore_for_ncterm = True - else: - underscore_for_ncterm = False + underscore_for_ncterm = modseq[0] == "_" mod_list = [] site_list = [] site = PeptideModSeq.find(mod_sep[0]) @@ -136,7 +133,7 @@ def __init__( fdr=0.01, keep_decoy=False, fixed_C57=True, - mod_seq_columns=["Modified sequence"], + mod_seq_columns=None, **kwargs, ): """Reader for MaxQuant msms.txt and evidence.txt @@ -168,6 +165,8 @@ def __init__( The columns to find modified sequences, by default ['Modified sequence'] """ + if mod_seq_columns is None: + mod_seq_columns = ["Modified sequence"] super().__init__( column_mapping=column_mapping, modification_mapping=modification_mapping, diff --git a/alphabase/psm_reader/msfragger_reader.py b/alphabase/psm_reader/msfragger_reader.py index 56f9ef9c..005f23d8 100644 --- a/alphabase/psm_reader/msfragger_reader.py +++ b/alphabase/psm_reader/msfragger_reader.py @@ -13,10 +13,7 @@ def _is_fragger_decoy(proteins): - for prot in proteins: - if not prot.lower().startswith("rev_"): - return False - return True + return all(prot.lower().startswith("rev_") for prot in proteins) mass_mapped_mods = psm_reader_yaml["msfragger_pepxml"]["mass_mapped_mods"] diff --git a/alphabase/psm_reader/psm_reader.py b/alphabase/psm_reader/psm_reader.py index e57c1d92..9721cdc8 100644 --- a/alphabase/psm_reader/psm_reader.py +++ b/alphabase/psm_reader/psm_reader.py @@ -344,7 +344,7 @@ def normalize_rt_by_raw_name(self): self.norm_rt() if "raw_name" not in self.psm_df.columns: return - for raw_name, df_group in self.psm_df.groupby("raw_name"): + for _, df_group in self.psm_df.groupby("raw_name"): self.psm_df.loc[df_group.index, "rt_norm"] = ( df_group.rt_norm / df_group.rt_norm.max() ) @@ -510,19 +510,21 @@ def _post_process(self, origin_df: pd.DataFrame): def filter_psm_by_modifications( self, - include_mod_set=set( - [ - "Oxidation@M", - "Phospho@S", - "Phospho@T", - "Phospho@Y", - "Acetyl@Protein N-term", - ] - ), + include_mod_set=None, ): """ Only keeps peptides with modifications in `include_mod_list`. """ + if include_mod_set is None: + include_mod_set = set( + [ + "Oxidation@M", + "Phospho@S", + "Phospho@T", + "Phospho@Y", + "Acetyl@Protein N-term", + ] + ) self._psm_df.mods = self._psm_df.mods.apply( keep_modifications, mod_set=include_mod_set ) diff --git a/alphabase/quantification/quant_reader/config_dict_loader.py b/alphabase/quantification/quant_reader/config_dict_loader.py index 2cdf4aa1..1f37adbc 100644 --- a/alphabase/quantification/quant_reader/config_dict_loader.py +++ b/alphabase/quantification/quant_reader/config_dict_loader.py @@ -24,7 +24,7 @@ def get_input_type_and_config_dict(input_file, input_type_to_use=None): uploaded_data_columns = set(pd.read_csv(input_file, sep=sep, nrows=1).columns) - for input_type in type2relevant_columns.keys(): + for input_type in type2relevant_columns: if (input_type_to_use is not None) and (input_type != input_type_to_use): continue relevant_columns = type2relevant_columns.get(input_type) @@ -65,7 +65,7 @@ def _load_config(config_yaml): def _get_type2relevant_cols(config_all): type2relcols = {} - for type in config_all.keys(): + for type in config_all: config_typedict = config_all.get(type) relevant_cols = get_relevant_columns_config_dict(config_typedict) type2relcols[type] = relevant_cols @@ -78,7 +78,7 @@ def get_relevant_columns_config_dict(config_typedict): for filtconf in config_typedict.get("filters", {}).values(): filtcols.append(filtconf.get("param")) - if "ion_hierarchy" in config_typedict.keys(): + if "ion_hierarchy" in config_typedict: for headr in config_typedict.get("ion_hierarchy").values(): ioncols = list(itertools.chain.from_iterable(headr.get("mapping").values())) dict_ioncols.extend(ioncols) diff --git a/alphabase/quantification/quant_reader/longformat_reader.py b/alphabase/quantification/quant_reader/longformat_reader.py index 88a31db7..a89c9952 100644 --- a/alphabase/quantification/quant_reader/longformat_reader.py +++ b/alphabase/quantification/quant_reader/longformat_reader.py @@ -153,7 +153,7 @@ def adapt_subtable(input_df_subset, config_dict, use_alphaquant_format): input_df_subset = quantreader_utils.filter_input( config_dict.get("filters", {}), input_df_subset ) - if "ion_hierarchy" in config_dict.keys(): + if "ion_hierarchy" in config_dict: return table_reformatter.merge_protein_cols_and_config_dict( input_df_subset, config_dict, use_alphaquant_format ) @@ -240,9 +240,9 @@ def process_with_dask( def get_hierarchy_names_from_config_dict(config_dict_for_type): hierarchy_names = [] - if "ion_hierarchy" in config_dict_for_type.keys(): + if "ion_hierarchy" in config_dict_for_type: ion_hierarchy = config_dict_for_type.get("ion_hierarchy") - for hierarchy_type in ion_hierarchy.keys(): + for hierarchy_type in ion_hierarchy: hierarchy_names += ion_hierarchy.get(hierarchy_type).get("order") return list(set(hierarchy_names)) else: diff --git a/alphabase/quantification/quant_reader/quantreader_utils.py b/alphabase/quantification/quant_reader/quantreader_utils.py index 49c094fc..9c01071e 100644 --- a/alphabase/quantification/quant_reader/quantreader_utils.py +++ b/alphabase/quantification/quant_reader/quantreader_utils.py @@ -1,3 +1,6 @@ +import contextlib + + def filter_input(filter_dict, input): if filter_dict is None: return input @@ -14,10 +17,8 @@ def filter_input(filter_dict, input): if comparator == "==": input = input[input[param] == value] continue - try: + with contextlib.suppress(Exception): input = input.astype({f"{param}": "float"}) - except Exception: - pass if comparator == ">": input = input[input[param].astype(type(value)) > value] diff --git a/alphabase/quantification/quant_reader/table_reformatter.py b/alphabase/quantification/quant_reader/table_reformatter.py index 70527265..4c221f1d 100644 --- a/alphabase/quantification/quant_reader/table_reformatter.py +++ b/alphabase/quantification/quant_reader/table_reformatter.py @@ -25,7 +25,7 @@ def merge_protein_cols_and_config_dict( input_df = input_df.drop(columns=[x for x in protein_cols if x != "protein"]) index_names = [] - for hierarchy_type in ion_hierarchy.keys(): + for hierarchy_type in ion_hierarchy: df_subset = input_df.copy() ion_hierarchy_local = ion_hierarchy.get(hierarchy_type).get("order") ion_headers_merged, ion_headers_grouped = get_ionname_columns( @@ -35,13 +35,13 @@ def merge_protein_cols_and_config_dict( df_subset, hierarchy_type, config_dict, ion_headers_merged ) headers = list(set(ion_headers_merged + quant_columns + ["protein"])) - if "sample_ID" in config_dict.keys(): + if "sample_ID" in config_dict: headers += [config_dict.get("sample_ID")] df_subset = df_subset[headers].drop_duplicates() if splitcol2sep is not None: if ( - quant_columns[0] in splitcol2sep.keys() + quant_columns[0] in splitcol2sep ): # in the case that quantitative values are stored grouped in one column (e.g. msiso1,msiso2,msiso3, etc.), reformat accordingly df_subset = split_extend_df(df_subset, splitcol2sep) ion_headers_grouped = adapt_headers_on_extended_df( @@ -97,7 +97,7 @@ def get_quantitative_columns(input_df, hierarchy_type, config_dict, ion_headers_ quantcolumn_candidates = [ x for x in input_df.columns if x not in naming_columns ] - if "quant_pre_or_suffix" in config_dict.keys(): + if "quant_pre_or_suffix" in config_dict: return [ x for x in quantcolumn_candidates @@ -147,7 +147,7 @@ def adapt_headers_on_extended_df(ion_headers_grouped, splitcol2sep): for vals in ion_headers_grouped_copy: if splitcol2sep is not None: for idx in range(len(vals)): - if vals[idx] in splitcol2sep.keys(): + if vals[idx] in splitcol2sep: vals[idx] = vals[idx] + "_idxs" return ion_headers_grouped_copy diff --git a/alphabase/quantification/quant_reader/wideformat_reader.py b/alphabase/quantification/quant_reader/wideformat_reader.py index 52ce7cdf..1e7c03a3 100644 --- a/alphabase/quantification/quant_reader/wideformat_reader.py +++ b/alphabase/quantification/quant_reader/wideformat_reader.py @@ -12,7 +12,7 @@ def reformat_and_write_wideformat_table(peptides_tsv, outfile_name, config_dict) input_df = table_reformatter.merge_protein_cols_and_config_dict( input_df, config_dict ) - if "quant_pre_or_suffix" in config_dict.keys(): + if "quant_pre_or_suffix" in config_dict: quant_pre_or_suffix = config_dict.get("quant_pre_or_suffix") headers = ["protein", "quant_id"] + list( filter( diff --git a/alphabase/spectral_library/base.py b/alphabase/spectral_library/base.py index ba687112..fc84404f 100644 --- a/alphabase/spectral_library/base.py +++ b/alphabase/spectral_library/base.py @@ -80,7 +80,7 @@ def __init__( # ['b_z1','b_z2','y_z1','y_modloss_z1', ...]; # 'b_z1': 'b' is the fragment type and # 'z1' is the charge state z=1. - charged_frag_types: typing.List[str] = ["b_z1", "b_z2", "y_z1", "y_z2"], + charged_frag_types: typing.List[str] = None, precursor_mz_min=400, precursor_mz_max=6000, decoy: str = None, @@ -104,7 +104,11 @@ def __init__( Decoy methods, could be "pseudo_reverse" or "diann". Defaults to None. """ - self.charged_frag_types = charged_frag_types + self.charged_frag_types = ( + ["b_z1", "b_z2", "y_z1", "y_z2"] + if charged_frag_types is None + else charged_frag_types + ) self._precursor_df = pd.DataFrame() self._fragment_intensity_df = pd.DataFrame() self._fragment_mz_df = pd.DataFrame() @@ -190,12 +194,7 @@ def copy(self): def append( self, other: "SpecLibBase", - dfs_to_append: typing.List[str] = [ - "_precursor_df", - "_fragment_intensity_df", - "_fragment_mz_df", - "_fragment_intensity_predicted_df", - ], + dfs_to_append: typing.List[str] = None, remove_unused_dfs: bool = True, ): """ @@ -223,6 +222,13 @@ def append( None """ + if dfs_to_append is None: + dfs_to_append = [ + "_precursor_df", + "_fragment_intensity_df", + "_fragment_mz_df", + "_fragment_intensity_predicted_df", + ] if remove_unused_dfs: current_frag_dfs = self.available_dense_fragment_dfs() for attr in current_frag_dfs: @@ -265,11 +271,10 @@ def check_matching_columns(df1, df2): n_fragments = [] # get subset of dfs_to_append starting with _fragment for attr in dfs_to_append: - if attr.startswith("_fragment"): - if hasattr(self, attr): - n_current_fragments = len(getattr(self, attr)) - if n_current_fragments > 0: - n_fragments += [n_current_fragments] + if attr.startswith("_fragment") and hasattr(self, attr): + n_current_fragments = len(getattr(self, attr)) + if n_current_fragments > 0: + n_fragments += [n_current_fragments] if not np.all(np.array(n_fragments) == n_fragments[0]): raise ValueError( diff --git a/alphabase/spectral_library/flat.py b/alphabase/spectral_library/flat.py index 62e9db7c..3af62c78 100644 --- a/alphabase/spectral_library/flat.py +++ b/alphabase/spectral_library/flat.py @@ -38,16 +38,10 @@ class SpecLibFlat(SpecLibBase): def __init__( self, - charged_frag_types: list = ["b_z1", "b_z2", "y_z1", "y_z2"], + charged_frag_types: list = None, min_fragment_intensity: float = 0.001, keep_top_k_fragments: int = 1000, - custom_fragment_df_columns: list = [ - "type", - "number", - "position", - "charge", - "loss_type", - ], + custom_fragment_df_columns: list = None, **kwargs, ): """ @@ -63,6 +57,16 @@ def __init__( See :attr:`custom_fragment_df_columns`, defaults to ['type','number','position','charge','loss_type'] """ + if custom_fragment_df_columns is None: + custom_fragment_df_columns = [ + "type", + "number", + "position", + "charge", + "loss_type", + ] + if charged_frag_types is None: + charged_frag_types = ["b_z1", "b_z2", "y_z1", "y_z2"] super().__init__(charged_frag_types=charged_frag_types) self.min_fragment_intensity = min_fragment_intensity self.keep_top_k_fragments = keep_top_k_fragments diff --git a/alphabase/spectral_library/reader.py b/alphabase/spectral_library/reader.py index f5dd209a..ed5e5975 100644 --- a/alphabase/spectral_library/reader.py +++ b/alphabase/spectral_library/reader.py @@ -15,16 +15,7 @@ class LibraryReaderBase(MaxQuantReader, SpecLibBase): def __init__( self, - charged_frag_types: typing.List[str] = [ - "b_z1", - "b_z2", - "y_z1", - "y_z2", - "b_modloss_z1", - "b_modloss_z2", - "y_modloss_z1", - "y_modloss_z2", - ], + charged_frag_types: typing.List[str] = None, column_mapping: dict = None, modification_mapping: dict = None, fdr=0.01, @@ -79,6 +70,17 @@ def __init__( Can be either `pseudo_reverse` or `diann` """ + if charged_frag_types is None: + charged_frag_types = [ + "b_z1", + "b_z2", + "y_z1", + "y_z2", + "b_modloss_z1", + "b_modloss_z2", + "y_modloss_z1", + "y_modloss_z2", + ] SpecLibBase.__init__( self, charged_frag_types=charged_frag_types, @@ -293,21 +295,23 @@ def _post_process( class LibraryReaderFromRawData(SpecLibBase): def __init__( self, - charged_frag_types: typing.List[str] = [ - "b_z1", - "b_z2", - "y_z1", - "y_z2", - "b_modloss_z1", - "b_modloss_z2", - "y_modloss_z1", - "y_modloss_z2", - ], + charged_frag_types: typing.List[str] = None, precursor_mz_min: float = 400, precursor_mz_max: float = 2000, decoy: str = None, **kwargs, ): + if charged_frag_types is None: + charged_frag_types = [ + "b_z1", + "b_z2", + "y_z1", + "y_z2", + "b_modloss_z1", + "b_modloss_z2", + "y_modloss_z1", + "y_modloss_z2", + ] super().__init__( charged_frag_types=charged_frag_types, precursor_mz_min=precursor_mz_min, diff --git a/alphabase/spectral_library/translate.py b/alphabase/spectral_library/translate.py index 6a8f460c..b163a37d 100644 --- a/alphabase/spectral_library/translate.py +++ b/alphabase/spectral_library/translate.py @@ -120,7 +120,7 @@ def merge_precursor_fragment_df( iters = enumerate(df[["frag_start_idx", "frag_stop_idx"]].values) if verbose: iters = tqdm.tqdm(iters) - for i, (start, end) in iters: + for _i, (start, end) in iters: intens = fragment_inten_df.iloc[start:end, :].to_numpy( copy=True ) # is loc[start:end-1,:] faster? diff --git a/alphabase/spectral_library/validate.py b/alphabase/spectral_library/validate.py index 4c869322..3396d577 100644 --- a/alphabase/spectral_library/validate.py +++ b/alphabase/spectral_library/validate.py @@ -72,17 +72,15 @@ def __call__(self, df: pd.DataFrame): f"Validation failed: Column {self.name} of type {_get_type_name(df[self.name].dtype)} cannot be cast to {_get_type_name(self.type)}" ) - if not self.allow_NaN: - if df[self.name].isna().any(): - raise ValueError( - f"Validation failed: Column {self.name} contains NaN values" - ) + if not self.allow_NaN and df[self.name].isna().any(): + raise ValueError( + f"Validation failed: Column {self.name} contains NaN values" + ) - if not self.allow_inf: - if not np.isfinite(df[self.name]).all(): - raise ValueError( - f"Validation failed: Column {self.name} contains inf values" - ) + if not self.allow_inf and not np.isfinite(df[self.name]).all(): + raise ValueError( + f"Validation failed: Column {self.name} contains inf values" + ) class Optional(Column): diff --git a/setup.py b/setup.py index d3e36c2b..b803c302 100644 --- a/setup.py +++ b/setup.py @@ -21,10 +21,7 @@ def get_requirements(): requirement_file_names[""] = "requirements.txt" for extra, requirement_file_name in requirement_file_names.items(): with open(requirement_file_name) as requirements_file: - if extra != "": - extra_stable = f"{extra}-stable" - else: - extra_stable = "stable" + extra_stable = f"{extra}-stable" if extra != "" else "stable" extra_requirements[extra_stable] = [] extra_requirements[extra] = [] for line in requirements_file: From dae9f9527f38d80ed4c91e42a618de1f0a6e30c7 Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Wed, 22 May 2024 16:54:57 +0200 Subject: [PATCH 03/53] fix more linting issues --- alphabase/io/hdf.py | 16 +++++++++------- alphabase/io/tempmmap.py | 9 ++++----- alphabase/psm_reader/psm_reader.py | 7 ++++--- alphabase/spectral_library/base.py | 12 +++++++----- alphabase/utils.py | 2 +- pyproject.toml | 16 ++++++++-------- 6 files changed, 33 insertions(+), 29 deletions(-) diff --git a/alphabase/io/hdf.py b/alphabase/io/hdf.py index d4ffcc61..8262d9e1 100644 --- a/alphabase/io/hdf.py +++ b/alphabase/io/hdf.py @@ -199,14 +199,16 @@ def set_truncate(self, truncate: bool = True): def __setattr__(self, name, value): try: super().__setattr__(name, value) - except NotImplementedError: + except NotImplementedError as e: if not self.truncate: if name in self.group_names: - raise KeyError(f"Group name '{name}' cannot be truncated") + raise KeyError(f"Group name '{name}' cannot be truncated") from e elif name in self.dataset_names: - raise KeyError(f"Dataset name '{name}' cannot be truncated") + raise KeyError(f"Dataset name '{name}' cannot be truncated") from e elif name in self.dataframe_names: - raise KeyError(f"Dataframe name '{name}' cannot be truncated") + raise KeyError( + f"Dataframe name '{name}' cannot be truncated" + ) from e if isinstance(value, (np.ndarray, pd.core.series.Series)): self.add_dataset(name, value) elif isinstance(value, (dict, pd.DataFrame)): @@ -217,7 +219,7 @@ def __setattr__(self, name, value): "Only (str, bool, int, float, np.ndarray, " "pd.core.series.Series, dict pd.DataFrame) types are " "accepted.", - ) + ) from e def add_dataset( self, @@ -252,12 +254,12 @@ def add_dataset( # chunks=array.shape, maxshape=tuple([None for i in array.shape]), ) - except TypeError: + except TypeError as e: raise NotImplementedError( f"Type {array.dtype} is not understood. " "If this is a string format, try to cast it to " "np.dtype('O') as possible solution." - ) + ) from e dataset = HDF_Dataset( file_name=self.file_name, name=f"{self.name}/{name}", diff --git a/alphabase/io/tempmmap.py b/alphabase/io/tempmmap.py index 7dfac298..a4d7b0d8 100644 --- a/alphabase/io/tempmmap.py +++ b/alphabase/io/tempmmap.py @@ -119,11 +119,10 @@ def create_empty_mmap(shape: tuple, dtype: np.dtype, path: str = None, overwrite ) else: # check that if overwrite is false the file does not already exist - if not overwrite: - if os.path.exists(path): - raise ValueError( - "The file already exists. Set overwrite to True to overwrite the file or choose a different name." - ) + if not overwrite and os.path.exists(path): + raise ValueError( + "The file already exists. Set overwrite to True to overwrite the file or choose a different name." + ) if not os.path.basename.endswith(".hdf"): raise ValueError("The chosen file name needs to end with .hdf") if os.path.isdir(os.path.commonpath(path)): diff --git a/alphabase/psm_reader/psm_reader.py b/alphabase/psm_reader/psm_reader.py index 9721cdc8..0ff51dbb 100644 --- a/alphabase/psm_reader/psm_reader.py +++ b/alphabase/psm_reader/psm_reader.py @@ -240,9 +240,10 @@ def _reverse_mod_mapping(self): for this_mod, other_mod in self.modification_mapping.items(): if isinstance(other_mod, (list, tuple)): for _mod in other_mod: - if _mod in self.rev_mod_mapping: - if this_mod.endswith("Protein N-term"): - continue + if _mod in self.rev_mod_mapping and this_mod.endswith( + "Protein N-term" + ): + continue self.rev_mod_mapping[_mod] = this_mod else: self.rev_mod_mapping[other_mod] = this_mod diff --git a/alphabase/spectral_library/base.py b/alphabase/spectral_library/base.py index fc84404f..47a31b20 100644 --- a/alphabase/spectral_library/base.py +++ b/alphabase/spectral_library/base.py @@ -281,19 +281,21 @@ def check_matching_columns(df1, df2): "The libraries can't be appended as the number of fragments in the current libraries are not the same." ) - for attr, matching_columns in zip(dfs_to_append, matching_columns): + for attr, column in zip(dfs_to_append, matching_columns): if hasattr(self, attr) and hasattr(other, attr): current_df = getattr(self, attr) # copy dataframes to avoid changing the original ones - other_df = getattr(other, attr)[matching_columns].copy() + other_df = getattr(other, attr)[column].copy() if attr.startswith("_precursor"): frag_idx_increment = 0 for fragment_df in ["_fragment_intensity_df", "_fragment_mz_df"]: - if hasattr(self, fragment_df): - if len(getattr(self, fragment_df)) > 0: - frag_idx_increment = len(getattr(self, fragment_df)) + if ( + hasattr(self, fragment_df) + and len(getattr(self, fragment_df)) > 0 + ): + frag_idx_increment = len(getattr(self, fragment_df)) if "frag_start_idx" in other_df.columns: other_df["frag_start_idx"] += frag_idx_increment diff --git a/alphabase/utils.py b/alphabase/utils.py index adb00faf..7bb7fa3c 100644 --- a/alphabase/utils.py +++ b/alphabase/utils.py @@ -9,7 +9,7 @@ def process_bar(iterator, len_iter): with tqdm.tqdm(total=len_iter) as bar: i = 0 - for i, iter in enumerate(iterator): + for i, iter in enumerate(iterator): # noqa: B007 yield iter bar.update() bar.update(len_iter - i - 1) diff --git a/pyproject.toml b/pyproject.toml index ebe33c6e..4847eeee 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,14 +4,14 @@ select = [ "E", # Pyflakes "F", -# # pyupgrade -# "UP", -# # flake8-bugbear -# "B", -# # flake8-simplify -# "SIM", -# # isort -# "I", + # pyupgrade + "UP", + # flake8-bugbear + "B", + # flake8-simplify + "SIM", + # isort + "I", ] ignore = [ "E501", # Line too long (ruff wraps code, but not docstrings) From fd03c81662f2245c3240922d748728fe0b4ea193 Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Wed, 12 Jun 2024 09:11:29 +0200 Subject: [PATCH 04/53] revert back tp mutable arguments --- alphabase/constants/aa.py | 4 +--- alphabase/constants/isotope.py | 18 ++++++++---------- alphabase/peptide/fragment.py | 12 +++--------- alphabase/protein/fasta.py | 20 +++++--------------- alphabase/spectral_library/base.py | 22 ++++++++-------------- alphabase/spectral_library/flat.py | 20 ++++++++------------ alphabase/spectral_library/reader.py | 22 ++++++++++------------ pyproject.toml | 3 ++- 8 files changed, 45 insertions(+), 76 deletions(-) diff --git a/alphabase/constants/aa.py b/alphabase/constants/aa.py index 8cae7d7e..c2334e9e 100644 --- a/alphabase/constants/aa.py +++ b/alphabase/constants/aa.py @@ -77,9 +77,7 @@ def reset_AA_Composition(): reset_AA_Composition() -def reset_AA_atoms(atom_replace_dict: typing.Dict = None): - if atom_replace_dict is None: - atom_replace_dict = {} +def reset_AA_atoms(atom_replace_dict: typing.Dict = {}): reset_elements() replace_atoms(atom_replace_dict) reset_AA_mass() diff --git a/alphabase/constants/isotope.py b/alphabase/constants/isotope.py index 82528fa0..8feecc93 100644 --- a/alphabase/constants/isotope.py +++ b/alphabase/constants/isotope.py @@ -155,7 +155,14 @@ def _calc_one_elem_cum_dist(element_cum_dist: np.ndarray, element_cum_mono: np.n class IsotopeDistribution: def __init__( self, - max_elem_num_dict: dict = None, + max_elem_num_dict: dict = { + "C": 2000, + "H": 5000, + "N": 1000, + "O": 1000, + "S": 200, + "P": 200, + }, ): """Faster calculation of isotope abundance distribution by pre-building isotope distribution tables for most common elements. @@ -186,15 +193,6 @@ def __init__( {element: mono position array of cumulated isotope distribution}, and mono position array is a 1-D int np.ndarray. """ - if max_elem_num_dict is None: - max_elem_num_dict = { - "C": 2000, - "H": 5000, - "N": 1000, - "O": 1000, - "S": 200, - "P": 200, - } self.element_to_cum_dist_dict = {} self.element_to_cum_mono_idx = {} for elem, n in max_elem_num_dict.items(): diff --git a/alphabase/peptide/fragment.py b/alphabase/peptide/fragment.py index c65e079e..2eb4a6d9 100644 --- a/alphabase/peptide/fragment.py +++ b/alphabase/peptide/fragment.py @@ -494,12 +494,10 @@ def mask_fragments_for_charge_greater_than_precursor_charge( precursor_charge_array: np.ndarray, nAA_array: np.ndarray, *, - candidate_fragment_charges: list = None, + candidate_fragment_charges: list = [2, 3, 4], ): """Mask the fragment dataframe when the fragment charge is larger than the precursor charge""" - if candidate_fragment_charges is None: - candidate_fragment_charges = [2, 3, 4] precursor_charge_array = np.repeat(precursor_charge_array, nAA_array - 1) for col in fragment_df.columns: for charge in candidate_fragment_charges: @@ -683,8 +681,8 @@ def flatten_fragments( fragment_intensity_df: pd.DataFrame, min_fragment_intensity: float = -1, keep_top_k_fragments: int = 1000, - custom_columns: list = None, - custom_df: Dict[str, pd.DataFrame] = None, + custom_columns: list = ["type", "number", "position", "charge", "loss_type"], + custom_df: Dict[str, pd.DataFrame] = {}, ) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Converts the tabular fragment format consisting of @@ -752,10 +750,6 @@ def flatten_fragments( - charge: uint8, fragment charge - loss_type: int16, fragment loss type, 0=noloss, 17=NH3, 18=H2O, 98=H3PO4 (phos), ... """ - if custom_df is None: - custom_df = {} - if custom_columns is None: - custom_columns = ["type", "number", "position", "charge", "loss_type"] if len(precursor_df) == 0: return precursor_df, pd.DataFrame() # new dataframes for fragments and precursors are created diff --git a/alphabase/protein/fasta.py b/alphabase/protein/fasta.py index 3c5e6dc6..5de7670b 100644 --- a/alphabase/protein/fasta.py +++ b/alphabase/protein/fasta.py @@ -504,7 +504,7 @@ def protein_idxes_to_names(protein_idxes: str, protein_names: list): def append_special_modifications( df: pd.DataFrame, - var_mods: list = None, + var_mods: list = ["Phospho@S", "Phospho@T", "Phospho@Y"], min_mod_num: int = 0, max_mod_num: int = 1, max_peptidoform_num: int = 100, @@ -550,8 +550,6 @@ def append_special_modifications( pd.DataFrame The precursor_df with new modification added. """ - if var_mods is None: - var_mods = ["Phospho@S", "Phospho@T", "Phospho@Y"] if len(var_mods) == 0 or len(df) == 0: return df @@ -643,7 +641,7 @@ class SpecLibFasta(SpecLibBase): def __init__( self, - charged_frag_types: list = None, + charged_frag_types: list = ["b_z1", "b_z2", "y_z1", "y_z2"], *, protease: str = "trypsin", max_missed_cleavages: int = 2, @@ -653,12 +651,12 @@ def __init__( precursor_charge_max: int = 4, precursor_mz_min: float = 400.0, precursor_mz_max: float = 2000.0, - var_mods: list = None, + var_mods: list = ["Acetyl@Protein_N-term", "Oxidation@M"], min_var_mod_num: int = 0, max_var_mod_num: int = 2, - fix_mods: list = None, + fix_mods: list = ["Carbamidomethyl@C"], labeling_channels: dict = None, - special_mods: list = None, + special_mods: list = [], min_special_mod_num: int = 0, max_special_mod_num: int = 1, special_mods_cannot_modify_pep_n_term: bool = False, @@ -757,14 +755,6 @@ def __init__( include_contaminants : bool, optional If include contaminants.fasta, by default False """ - if special_mods is None: - special_mods = [] - if fix_mods is None: - fix_mods = ["Carbamidomethyl@C"] - if var_mods is None: - var_mods = ["Acetyl@Protein_N-term", "Oxidation@M"] - if charged_frag_types is None: - charged_frag_types = ["b_z1", "b_z2", "y_z1", "y_z2"] super().__init__( charged_frag_types=charged_frag_types, precursor_mz_min=precursor_mz_min, diff --git a/alphabase/spectral_library/base.py b/alphabase/spectral_library/base.py index 47a31b20..cf605207 100644 --- a/alphabase/spectral_library/base.py +++ b/alphabase/spectral_library/base.py @@ -80,7 +80,7 @@ def __init__( # ['b_z1','b_z2','y_z1','y_modloss_z1', ...]; # 'b_z1': 'b' is the fragment type and # 'z1' is the charge state z=1. - charged_frag_types: typing.List[str] = None, + charged_frag_types: typing.List[str] = ["b_z1", "b_z2", "y_z1", "y_z2"], precursor_mz_min=400, precursor_mz_max=6000, decoy: str = None, @@ -104,11 +104,7 @@ def __init__( Decoy methods, could be "pseudo_reverse" or "diann". Defaults to None. """ - self.charged_frag_types = ( - ["b_z1", "b_z2", "y_z1", "y_z2"] - if charged_frag_types is None - else charged_frag_types - ) + self.charged_frag_types = charged_frag_types self._precursor_df = pd.DataFrame() self._fragment_intensity_df = pd.DataFrame() self._fragment_mz_df = pd.DataFrame() @@ -194,7 +190,12 @@ def copy(self): def append( self, other: "SpecLibBase", - dfs_to_append: typing.List[str] = None, + dfs_to_append: typing.List[str] = [ + "_precursor_df", + "_fragment_intensity_df", + "_fragment_mz_df", + "_fragment_intensity_predicted_df", + ], remove_unused_dfs: bool = True, ): """ @@ -222,13 +223,6 @@ def append( None """ - if dfs_to_append is None: - dfs_to_append = [ - "_precursor_df", - "_fragment_intensity_df", - "_fragment_mz_df", - "_fragment_intensity_predicted_df", - ] if remove_unused_dfs: current_frag_dfs = self.available_dense_fragment_dfs() for attr in current_frag_dfs: diff --git a/alphabase/spectral_library/flat.py b/alphabase/spectral_library/flat.py index 3af62c78..62e9db7c 100644 --- a/alphabase/spectral_library/flat.py +++ b/alphabase/spectral_library/flat.py @@ -38,10 +38,16 @@ class SpecLibFlat(SpecLibBase): def __init__( self, - charged_frag_types: list = None, + charged_frag_types: list = ["b_z1", "b_z2", "y_z1", "y_z2"], min_fragment_intensity: float = 0.001, keep_top_k_fragments: int = 1000, - custom_fragment_df_columns: list = None, + custom_fragment_df_columns: list = [ + "type", + "number", + "position", + "charge", + "loss_type", + ], **kwargs, ): """ @@ -57,16 +63,6 @@ def __init__( See :attr:`custom_fragment_df_columns`, defaults to ['type','number','position','charge','loss_type'] """ - if custom_fragment_df_columns is None: - custom_fragment_df_columns = [ - "type", - "number", - "position", - "charge", - "loss_type", - ] - if charged_frag_types is None: - charged_frag_types = ["b_z1", "b_z2", "y_z1", "y_z2"] super().__init__(charged_frag_types=charged_frag_types) self.min_fragment_intensity = min_fragment_intensity self.keep_top_k_fragments = keep_top_k_fragments diff --git a/alphabase/spectral_library/reader.py b/alphabase/spectral_library/reader.py index ed5e5975..6461ef00 100644 --- a/alphabase/spectral_library/reader.py +++ b/alphabase/spectral_library/reader.py @@ -15,7 +15,16 @@ class LibraryReaderBase(MaxQuantReader, SpecLibBase): def __init__( self, - charged_frag_types: typing.List[str] = None, + charged_frag_types: typing.List[str] = [ + "b_z1", + "b_z2", + "y_z1", + "y_z2", + "b_modloss_z1", + "b_modloss_z2", + "y_modloss_z1", + "y_modloss_z2", + ], column_mapping: dict = None, modification_mapping: dict = None, fdr=0.01, @@ -70,17 +79,6 @@ def __init__( Can be either `pseudo_reverse` or `diann` """ - if charged_frag_types is None: - charged_frag_types = [ - "b_z1", - "b_z2", - "y_z1", - "y_z2", - "b_modloss_z1", - "b_modloss_z2", - "y_modloss_z1", - "y_modloss_z2", - ] SpecLibBase.__init__( self, charged_frag_types=charged_frag_types, diff --git a/pyproject.toml b/pyproject.toml index 4847eeee..565e4a72 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,5 +15,6 @@ select = [ ] ignore = [ "E501", # Line too long (ruff wraps code, but not docstrings) - "B028" # No explicit `stacklevel` keyword argument found (for warnings) + "B028", # No explicit `stacklevel` keyword argument found (for warnings) + "B006", # Do not use mutable data structures for argument defaults # TODO: fix this! ] From 6a574602fb38755fd074db4928f52ff887fc3156 Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Wed, 12 Jun 2024 14:28:05 +0200 Subject: [PATCH 05/53] tolerate mutable arguments in ruff --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 565e4a72..659c6123 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,5 +16,5 @@ select = [ ignore = [ "E501", # Line too long (ruff wraps code, but not docstrings) "B028", # No explicit `stacklevel` keyword argument found (for warnings) - "B006", # Do not use mutable data structures for argument defaults # TODO: fix this! + #"B006", # Do not use mutable data structures for argument defaults # TODO: fix this! ] From 7811213fba89b2522b4514d4bacd3a6eb3408d4d Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Wed, 12 Jun 2024 14:28:18 +0200 Subject: [PATCH 06/53] tolerate mutable arguments in ruff --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 659c6123..565e4a72 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,5 +16,5 @@ select = [ ignore = [ "E501", # Line too long (ruff wraps code, but not docstrings) "B028", # No explicit `stacklevel` keyword argument found (for warnings) - #"B006", # Do not use mutable data structures for argument defaults # TODO: fix this! + "B006", # Do not use mutable data structures for argument defaults # TODO: fix this! ] From 894e4a2615d808583c2f8b607f0a3a80a2f4b2e0 Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Mon, 24 Jun 2024 13:48:06 +0200 Subject: [PATCH 07/53] test --- nbs_tests/psm_reader/sage_reader.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/nbs_tests/psm_reader/sage_reader.ipynb b/nbs_tests/psm_reader/sage_reader.ipynb index cb1f90f5..b19b901a 100644 --- a/nbs_tests/psm_reader/sage_reader.ipynb +++ b/nbs_tests/psm_reader/sage_reader.ipynb @@ -58,7 +58,9 @@ "outputs": [], "source": [ "#| hide\n", - "assert capture_modifications('Q[-17.026548]DQSANEKNK[+42.010567]LEM[+15.9949]NK[+42.010567]', annotated_mod_df) == ('0;9;12;14', 'Gln->pyro-Glu@Q^Any N-term;Acetyl@K;Oxidation@M;Acetyl@K')" + "result = capture_modifications('Q[-17.026548]DQSANEKNK[+42.010567]LEM[+15.9949]NK[+42.010567]', annotated_mod_df) \n", + "expected = ('0;9;12;14', 'Gln->pyro-Glu@Q^Any N-term;Acetyl@K;Oxidation@M;Acetyl@K') \n", + "assert result ==expected, f\"Expected: {expected}, got: {result}\"" ] }, { From 3f38a892f3e468217538ed766ca7d1edf6a0a5ee Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Tue, 25 Jun 2024 13:57:27 +0200 Subject: [PATCH 08/53] remove modification names with whitespaces --- .../constants/const_files/modification.tsv | 602 +++++++++--------- scripts/unimod_to_tsv.ipynb | 188 +++--- 2 files changed, 410 insertions(+), 380 deletions(-) diff --git a/alphabase/constants/const_files/modification.tsv b/alphabase/constants/const_files/modification.tsv index 49ed5d0e..8ba1b0cc 100644 --- a/alphabase/constants/const_files/modification.tsv +++ b/alphabase/constants/const_files/modification.tsv @@ -1,16 +1,16 @@ mod_name unimod_mass unimod_avge_mass composition unimod_modloss modloss_composition classification unimod_id modloss_importance Acetyl@T 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 -Acetyl@Protein N-term 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 +Acetyl@Protein_N-term 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 Acetyl@S 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 Acetyl@C 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 -Acetyl@Any N-term 42.010565 42.0367 H(2)C(2)O(1) 0.0 Multiple 1 0.0 +Acetyl@Any_N-term 42.010565 42.0367 H(2)C(2)O(1) 0.0 Multiple 1 0.0 Acetyl@K 42.010565 42.0367 H(2)C(2)O(1) 0.0 Multiple 1 0.0 Acetyl@Y 42.010565 42.0367 H(2)C(2)O(1) 0.0 Chemical derivative 1 0.0 Acetyl@H 42.010565 42.0367 H(2)C(2)O(1) 0.0 Chemical derivative 1 0.0 Acetyl@R 42.010565 42.0367 H(2)C(2)O(1) 0.0 Artefact 1 0.0 -Amidated@Any C-term -0.984016 -0.9848 H(1)N(1)O(-1) 0.0 Artefact 2 0.0 -Amidated@Protein C-term -0.984016 -0.9848 H(1)N(1)O(-1) 0.0 Post-translational 2 0.0 -Biotin@Any N-term 226.077598 226.2954 H(14)C(10)N(2)O(2)S(1) 0.0 Chemical derivative 3 0.0 +Amidated@Any_C-term -0.984016 -0.9848 H(1)N(1)O(-1) 0.0 Artefact 2 0.0 +Amidated@Protein_C-term -0.984016 -0.9848 H(1)N(1)O(-1) 0.0 Post-translational 2 0.0 +Biotin@Any_N-term 226.077598 226.2954 H(14)C(10)N(2)O(2)S(1) 0.0 Chemical derivative 3 0.0 Biotin@K 226.077598 226.2954 H(14)C(10)N(2)O(2)S(1) 0.0 Post-translational 3 0.0 Carbamidomethyl@Y 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 Carbamidomethyl@T 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 @@ -18,7 +18,7 @@ Carbamidomethyl@S 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 Carbamidomethyl@E 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 Carbamidomethyl@D 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 Carbamidomethyl@H 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@Any N-term 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@Any_N-term 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 Carbamidomethyl@K 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 Carbamidomethyl@C 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 4 0.0 Carbamidomethyl@U 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 4 0.0 @@ -29,10 +29,10 @@ Carbamyl@S 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Chemical derivative 5 0.0 Carbamyl@M 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 Carbamyl@C 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 Carbamyl@R 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 -Carbamyl@Any N-term 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Multiple 5 0.0 +Carbamyl@Any_N-term 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Multiple 5 0.0 Carbamyl@K 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Multiple 5 0.0 -Carbamyl@Protein N-term 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Post-translational 5 0.0 -Carboxymethyl@Any N-term 58.005479 58.0361 H(2)C(2)O(2) 0.0 Artefact 6 0.0 +Carbamyl@Protein_N-term 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Post-translational 5 0.0 +Carboxymethyl@Any_N-term 58.005479 58.0361 H(2)C(2)O(2) 0.0 Artefact 6 0.0 Carboxymethyl@K 58.005479 58.0361 H(2)C(2)O(2) 0.0 Artefact 6 0.0 Carboxymethyl@C 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 Carboxymethyl@W 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 @@ -40,11 +40,11 @@ Carboxymethyl@U 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 Deamidated@Q 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Artefact 7 0.0 Deamidated@R 0.984016 0.9848 H(-1)N(-1)O(1) 43.005814 H(1)C(1)N(1)O(1) Post-translational 7 0.5 Deamidated@N 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Artefact 7 0.0 -Deamidated@F^Protein N-term 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Post-translational 7 0.0 +Deamidated@F^Protein_N-term 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Post-translational 7 0.0 ICAT-G@C 486.251206 486.6253 H(38)C(22)N(4)O(6)S(1) 0.0 Isotopic label 8 0.0 ICAT-G:2H(8)@C 494.30142 494.6746 H(30)2H(8)C(22)N(4)O(6)S(1) 0.0 Isotopic label 9 0.0 -Met->Hse@M^Any C-term -29.992806 -30.0922 H(-2)C(-1)O(1)S(-1) 0.0 Chemical derivative 10 0.0 -Met->Hsl@M^Any C-term -48.003371 -48.1075 H(-4)C(-1)S(-1) 0.0 Chemical derivative 11 0.0 +Met->Hse@M^Any_C-term -29.992806 -30.0922 H(-2)C(-1)O(1)S(-1) 0.0 Chemical derivative 10 0.0 +Met->Hsl@M^Any_C-term -48.003371 -48.1075 H(-4)C(-1)S(-1) 0.0 Chemical derivative 11 0.0 ICAT-D:2H(8)@C 450.275205 450.6221 H(26)2H(8)C(20)N(4)O(5)S(1) 0.0 Isotopic label 12 0.0 ICAT-D@C 442.224991 442.5728 H(34)C(20)N(4)O(5)S(1) 0.0 Isotopic label 13 0.0 NIPCAM@C 99.068414 99.1311 H(9)C(5)N(1)O(1) 0.0 Chemical derivative 17 0.0 @@ -68,32 +68,32 @@ Dehydrated@D -18.010565 -18.0153 H(-2)O(-1) 0.0 Chemical derivative 23 0.0 Dehydrated@Y -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 Dehydrated@T -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 Dehydrated@S -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 -Dehydrated@N^Protein C-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 -Dehydrated@Q^Protein C-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 -Dehydrated@C^Any N-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Artefact 23 0.0 +Dehydrated@N^Protein_C-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 +Dehydrated@Q^Protein_C-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 +Dehydrated@C^Any_N-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Artefact 23 0.0 Propionamide@C 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Artefact 24 0.0 Propionamide@K 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Chemical derivative 24 0.0 -Propionamide@Any N-term 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Chemical derivative 24 0.0 -Pyridylacetyl@Any N-term 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 25 0.0 +Propionamide@Any_N-term 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Chemical derivative 24 0.0 +Pyridylacetyl@Any_N-term 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 25 0.0 Pyridylacetyl@K 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 25 0.0 -Pyro-carbamidomethyl@C^Any N-term 39.994915 40.0208 C(2)O(1) 0.0 Artefact 26 0.0 -Glu->pyro-Glu@E^Any N-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Artefact 27 0.0 -Gln->pyro-Glu@Q^Any N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Artefact 28 0.0 -SMA@Any N-term 127.063329 127.1412 H(9)C(6)N(1)O(2) 0.0 Chemical derivative 29 0.0 +Pyro-carbamidomethyl@C^Any_N-term 39.994915 40.0208 C(2)O(1) 0.0 Artefact 26 0.0 +Glu->pyro-Glu@E^Any_N-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Artefact 27 0.0 +Gln->pyro-Glu@Q^Any_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Artefact 28 0.0 +SMA@Any_N-term 127.063329 127.1412 H(9)C(6)N(1)O(2) 0.0 Chemical derivative 29 0.0 SMA@K 127.063329 127.1412 H(9)C(6)N(1)O(2) 0.0 Chemical derivative 29 0.0 Cation:Na@D 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 -Cation:Na@Any C-term 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 +Cation:Na@Any_C-term 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 Cation:Na@E 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 Pyridylethyl@C 105.057849 105.1372 H(7)C(7)N(1) 0.0 Chemical derivative 31 0.0 Methyl@E 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 Methyl@D 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@Any C-term 14.01565 14.0266 H(2)C(1) 0.0 Multiple 34 0.0 -Methyl@Protein N-term 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@Any_C-term 14.01565 14.0266 H(2)C(1) 0.0 Multiple 34 0.0 +Methyl@Protein_N-term 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 Methyl@L 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 Methyl@I 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 Methyl@R 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 Methyl@Q 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@Any N-term 14.01565 14.0266 H(2)C(1) 0.0 Chemical derivative 34 0.0 +Methyl@Any_N-term 14.01565 14.0266 H(2)C(1) 0.0 Chemical derivative 34 0.0 Methyl@N 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 Methyl@K 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 Methyl@H 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 @@ -107,7 +107,7 @@ Oxidation@Q 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 Oxidation@L 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 Oxidation@I 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 Oxidation@U 15.994915 15.9994 O(1) 0.0 Multiple 35 0.0 -Oxidation@G^Any C-term 15.994915 15.9994 O(1) 0.0 Pre-translational 35 0.0 +Oxidation@G^Any_C-term 15.994915 15.9994 O(1) 0.0 Pre-translational 35 0.0 Oxidation@W 15.994915 15.9994 O(1) 0.0 Artefact 35 0.0 Oxidation@C 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 Oxidation@H 15.994915 15.9994 O(1) 0.0 Artefact 35 0.0 @@ -120,20 +120,20 @@ Oxidation@P 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 Oxidation@N 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 Oxidation@K 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 Oxidation@D 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 -Dimethyl@Protein N-term 28.0313 28.0532 H(4)C(2) 0.0 Isotopic label 36 0.0 -Dimethyl@P^Protein N-term 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 +Dimethyl@Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Isotopic label 36 0.0 +Dimethyl@P^Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 Dimethyl@N 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 -Dimethyl@Any N-term 28.0313 28.0532 H(4)C(2) 0.0 Isotopic label 36 0.0 +Dimethyl@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Isotopic label 36 0.0 Dimethyl@K 28.0313 28.0532 H(4)C(2) 0.0 Multiple 36 0.0 Dimethyl@R 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 -Trimethyl@A^Protein N-term 42.04695 42.0797 H(6)C(3) 0.0 Post-translational 37 0.0 +Trimethyl@A^Protein_N-term 42.04695 42.0797 H(6)C(3) 0.0 Post-translational 37 0.0 Trimethyl@R 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 37 0.0 Trimethyl@K 42.04695 42.0797 H(6)C(3) 59.073499 H(9)C(3)N(1) Post-translational 37 0.5 Methylthio@C 45.987721 46.0916 H(2)C(1)S(1) 0.0 Multiple 39 0.0 Methylthio@N 45.987721 46.0916 H(2)C(1)S(1) 0.0 Post-translational 39 0.0 Methylthio@D 45.987721 46.0916 H(2)C(1)S(1) 0.0 Post-translational 39 0.0 Methylthio@K 45.987721 46.0916 H(2)C(1)S(1) 0.0 Artefact 39 0.0 -Methylthio@Any N-term 45.987721 46.0916 H(2)C(1)S(1) 0.0 Artefact 39 0.0 +Methylthio@Any_N-term 45.987721 46.0916 H(2)C(1)S(1) 0.0 Artefact 39 0.0 Sulfo@S 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 Sulfo@T 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 Sulfo@Y 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 @@ -142,7 +142,7 @@ Hex@C 162.052824 162.1406 H(10)C(6)O(5) 0.0 Other glycosylation 41 0.0 Hex@W 162.052824 162.1406 H(10)C(6)O(5) 0.0 Other glycosylation 41 0.0 Hex@T 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) O-linked glycosylation 41 0.5 Hex@S 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) O-linked glycosylation 41 0.5 -Hex@Any N-term 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 +Hex@Any_N-term 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 Hex@N 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) N-linked glycosylation 41 0.5 Hex@R 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 Hex@K 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 @@ -155,21 +155,21 @@ HexNAc@N 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) N-li Farnesyl@C 204.187801 204.3511 H(24)C(15) 0.0 Post-translational 44 0.0 Myristoyl@C 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 Myristoyl@K 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 -Myristoyl@G^Any N-term 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 +Myristoyl@G^Any_N-term 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 PyridoxalPhosphate@K 229.014009 229.1266 H(8)C(8)N(1)O(5)P(1) 0.0 Post-translational 46 0.0 Palmitoyl@T 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 Palmitoyl@S 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 Palmitoyl@K 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 Palmitoyl@C 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 -Palmitoyl@Protein N-term 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 +Palmitoyl@Protein_N-term 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 GeranylGeranyl@C 272.250401 272.4681 H(32)C(20) 0.0 Post-translational 48 0.0 Phosphopantetheine@S 340.085794 340.333 H(21)C(11)N(2)O(6)P(1)S(1) 0.0 Post-translational 49 0.0 FAD@Y 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 FAD@H 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 FAD@C 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 -Tripalmitate@C^Protein N-term 788.725777 789.3049 H(96)C(51)O(5) 0.0 Post-translational 51 0.0 +Tripalmitate@C^Protein_N-term 788.725777 789.3049 H(96)C(51)O(5) 0.0 Post-translational 51 0.0 Guanidinyl@K 42.021798 42.04 H(2)C(1)N(2) 0.0 Chemical derivative 52 0.0 -Guanidinyl@Any N-term 42.021798 42.04 H(2)C(1)N(2) 0.0 Chemical derivative 52 0.0 +Guanidinyl@Any_N-term 42.021798 42.04 H(2)C(1)N(2) 0.0 Chemical derivative 52 0.0 HNE@K 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 HNE@H 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 HNE@C 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 @@ -177,36 +177,36 @@ HNE@A 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 HNE@L 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 Glucuronyl@T 176.032088 176.1241 H(8)C(6)O(6) 176.032088 H(8)C(6)O(6) O-linked glycosylation 54 0.5 Glucuronyl@S 176.032088 176.1241 H(8)C(6)O(6) 176.032088 H(8)C(6)O(6) O-linked glycosylation 54 0.5 -Glucuronyl@Protein N-term 176.032088 176.1241 H(8)C(6)O(6) 0.0 Other glycosylation 54 0.0 +Glucuronyl@Protein_N-term 176.032088 176.1241 H(8)C(6)O(6) 0.0 Other glycosylation 54 0.0 Glutathione@C 305.068156 305.3076 H(15)C(10)N(3)O(6)S(1) 0.0 Post-translational 55 0.0 Acetyl:2H(3)@T 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 Acetyl:2H(3)@S 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 Acetyl:2H(3)@H 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Acetyl:2H(3)@Any N-term 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Acetyl:2H(3)@Any_N-term 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 Acetyl:2H(3)@K 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 Acetyl:2H(3)@Y 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Acetyl:2H(3)@Protein N-term 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Propionyl@Any N-term 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 +Acetyl:2H(3)@Protein_N-term 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Propionyl@Any_N-term 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 Propionyl@K 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 Propionyl@S 56.026215 56.0633 H(4)C(3)O(1) 0.0 Chemical derivative 58 0.0 Propionyl@T 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 -Propionyl@Protein N-term 56.026215 56.0633 H(4)C(3)O(1) 0.0 Multiple 58 0.0 -Propionyl:13C(3)@Any N-term 59.036279 59.0412 H(4)13C(3)O(1) 0.0 Isotopic label 59 0.0 +Propionyl@Protein_N-term 56.026215 56.0633 H(4)C(3)O(1) 0.0 Multiple 58 0.0 +Propionyl:13C(3)@Any_N-term 59.036279 59.0412 H(4)13C(3)O(1) 0.0 Isotopic label 59 0.0 Propionyl:13C(3)@K 59.036279 59.0412 H(4)13C(3)O(1) 0.0 Isotopic label 59 0.0 -GIST-Quat@Any N-term 127.099714 127.1842 H(13)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 60 0.5 +GIST-Quat@Any_N-term 127.099714 127.1842 H(13)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 60 0.5 GIST-Quat@K 127.099714 127.1842 H(13)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 60 0.5 -GIST-Quat:2H(3)@Any N-term 130.118544 130.2027 H(10)2H(3)C(7)N(1)O(1) 62.09233 H(6)2H(3)C(3)N(1) Isotopic label 61 0.5 +GIST-Quat:2H(3)@Any_N-term 130.118544 130.2027 H(10)2H(3)C(7)N(1)O(1) 62.09233 H(6)2H(3)C(3)N(1) Isotopic label 61 0.5 GIST-Quat:2H(3)@K 130.118544 130.2027 H(10)2H(3)C(7)N(1)O(1) 62.09233 H(6)2H(3)C(3)N(1) Isotopic label 61 0.5 -GIST-Quat:2H(6)@Any N-term 133.137375 133.2212 H(7)2H(6)C(7)N(1)O(1) 65.11116 H(3)2H(6)C(3)N(1) Isotopic label 62 0.5 +GIST-Quat:2H(6)@Any_N-term 133.137375 133.2212 H(7)2H(6)C(7)N(1)O(1) 65.11116 H(3)2H(6)C(3)N(1) Isotopic label 62 0.5 GIST-Quat:2H(6)@K 133.137375 133.2212 H(7)2H(6)C(7)N(1)O(1) 65.11116 H(3)2H(6)C(3)N(1) Isotopic label 62 0.5 -GIST-Quat:2H(9)@Any N-term 136.156205 136.2397 H(4)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 63 0.5 +GIST-Quat:2H(9)@Any_N-term 136.156205 136.2397 H(4)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 63 0.5 GIST-Quat:2H(9)@K 136.156205 136.2397 H(4)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 63 0.5 -Succinyl@Protein N-term 100.016044 100.0728 H(4)C(4)O(3) 0.0 Post-translational 64 0.0 -Succinyl@Any N-term 100.016044 100.0728 H(4)C(4)O(3) 0.0 Isotopic label 64 0.0 +Succinyl@Protein_N-term 100.016044 100.0728 H(4)C(4)O(3) 0.0 Post-translational 64 0.0 +Succinyl@Any_N-term 100.016044 100.0728 H(4)C(4)O(3) 0.0 Isotopic label 64 0.0 Succinyl@K 100.016044 100.0728 H(4)C(4)O(3) 0.0 Isotopic label 64 0.0 -Succinyl:2H(4)@Any N-term 104.041151 104.0974 2H(4)C(4)O(3) 0.0 Isotopic label 65 0.0 +Succinyl:2H(4)@Any_N-term 104.041151 104.0974 2H(4)C(4)O(3) 0.0 Isotopic label 65 0.0 Succinyl:2H(4)@K 104.041151 104.0974 2H(4)C(4)O(3) 0.0 Isotopic label 65 0.0 -Succinyl:13C(4)@Any N-term 104.029463 104.0434 H(4)13C(4)O(3) 0.0 Isotopic label 66 0.0 +Succinyl:13C(4)@Any_N-term 104.029463 104.0434 H(4)13C(4)O(3) 0.0 Isotopic label 66 0.0 Succinyl:13C(4)@K 104.029463 104.0434 H(4)13C(4)O(3) 0.0 Isotopic label 66 0.0 probiotinhydrazide@P 258.115047 258.3405 H(18)C(10)N(4)O(2)S(1) 0.0 Chemical derivative 357 0.0 Pro->pyro-Glu@P 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 359 0.0 @@ -215,23 +215,23 @@ His->Asp@H -22.031969 -22.0519 H(-2)C(-2)N(-2)O(2) 0.0 AA substitution 349 0.0 Trp->Hydroxykynurenin@W 19.989829 19.9881 C(-1)O(2) 0.0 Chemical derivative 350 0.0 Delta:H(4)C(3)@K 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 Delta:H(4)C(3)@H 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 -Delta:H(4)C(3)@Protein N-term 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 +Delta:H(4)C(3)@Protein_N-term 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 Delta:H(4)C(2)@K 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 Delta:H(4)C(2)@H 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 -Delta:H(4)C(2)@Any N-term 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 +Delta:H(4)C(2)@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 Cys->Dha@C -33.987721 -34.0809 H(-2)S(-1) 0.0 Chemical derivative 368 0.0 Arg->GluSA@R -43.053433 -43.0711 H(-5)C(-1)N(-3)O(1) 0.0 Chemical derivative 344 0.0 Trioxidation@Y 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 Trioxidation@W 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 Trioxidation@C 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 Trioxidation@F 47.984744 47.9982 O(3) 0.0 Artefact 345 0.0 -Iminobiotin@Any N-term 225.093583 225.3106 H(15)C(10)N(3)O(1)S(1) 0.0 Chemical derivative 89 0.0 +Iminobiotin@Any_N-term 225.093583 225.3106 H(15)C(10)N(3)O(1)S(1) 0.0 Chemical derivative 89 0.0 Iminobiotin@K 225.093583 225.3106 H(15)C(10)N(3)O(1)S(1) 0.0 Chemical derivative 89 0.0 -ESP@Any N-term 338.177647 338.4682 H(26)C(16)N(4)O(2)S(1) 0.0 Isotopic label 90 0.0 +ESP@Any_N-term 338.177647 338.4682 H(26)C(16)N(4)O(2)S(1) 0.0 Isotopic label 90 0.0 ESP@K 338.177647 338.4682 H(26)C(16)N(4)O(2)S(1) 0.0 Isotopic label 90 0.0 -ESP:2H(10)@Any N-term 348.240414 348.5299 H(16)2H(10)C(16)N(4)O(2)S(1) 0.0 Isotopic label 91 0.0 +ESP:2H(10)@Any_N-term 348.240414 348.5299 H(16)2H(10)C(16)N(4)O(2)S(1) 0.0 Isotopic label 91 0.0 ESP:2H(10)@K 348.240414 348.5299 H(16)2H(10)C(16)N(4)O(2)S(1) 0.0 Isotopic label 91 0.0 -NHS-LC-Biotin@Any N-term 339.161662 339.453 H(25)C(16)N(3)O(3)S(1) 0.0 Chemical derivative 92 0.0 +NHS-LC-Biotin@Any_N-term 339.161662 339.453 H(25)C(16)N(3)O(3)S(1) 0.0 Chemical derivative 92 0.0 NHS-LC-Biotin@K 339.161662 339.453 H(25)C(16)N(3)O(3)S(1) 0.0 Chemical derivative 92 0.0 EDT-maleimide-PEO-biotin@T 601.206246 601.8021 H(39)C(25)N(5)O(6)S(3) 0.0 Chemical derivative 93 0.0 EDT-maleimide-PEO-biotin@S 601.206246 601.8021 H(39)C(25)N(5)O(6)S(3) 0.0 Chemical derivative 93 0.0 @@ -243,14 +243,14 @@ Nitro@Y 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Chemical derivative 354 0.0 Nitro@W 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Chemical derivative 354 0.0 Nitro@F 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Artefact 354 0.0 ICAT-C@C 227.126991 227.2603 H(17)C(10)N(3)O(3) 0.0 Isotopic label 105 0.0 -Delta:H(2)C(2)@Protein N-term 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 +Delta:H(2)C(2)@Protein_N-term 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 Delta:H(2)C(2)@K 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 Delta:H(2)C(2)@H 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 -Delta:H(2)C(2)@Any N-term 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 +Delta:H(2)C(2)@Any_N-term 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 Trp->Kynurenin@W 3.994915 3.9887 C(-1)O(1) 0.0 Chemical derivative 351 0.0 Lys->Allysine@K -1.031634 -1.0311 H(-3)N(-1)O(1) 0.0 Post-translational 352 0.0 ICAT-C:13C(9)@C 236.157185 236.1942 H(17)C(1)13C(9)N(3)O(3) 0.0 Isotopic label 106 0.0 -FormylMet@Protein N-term 159.035399 159.2062 H(9)C(6)N(1)O(2)S(1) 0.0 Pre-translational 107 0.0 +FormylMet@Protein_N-term 159.035399 159.2062 H(9)C(6)N(1)O(2)S(1) 0.0 Pre-translational 107 0.0 Nethylmaleimide@C 125.047679 125.1253 H(7)C(6)N(1)O(2) 0.0 Chemical derivative 108 0.0 OxLysBiotinRed@K 354.172562 354.4676 H(26)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 112 0.0 IBTP@C 316.138088 316.3759 H(21)C(22)P(1) 0.0 Chemical derivative 119 0.0 @@ -265,21 +265,21 @@ GG@C 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 GG@T 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 GG@S 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 GG@K 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 1000000.0 -GG@Protein N-term 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Post-translational 121 0.0 -Formyl@Protein N-term 27.994915 28.0101 C(1)O(1) 0.0 Post-translational 122 0.0 +GG@Protein_N-term 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Post-translational 121 0.0 +Formyl@Protein_N-term 27.994915 28.0101 C(1)O(1) 0.0 Post-translational 122 0.0 Formyl@T 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 Formyl@K 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 -Formyl@Any N-term 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 +Formyl@Any_N-term 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 Formyl@S 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 ICAT-H@C 345.097915 345.7754 H(20)C(15)N(1)O(6)Cl(1) 0.0 Isotopic label 123 0.0 ICAT-H:13C(6)@C 351.118044 351.7313 H(20)C(9)13C(6)N(1)O(6)Cl(1) 0.0 Isotopic label 124 0.0 -Cation:K@Any C-term 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 +Cation:K@Any_C-term 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 Cation:K@E 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 Cation:K@D 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 -Xlink:DTSSP[88]@Protein N-term 87.998285 88.1283 H(4)C(3)O(1)S(1) 0.0 Chemical derivative 126 0.0 +Xlink:DTSSP[88]@Protein_N-term 87.998285 88.1283 H(4)C(3)O(1)S(1) 0.0 Chemical derivative 126 0.0 Xlink:DTSSP[88]@K 87.998285 88.1283 H(4)C(3)O(1)S(1) 0.0 Chemical derivative 126 0.0 Xlink:EGS[226]@K 226.047738 226.1828 H(10)C(10)O(6) 0.0 Chemical derivative 1897 0.0 -Xlink:EGS[226]@Protein N-term 226.047738 226.1828 H(10)C(10)O(6) 0.0 Chemical derivative 1897 0.0 +Xlink:EGS[226]@Protein_N-term 226.047738 226.1828 H(10)C(10)O(6) 0.0 Chemical derivative 1897 0.0 Fluoro@Y 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 Fluoro@W 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 Fluoro@F 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 @@ -290,16 +290,16 @@ Iodo@Y 125.896648 125.8965 H(-1)I(1) 0.0 Chemical derivative 129 0.0 Diiodo@Y 251.793296 251.7931 H(-2)I(2) 0.0 Chemical derivative 130 0.0 Diiodo@H 251.793296 251.7931 H(-2)I(2) 0.0 Chemical derivative 130 0.0 Triiodo@Y 377.689944 377.6896 H(-3)I(3) 0.0 Chemical derivative 131 0.0 -Myristoleyl@G^Protein N-term 208.182715 208.3398 H(24)C(14)O(1) 0.0 Co-translational 134 0.0 +Myristoleyl@G^Protein_N-term 208.182715 208.3398 H(24)C(14)O(1) 0.0 Co-translational 134 0.0 Pro->Pyrrolidinone@P -30.010565 -30.026 H(-2)C(-1)O(-1) 0.0 Chemical derivative 360 0.0 -Myristoyl+Delta:H(-4)@G^Protein N-term 206.167065 206.3239 H(22)C(14)O(1) 0.0 Co-translational 135 0.0 -Benzoyl@Any N-term 104.026215 104.1061 H(4)C(7)O(1) 0.0 Isotopic label 136 0.0 +Myristoyl+Delta:H(-4)@G^Protein_N-term 206.167065 206.3239 H(22)C(14)O(1) 0.0 Co-translational 135 0.0 +Benzoyl@Any_N-term 104.026215 104.1061 H(4)C(7)O(1) 0.0 Isotopic label 136 0.0 Benzoyl@K 104.026215 104.1061 H(4)C(7)O(1) 0.0 Isotopic label 136 0.0 Hex(5)HexNAc(2)@N 1216.422863 1217.088 H(76)C(46)N(2)O(35) 1216.422863 H(76)C(46)N(2)O(35) N-linked glycosylation 137 0.5 -Dansyl@Any N-term 233.051049 233.2862 H(11)C(12)N(1)O(2)S(1) 0.0 Chemical derivative 139 0.0 +Dansyl@Any_N-term 233.051049 233.2862 H(11)C(12)N(1)O(2)S(1) 0.0 Chemical derivative 139 0.0 Dansyl@K 233.051049 233.2862 H(11)C(12)N(1)O(2)S(1) 0.0 Chemical derivative 139 0.0 -a-type-ion@Any C-term -46.005479 -46.0254 H(-2)C(-1)O(-2) 0.0 Other 140 0.0 -Amidine@Any N-term 41.026549 41.0519 H(3)C(2)N(1) 0.0 Chemical derivative 141 0.0 +a-type-ion@Any_C-term -46.005479 -46.0254 H(-2)C(-1)O(-2) 0.0 Other 140 0.0 +Amidine@Any_N-term 41.026549 41.0519 H(3)C(2)N(1) 0.0 Chemical derivative 141 0.0 Amidine@K 41.026549 41.0519 H(3)C(2)N(1) 0.0 Chemical derivative 141 0.0 HexNAc(1)dHex(1)@T 349.137281 349.3337 H(23)C(14)N(1)O(9) 349.137281 H(23)C(14)N(1)O(9) O-linked glycosylation 142 0.5 HexNAc(1)dHex(1)@S 349.137281 349.3337 H(23)C(14)N(1)O(9) 349.137281 H(23)C(14)N(1)O(9) O-linked glycosylation 142 0.5 @@ -350,9 +350,9 @@ Delta:S(-1)Se(1)@C 47.944449 46.895 S(-1)Se(1) 0.0 Non-standard residue 162 0.0 NBS:13C(6)@W 159.008578 159.1144 H(3)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 171 0.0 Methyl:2H(3)13C(1)@K 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 Methyl:2H(3)13C(1)@R 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 -Methyl:2H(3)13C(1)@Any N-term 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 -Dimethyl:2H(6)13C(2)@Protein N-term 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 -Dimethyl:2H(6)13C(2)@Any N-term 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 +Methyl:2H(3)13C(1)@Any_N-term 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 +Dimethyl:2H(6)13C(2)@Protein_N-term 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 +Dimethyl:2H(6)13C(2)@Any_N-term 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 Dimethyl:2H(6)13C(2)@R 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 Dimethyl:2H(6)13C(2)@K 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 NBS@W 152.988449 153.1585 H(3)C(6)N(1)O(2)S(1) 0.0 Chemical derivative 172 0.0 @@ -375,11 +375,11 @@ Label:13C(6)@R 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 HPG@R 132.021129 132.1162 H(4)C(8)O(2) 0.0 Chemical derivative 186 0.0 2HPG@R 282.052824 282.2476 H(10)C(16)O(5) 0.0 Chemical derivative 187 0.0 QAT:2H(3)@C 174.168569 174.2784 H(16)2H(3)C(9)N(2)O(1) 0.0 Isotopic label 196 0.0 -Label:18O(2)@Any C-term 4.008491 3.9995 O(-2)18O(2) 0.0 Isotopic label 193 0.0 -AccQTag@Any N-term 170.048013 170.1674 H(6)C(10)N(2)O(1) 0.0 Chemical derivative 194 0.0 +Label:18O(2)@Any_C-term 4.008491 3.9995 O(-2)18O(2) 0.0 Isotopic label 193 0.0 +AccQTag@Any_N-term 170.048013 170.1674 H(6)C(10)N(2)O(1) 0.0 Chemical derivative 194 0.0 AccQTag@K 170.048013 170.1674 H(6)C(10)N(2)O(1) 0.0 Chemical derivative 194 0.0 -Dimethyl:2H(4)@Protein N-term 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 -Dimethyl:2H(4)@Any N-term 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 +Dimethyl:2H(4)@Protein_N-term 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 +Dimethyl:2H(4)@Any_N-term 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 Dimethyl:2H(4)@K 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 Dimethyl:2H(4)@R 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 EQAT@C 184.157563 184.2786 H(20)C(10)N(2)O(1) 0.0 Chemical derivative 197 0.0 @@ -408,11 +408,11 @@ NEIAA@Y 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Isotopic label 211 0.0 NEIAA@C 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Isotopic label 211 0.0 iTRAQ4plex@C 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 iTRAQ4plex@T 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@Protein N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@Protein_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 iTRAQ4plex@S 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 iTRAQ4plex@H 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 iTRAQ4plex@Y 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@Any N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@Any_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 iTRAQ4plex@K 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 Crotonaldehyde@K 70.041865 70.0898 H(6)C(4)O(1) 0.0 Other 253 0.0 Crotonaldehyde@H 70.041865 70.0898 H(6)C(4)O(1) 0.0 Other 253 0.0 @@ -426,26 +426,26 @@ Argbiotinhydrazide@R 199.066699 199.27 H(13)C(9)N(1)O(2)S(1) 0.0 Chemical deriv Label:18O(1)@Y 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 Label:18O(1)@T 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 Label:18O(1)@S 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 -Label:18O(1)@Any C-term 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 +Label:18O(1)@Any_C-term 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 Label:13C(6)15N(2)@K 8.014199 7.9427 C(-6)13C(6)N(-2)15N(2) 0.0 Isotopic label 259 0.0 Thiophospho@Y 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 Thiophospho@T 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 Thiophospho@S 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 SPITC@K 214.971084 215.2495 H(5)C(7)N(1)O(3)S(2) 0.0 Chemical derivative 261 0.0 -SPITC@Any N-term 214.971084 215.2495 H(5)C(7)N(1)O(3)S(2) 0.0 Chemical derivative 261 0.0 +SPITC@Any_N-term 214.971084 215.2495 H(5)C(7)N(1)O(3)S(2) 0.0 Chemical derivative 261 0.0 IGBP@C 296.016039 297.1478 H(13)C(12)N(2)O(2)Br(1) 0.0 Isotopic label 243 0.0 Cytopiloyne@Y 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 Cytopiloyne@S 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 Cytopiloyne@R 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 Cytopiloyne@P 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 -Cytopiloyne@Any N-term 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 +Cytopiloyne@Any_N-term 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 Cytopiloyne@K 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 Cytopiloyne@C 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 Cytopiloyne+water@Y 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 Cytopiloyne+water@T 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 Cytopiloyne+water@S 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 Cytopiloyne+water@R 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 -Cytopiloyne+water@Any N-term 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 +Cytopiloyne+water@Any_N-term 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 Cytopiloyne+water@K 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 Cytopiloyne+water@C 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 Label:13C(6)15N(4)@R 10.008269 9.9296 C(-6)13C(6)N(-4)15N(4) 0.0 Isotopic label 267 0.0 @@ -458,8 +458,8 @@ Label:13C(5)15N(1)@V 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label Label:13C(5)15N(1)@E 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 PET@T 121.035005 121.2028 H(7)C(7)N(1)O(-1)S(1) 0.0 Chemical derivative 264 0.0 PET@S 121.035005 121.2028 H(7)C(7)N(1)O(-1)S(1) 0.0 Chemical derivative 264 0.0 -CAF@Any N-term 135.983029 136.1265 H(4)C(3)O(4)S(1) 0.0 Chemical derivative 272 0.0 -Xlink:BS2G[96]@Protein N-term 96.021129 96.0841 H(4)C(5)O(2) 0.0 Chemical derivative 1905 0.0 +CAF@Any_N-term 135.983029 136.1265 H(4)C(3)O(4)S(1) 0.0 Chemical derivative 272 0.0 +Xlink:BS2G[96]@Protein_N-term 96.021129 96.0841 H(4)C(5)O(2) 0.0 Chemical derivative 1905 0.0 Xlink:BS2G[96]@K 96.021129 96.0841 H(4)C(5)O(2) 0.0 Chemical derivative 1905 0.0 Nitrosyl@C 28.990164 28.9982 H(-1)N(1)O(1) 0.0 Post-translational 275 0.0 Nitrosyl@Y 28.990164 28.9982 H(-1)N(1)O(1) 0.0 Chemical derivative 275 0.0 @@ -467,7 +467,7 @@ Kdo@T 220.058303 220.1767 H(12)C(8)O(7) 220.058303 H(12)C(8)O(7) O-linked glycos Kdo@S 220.058303 220.1767 H(12)C(8)O(7) 220.058303 H(12)C(8)O(7) O-linked glycosylation 2022 0.5 AEBS@Y 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 AEBS@S 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 -AEBS@Protein N-term 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 +AEBS@Protein_N-term 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 AEBS@K 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 AEBS@H 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 Ethanolyl@K 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 @@ -475,10 +475,10 @@ Ethanolyl@C 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 Ethanolyl@R 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 Label:13C(6)15N(2)+Dimethyl@K 36.045499 35.9959 H(4)C(-4)13C(6)N(-2)15N(2) 0.0 Isotopic label 987 0.0 HMVK@C 86.036779 86.0892 H(6)C(4)O(2) 0.0 Chemical derivative 371 0.0 -Ethyl@Any C-term 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 -Ethyl@Protein N-term 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 +Ethyl@Any_C-term 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 +Ethyl@Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 Ethyl@E 28.0313 28.0532 H(4)C(2) 0.0 Artefact 280 0.0 -Ethyl@Any N-term 28.0313 28.0532 H(4)C(2) 0.0 Multiple 280 0.0 +Ethyl@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Multiple 280 0.0 Ethyl@K 28.0313 28.0532 H(4)C(2) 0.0 Multiple 280 0.0 Ethyl@D 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 CoenzymeA@C 765.09956 765.5182 H(34)C(21)N(7)O(16)P(3)S(1) 0.0 Post-translational 281 0.0 @@ -486,15 +486,15 @@ Methyl+Deamidated@Q 14.999666 15.0113 H(1)C(1)N(-1)O(1) 0.0 Post-translational Methyl+Deamidated@N 14.999666 15.0113 H(1)C(1)N(-1)O(1) 0.0 Chemical derivative 528 0.0 Delta:H(5)C(2)@P 29.039125 29.0611 H(5)C(2) 0.0 Post-translational 529 0.0 Methyl:2H(2)@K 16.028204 16.0389 2H(2)C(1) 0.0 Isotopic label 284 0.0 -Methyl:2H(2)@Any N-term 16.028204 16.0389 2H(2)C(1) 0.0 Isotopic label 284 0.0 +Methyl:2H(2)@Any_N-term 16.028204 16.0389 2H(2)C(1) 0.0 Isotopic label 284 0.0 SulfanilicAcid@E 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 SulfanilicAcid@D 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 -SulfanilicAcid@Any C-term 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 +SulfanilicAcid@Any_C-term 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 SulfanilicAcid:13C(6)@E 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 SulfanilicAcid:13C(6)@D 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 -SulfanilicAcid:13C(6)@Any C-term 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 +SulfanilicAcid:13C(6)@Any_C-term 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 Biotin-PEO-Amine@D 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 -Biotin-PEO-Amine@Protein C-term 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 +Biotin-PEO-Amine@Protein_C-term 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 Biotin-PEO-Amine@E 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 Trp->Oxolactone@W 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 288 0.0 Biotin-HPDP@C 428.191582 428.6124 H(32)C(19)N(4)O(3)S(2) 0.0 Chemical derivative 290 0.0 @@ -502,7 +502,7 @@ Delta:Hg(1)@C 201.970617 200.59 Hg(1) 0.0 Chemical derivative 291 0.0 IodoU-AMP@Y 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 IodoU-AMP@W 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 IodoU-AMP@F 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 -CAMthiopropanoyl@Protein N-term 145.019749 145.1796 H(7)C(5)N(1)O(2)S(1) 0.0 Chemical derivative 293 0.0 +CAMthiopropanoyl@Protein_N-term 145.019749 145.1796 H(7)C(5)N(1)O(2)S(1) 0.0 Chemical derivative 293 0.0 CAMthiopropanoyl@K 145.019749 145.1796 H(7)C(5)N(1)O(2)S(1) 0.0 Chemical derivative 293 0.0 IED-Biotin@C 326.141261 326.4145 H(22)C(14)N(4)O(3)S(1) 0.0 Chemical derivative 294 0.0 dHex@N 146.057909 146.1412 H(10)C(6)O(4) 146.057909 H(10)C(6)O(4) N-linked glycosylation 295 0.5 @@ -517,7 +517,7 @@ Carboxy@E 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 Carboxy@D 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 Carboxy@K 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 Carboxy@W 43.989829 44.0095 C(1)O(2) 0.0 Chemical derivative 299 0.0 -Carboxy@M^Protein N-term 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 +Carboxy@M^Protein_N-term 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 Bromobimane@C 190.074228 190.1986 H(10)C(10)N(2)O(2) 0.0 Chemical derivative 301 0.0 Menadione@K 170.036779 170.1641 H(6)C(11)O(2) 0.0 Chemical derivative 302 0.0 Menadione@C 170.036779 170.1641 H(6)C(11)O(2) 0.0 Chemical derivative 302 0.0 @@ -539,7 +539,7 @@ Hex(5)HexNAc(4)@S 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C Hex(5)HexNAc(4)@N 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C(62)N(4)O(45) N-linked glycosylation 311 0.5 Hex(5)HexNAc(4)@T 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C(62)N(4)O(45) O-linked glycosylation 311 0.5 Cysteinyl@C 119.004099 119.1423 H(5)C(3)N(1)O(2)S(1) 0.0 Multiple 312 0.0 -Lys-loss@K^Protein C-term -128.094963 -128.1723 H(-12)C(-6)N(-2)O(-1) 0.0 Post-translational 313 0.0 +Lys-loss@K^Protein_C-term -128.094963 -128.1723 H(-12)C(-6)N(-2)O(-1) 0.0 Post-translational 313 0.0 Nmethylmaleimide@K 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 314 0.0 Nmethylmaleimide@C 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 314 0.0 CyDye-Cy3@C 672.298156 672.8335 H(44)C(37)N(4)O(6)S(1) 0.0 Chemical derivative 494 0.0 @@ -551,7 +551,7 @@ Nethylmaleimide+water@K 143.058243 143.1406 H(9)C(6)N(1)O(3) 0.0 Chemical deriv Nethylmaleimide+water@C 143.058243 143.1406 H(9)C(6)N(1)O(3) 0.0 Chemical derivative 320 0.0 Methyl+Acetyl:2H(3)@K 59.045045 59.0817 H(1)2H(3)C(3)O(1) 0.0 Isotopic label 768 0.0 Xlink:B10621@C 713.093079 713.5626 H(30)C(31)N(4)O(6)S(1)I(1) 0.0 Chemical derivative 323 0.0 -Xlink:DTBP[87]@Protein N-term 87.01427 87.1435 H(5)C(3)N(1)S(1) 0.0 Chemical derivative 324 0.0 +Xlink:DTBP[87]@Protein_N-term 87.01427 87.1435 H(5)C(3)N(1)S(1) 0.0 Chemical derivative 324 0.0 Xlink:DTBP[87]@K 87.01427 87.1435 H(5)C(3)N(1)S(1) 0.0 Chemical derivative 324 0.0 FP-Biotin@K 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 FP-Biotin@T 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 @@ -573,21 +573,21 @@ Diisopropylphosphate@K 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical deriv Diisopropylphosphate@Y 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 Diisopropylphosphate@T 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 Diisopropylphosphate@S 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 -Diisopropylphosphate@Any N-term 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 +Diisopropylphosphate@Any_N-term 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 Isopropylphospho@Y 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 Isopropylphospho@T 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 Isopropylphospho@S 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 -ICPL:13C(6)@Any N-term 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 -ICPL:13C(6)@Protein N-term 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 +ICPL:13C(6)@Any_N-term 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 +ICPL:13C(6)@Protein_N-term 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 ICPL:13C(6)@K 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 CarbamidomethylDTT@C 209.018035 209.2864 H(11)C(6)N(1)O(3)S(2) 0.0 Artefact 893 0.0 -ICPL@Protein N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 +ICPL@Protein_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 ICPL@K 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 -ICPL@Any N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 +ICPL@Any_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 Deamidated:18O(1)@Q 2.988261 2.9845 H(-1)N(-1)18O(1) 0.0 Isotopic label 366 0.0 Deamidated:18O(1)@N 2.988261 2.9845 H(-1)N(-1)18O(1) 0.0 Isotopic label 366 0.0 Arg->Orn@R -42.021798 -42.04 H(-2)C(-1)N(-2) 0.0 Artefact 372 0.0 -Cation:Cu[I]@Any C-term 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 +Cation:Cu[I]@Any_C-term 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 Cation:Cu[I]@E 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 Cation:Cu[I]@D 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 Cation:Cu[I]@H 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 @@ -600,10 +600,10 @@ Carboxyethyl@H 72.021129 72.0627 H(4)C(3)O(2) 0.0 Chemical derivative 378 0.0 Hypusine@K 87.068414 87.1204 H(9)C(4)N(1)O(1) 0.0 Post-translational 379 0.0 Retinylidene@K 266.203451 266.4204 H(26)C(20) 0.0 Post-translational 380 0.0 Lys->AminoadipicAcid@K 14.96328 14.9683 H(-3)N(-1)O(2) 0.0 Post-translational 381 0.0 -Cys->PyruvicAcid@C^Protein N-term -33.003705 -33.0961 H(-3)N(-1)O(1)S(-1) 0.0 Post-translational 382 0.0 -Ammonia-loss@C^Any N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Artefact 385 0.0 -Ammonia-loss@S^Protein N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Post-translational 385 0.0 -Ammonia-loss@T^Protein N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Post-translational 385 0.0 +Cys->PyruvicAcid@C^Protein_N-term -33.003705 -33.0961 H(-3)N(-1)O(1)S(-1) 0.0 Post-translational 382 0.0 +Ammonia-loss@C^Any_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Artefact 385 0.0 +Ammonia-loss@S^Protein_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Post-translational 385 0.0 +Ammonia-loss@T^Protein_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Post-translational 385 0.0 Ammonia-loss@N -17.026549 -17.0305 H(-3)N(-1) 0.0 Chemical derivative 385 0.0 Phycocyanobilin@C 586.279135 586.678 H(38)C(33)N(4)O(6) 0.0 Post-translational 387 0.0 Phycoerythrobilin@C 588.294785 588.6939 H(40)C(33)N(4)O(6) 0.0 Post-translational 388 0.0 @@ -614,7 +614,7 @@ Molybdopterin@C 521.884073 520.2668 H(11)C(10)N(5)O(8)P(1)S(2)Mo(1) 0.0 Post-tr Quinone@W 29.974179 29.9829 H(-2)O(2) 0.0 Post-translational 392 0.0 Quinone@Y 29.974179 29.9829 H(-2)O(2) 0.0 Post-translational 392 0.0 Glucosylgalactosyl@K 340.100562 340.2806 H(20)C(12)O(11) 340.100562 H(20)C(12)O(11) Other glycosylation 393 0.5 -GPIanchor@Protein C-term 123.00853 123.0477 H(6)C(2)N(1)O(3)P(1) 0.0 Post-translational 394 0.0 +GPIanchor@Protein_C-term 123.00853 123.0477 H(6)C(2)N(1)O(3)P(1) 0.0 Post-translational 394 0.0 PhosphoribosyldephosphoCoA@S 881.146904 881.6335 H(42)C(26)N(7)O(19)P(3)S(1) 0.0 Post-translational 395 0.0 GlycerylPE@E 197.04531 197.1262 H(12)C(5)N(1)O(5)P(1) 0.0 Post-translational 396 0.0 Triiodothyronine@Y 469.716159 469.785 H(1)C(6)O(1)I(3) 0.0 Post-translational 397 0.0 @@ -623,25 +623,25 @@ Tyr->Dha@Y -94.041865 -94.1112 H(-6)C(-6)O(-1) 0.0 Post-translational 400 0.0 Didehydro@S -2.01565 -2.0159 H(-2) 0.0 Post-translational 401 0.0 Didehydro@Y -2.01565 -2.0159 H(-2) 0.0 Post-translational 401 0.0 Didehydro@T -2.01565 -2.0159 H(-2) 0.0 Chemical derivative 401 0.0 -Didehydro@K^Any C-term -2.01565 -2.0159 H(-2) 0.0 Artefact 401 0.0 +Didehydro@K^Any_C-term -2.01565 -2.0159 H(-2) 0.0 Artefact 401 0.0 Cys->Oxoalanine@C -17.992806 -18.0815 H(-2)O(1)S(-1) 0.0 Post-translational 402 0.0 -Ser->LacticAcid@S^Protein N-term -15.010899 -15.0146 H(-1)N(-1) 0.0 Post-translational 403 0.0 +Ser->LacticAcid@S^Protein_N-term -15.010899 -15.0146 H(-1)N(-1) 0.0 Post-translational 403 0.0 GluGlu@E 258.085186 258.228 H(14)C(10)N(2)O(6) 0.0 Post-translational 451 0.0 -GluGlu@Protein C-term 258.085186 258.228 H(14)C(10)N(2)O(6) 0.0 Post-translational 451 0.0 +GluGlu@Protein_C-term 258.085186 258.228 H(14)C(10)N(2)O(6) 0.0 Post-translational 451 0.0 Phosphoadenosine@H 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 Phosphoadenosine@T 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 Phosphoadenosine@K 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 Phosphoadenosine@Y 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 Phosphoadenosine@S 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 Glu@E 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Post-translational 450 0.0 -Glu@Protein C-term 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Chemical derivative 450 0.0 +Glu@Protein_C-term 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Chemical derivative 450 0.0 Hydroxycinnamyl@C 146.036779 146.1427 H(6)C(9)O(2) 0.0 Post-translational 407 0.0 Glycosyl@P 148.037173 148.114 H(8)C(5)O(5) 0.0 Other glycosylation 408 0.0 FMNH@H 454.088965 454.3279 H(19)C(17)N(4)O(9)P(1) 0.0 Post-translational 409 0.0 FMNH@C 454.088965 454.3279 H(19)C(17)N(4)O(9)P(1) 0.0 Post-translational 409 0.0 Archaeol@C 634.662782 635.1417 H(86)C(43)O(2) 0.0 Post-translational 410 0.0 -Phenylisocyanate@Any N-term 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 411 0.0 -Phenylisocyanate:2H(5)@Any N-term 124.068498 124.1515 2H(5)C(7)N(1)O(1) 0.0 Chemical derivative 412 0.0 +Phenylisocyanate@Any_N-term 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 411 0.0 +Phenylisocyanate:2H(5)@Any_N-term 124.068498 124.1515 2H(5)C(7)N(1)O(1) 0.0 Chemical derivative 412 0.0 Phosphoguanosine@H 345.047435 345.2053 H(12)C(10)N(5)O(7)P(1) 0.0 Post-translational 413 0.0 Phosphoguanosine@K 345.047435 345.2053 H(12)C(10)N(5)O(7)P(1) 0.0 Post-translational 413 0.0 Hydroxymethyl@N 30.010565 30.026 H(2)C(1)O(1) 0.0 Post-translational 414 0.0 @@ -650,13 +650,13 @@ Dipyrrolylmethanemethyl@C 418.137616 418.3973 H(22)C(20)N(2)O(8) 0.0 Post-trans PhosphoUridine@H 306.025302 306.166 H(11)C(9)N(2)O(8)P(1) 0.0 Post-translational 417 0.0 PhosphoUridine@Y 306.025302 306.166 H(11)C(9)N(2)O(8)P(1) 0.0 Post-translational 417 0.0 Glycerophospho@S 154.00311 154.0584 H(7)C(3)O(5)P(1) 0.0 Post-translational 419 0.0 -Carboxy->Thiocarboxy@G^Protein C-term 15.977156 16.0656 O(-1)S(1) 0.0 Post-translational 420 0.0 +Carboxy->Thiocarboxy@G^Protein_C-term 15.977156 16.0656 O(-1)S(1) 0.0 Post-translational 420 0.0 Sulfide@D 31.972071 32.065 S(1) 0.0 Post-translational 421 0.0 Sulfide@C 31.972071 32.065 S(1) 0.0 Post-translational 421 0.0 Sulfide@W 31.972071 32.065 S(1) 0.0 Chemical derivative 421 0.0 PyruvicAcidIminyl@K 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 -PyruvicAcidIminyl@V^Protein N-term 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 -PyruvicAcidIminyl@C^Protein N-term 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 +PyruvicAcidIminyl@V^Protein_N-term 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 +PyruvicAcidIminyl@C^Protein_N-term 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 Delta:Se(1)@C 79.91652 78.96 Se(1) 0.0 Post-translational 423 0.0 MolybdopterinGD@D 1572.985775 1572.0146 H(47)C(40)N(20)O(26)P(4)S(4)Mo(1) 0.0 Post-translational 424 0.0 MolybdopterinGD@C 1572.985775 1572.0146 H(47)C(40)N(20)O(26)P(4)S(4)Mo(1) 0.0 Post-translational 424 0.0 @@ -684,12 +684,12 @@ PhosphoHex@S 242.019154 242.1205 H(11)C(6)O(8)P(1) 242.019154 H(11)C(6)O(8)P(1) Palmitoleyl@C 236.214016 236.3929 H(28)C(16)O(1) 0.0 Post-translational 431 0.0 Palmitoleyl@S 236.214016 236.3929 H(28)C(16)O(1) 0.0 Post-translational 431 0.0 Palmitoleyl@T 236.214016 236.3929 H(28)C(16)O(1) 0.0 Pre-translational 431 0.0 -Cholesterol@Protein C-term 368.344302 368.6383 H(44)C(27) 0.0 Post-translational 432 0.0 +Cholesterol@Protein_C-term 368.344302 368.6383 H(44)C(27) 0.0 Post-translational 432 0.0 Didehydroretinylidene@K 264.187801 264.4046 H(24)C(20) 0.0 Post-translational 433 0.0 CHDH@D 294.183109 294.3859 H(26)C(17)O(4) 0.0 Post-translational 434 0.0 Methylpyrroline@K 109.052764 109.1259 H(7)C(6)N(1)O(1) 0.0 Post-translational 435 0.0 Hydroxyheme@E 614.161645 614.4714 H(30)C(34)N(4)O(4)Fe(1) 0.0 Post-translational 436 0.0 -MicrocinC7@Protein C-term 386.110369 386.3003 H(19)C(13)N(6)O(6)P(1) 0.0 Post-translational 437 0.0 +MicrocinC7@Protein_C-term 386.110369 386.3003 H(19)C(13)N(6)O(6)P(1) 0.0 Post-translational 437 0.0 Cyano@C 24.995249 25.0095 H(-1)C(1)N(1) 0.0 Post-translational 438 0.0 Diironsubcluster@C 342.786916 342.876 H(-1)C(5)N(2)O(5)S(2)Fe(2) 0.0 Post-translational 439 0.0 Amidino@C 42.021798 42.04 H(2)C(1)N(2) 0.0 Post-translational 440 0.0 @@ -701,27 +701,27 @@ Hydroxytrimethyl@K 59.04969 59.0871 H(7)C(3)O(1) 0.0 Post-translational 445 0.0 Deoxy@T -15.994915 -15.9994 O(-1) 0.0 Chemical derivative 447 0.0 Deoxy@D -15.994915 -15.9994 O(-1) 0.0 Post-translational 447 0.0 Deoxy@S -15.994915 -15.9994 O(-1) 0.0 Chemical derivative 447 0.0 -Microcin@Protein C-term 831.197041 831.6871 H(37)C(36)N(3)O(20) 0.0 Post-translational 448 0.0 +Microcin@Protein_C-term 831.197041 831.6871 H(37)C(36)N(3)O(20) 0.0 Post-translational 448 0.0 Decanoyl@T 154.135765 154.2493 H(18)C(10)O(1) 0.0 Post-translational 449 0.0 Decanoyl@S 154.135765 154.2493 H(18)C(10)O(1) 0.0 Post-translational 449 0.0 -GluGluGlu@Protein C-term 387.127779 387.3419 H(21)C(15)N(3)O(9) 0.0 Post-translational 452 0.0 +GluGluGlu@Protein_C-term 387.127779 387.3419 H(21)C(15)N(3)O(9) 0.0 Post-translational 452 0.0 GluGluGlu@E 387.127779 387.3419 H(21)C(15)N(3)O(9) 0.0 Post-translational 452 0.0 -GluGluGluGlu@Protein C-term 516.170373 516.4559 H(28)C(20)N(4)O(12) 0.0 Post-translational 453 0.0 +GluGluGluGlu@Protein_C-term 516.170373 516.4559 H(28)C(20)N(4)O(12) 0.0 Post-translational 453 0.0 GluGluGluGlu@E 516.170373 516.4559 H(28)C(20)N(4)O(12) 0.0 Post-translational 453 0.0 HexN@W 161.068808 161.1558 H(11)C(6)N(1)O(4) 0.0 Other glycosylation 454 0.0 HexN@T 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) O-linked glycosylation 454 0.5 HexN@S 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) O-linked glycosylation 454 0.5 HexN@N 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) N-linked glycosylation 454 0.5 HexN@K 161.068808 161.1558 H(11)C(6)N(1)O(4) 0.0 Synth. pep. protect. gp. 454 0.0 -Xlink:DMP[154]@Protein N-term 154.110613 154.2096 H(14)C(8)N(2)O(1) 0.0 Chemical derivative 455 0.0 +Xlink:DMP[154]@Protein_N-term 154.110613 154.2096 H(14)C(8)N(2)O(1) 0.0 Chemical derivative 455 0.0 Xlink:DMP[154]@K 154.110613 154.2096 H(14)C(8)N(2)O(1) 0.0 Chemical derivative 455 0.0 -NDA@Any N-term 175.042199 175.1855 H(5)C(13)N(1) 0.0 Chemical derivative 457 0.0 +NDA@Any_N-term 175.042199 175.1855 H(5)C(13)N(1) 0.0 Chemical derivative 457 0.0 NDA@K 175.042199 175.1855 H(5)C(13)N(1) 0.0 Chemical derivative 457 0.0 -SPITC:13C(6)@Any N-term 220.991213 221.2054 H(5)C(1)13C(6)N(1)O(3)S(2) 0.0 Chemical derivative 464 0.0 +SPITC:13C(6)@Any_N-term 220.991213 221.2054 H(5)C(1)13C(6)N(1)O(3)S(2) 0.0 Chemical derivative 464 0.0 SPITC:13C(6)@K 220.991213 221.2054 H(5)C(1)13C(6)N(1)O(3)S(2) 0.0 Chemical derivative 464 0.0 -TMAB:2H(9)@Any N-term 137.16403 137.2476 H(5)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 477 0.5 +TMAB:2H(9)@Any_N-term 137.16403 137.2476 H(5)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 477 0.5 TMAB:2H(9)@K 137.16403 137.2476 H(5)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 477 0.5 -TMAB@Any N-term 128.107539 128.1922 H(14)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 476 0.5 +TMAB@Any_N-term 128.107539 128.1922 H(14)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 476 0.5 TMAB@K 128.107539 128.1922 H(14)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 476 0.5 FTC@S 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 FTC@R 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 @@ -749,21 +749,21 @@ BHTOH@C 234.16198 234.334 H(22)C(15)O(2) 0.0 Other 498 0.0 BHTOH@K 234.16198 234.334 H(22)C(15)O(2) 0.0 Other 498 0.0 IGBP:13C(2)@C 298.022748 299.1331 H(13)C(10)13C(2)N(2)O(2)Br(1) 0.0 Isotopic label 499 0.0 Nmethylmaleimide+water@C 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Chemical derivative 500 0.0 -PyMIC@Any N-term 134.048013 134.1353 H(6)C(7)N(2)O(1) 0.0 Chemical derivative 501 0.0 -LG-lactam-K@Protein N-term 332.19876 332.4339 H(28)C(20)O(4) 0.0 Post-translational 503 0.0 +PyMIC@Any_N-term 134.048013 134.1353 H(6)C(7)N(2)O(1) 0.0 Chemical derivative 501 0.0 +LG-lactam-K@Protein_N-term 332.19876 332.4339 H(28)C(20)O(4) 0.0 Post-translational 503 0.0 LG-lactam-K@K 332.19876 332.4339 H(28)C(20)O(4) 0.0 Post-translational 503 0.0 BisANS@K 594.091928 594.6569 H(22)C(32)N(2)O(6)S(2) 0.0 Chemical derivative 519 0.0 -Piperidine@Any N-term 68.0626 68.117 H(8)C(5) 0.0 Chemical derivative 520 0.0 +Piperidine@Any_N-term 68.0626 68.117 H(8)C(5) 0.0 Chemical derivative 520 0.0 Piperidine@K 68.0626 68.117 H(8)C(5) 0.0 Chemical derivative 520 0.0 -Diethyl@Any N-term 56.0626 56.1063 H(8)C(4) 0.0 Chemical derivative 518 0.0 +Diethyl@Any_N-term 56.0626 56.1063 H(8)C(4) 0.0 Chemical derivative 518 0.0 Diethyl@K 56.0626 56.1063 H(8)C(4) 0.0 Chemical derivative 518 0.0 -LG-Hlactam-K@Protein N-term 348.193674 348.4333 H(28)C(20)O(5) 0.0 Post-translational 504 0.0 +LG-Hlactam-K@Protein_N-term 348.193674 348.4333 H(28)C(20)O(5) 0.0 Post-translational 504 0.0 LG-Hlactam-K@K 348.193674 348.4333 H(28)C(20)O(5) 0.0 Post-translational 504 0.0 -Dimethyl:2H(4)13C(2)@Protein N-term 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 +Dimethyl:2H(4)13C(2)@Protein_N-term 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 Dimethyl:2H(4)13C(2)@R 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 Dimethyl:2H(4)13C(2)@K 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 -Dimethyl:2H(4)13C(2)@Any N-term 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 -C8-QAT@Any N-term 227.224915 227.3862 H(29)C(14)N(1)O(1) 0.0 Chemical derivative 513 0.0 +Dimethyl:2H(4)13C(2)@Any_N-term 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 +C8-QAT@Any_N-term 227.224915 227.3862 H(29)C(14)N(1)O(1) 0.0 Chemical derivative 513 0.0 C8-QAT@K 227.224915 227.3862 H(29)C(14)N(1)O(1) 0.0 Chemical derivative 513 0.0 Hex(2)@R 324.105647 324.2812 H(20)C(12)O(10) 0.0 Other glycosylation 512 0.0 Hex(2)@K 324.105647 324.2812 H(20)C(12)O(10) 0.0 Other glycosylation 512 0.0 @@ -773,30 +773,30 @@ LG-lactam-R@R 290.176961 290.3939 H(26)C(19)N(-2)O(4) 0.0 Post-translational 50 Withaferin@C 470.266839 470.5977 H(38)C(28)O(6) 0.0 Chemical derivative 1036 0.0 Biotin:Thermo-88317@S 443.291294 443.5603 H(42)C(22)N(3)O(4)P(1) 0.0 Chemical derivative 1037 0.0 Biotin:Thermo-88317@Y 443.291294 443.5603 H(42)C(22)N(3)O(4)P(1) 0.0 Chemical derivative 1037 0.0 -CLIP_TRAQ_2@Any N-term 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 +CLIP_TRAQ_2@Any_N-term 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 CLIP_TRAQ_2@K 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 CLIP_TRAQ_2@Y 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 LG-Hlactam-R@R 306.171876 306.3933 H(26)C(19)N(-2)O(5) 0.0 Post-translational 506 0.0 Maleimide-PEO2-Biotin@C 525.225719 525.6183 H(35)C(23)N(5)O(7)S(1) 0.0 Chemical derivative 522 0.0 -Sulfo-NHS-LC-LC-Biotin@Any N-term 452.245726 452.6106 H(36)C(22)N(4)O(4)S(1) 0.0 Chemical derivative 523 0.0 +Sulfo-NHS-LC-LC-Biotin@Any_N-term 452.245726 452.6106 H(36)C(22)N(4)O(4)S(1) 0.0 Chemical derivative 523 0.0 Sulfo-NHS-LC-LC-Biotin@K 452.245726 452.6106 H(36)C(22)N(4)O(4)S(1) 0.0 Chemical derivative 523 0.0 FNEM@C 427.069202 427.3625 H(13)C(24)N(1)O(7) 0.0 Chemical derivative 515 0.0 PropylNAGthiazoline@C 232.064354 232.2768 H(14)C(9)N(1)O(4)S(1) 0.0 Chemical derivative 514 0.0 Dethiomethyl@M -48.003371 -48.1075 H(-4)C(-1)S(-1) 0.0 Artefact 526 0.0 iTRAQ4plex114@Y 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 -iTRAQ4plex114@Any N-term 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 +iTRAQ4plex114@Any_N-term 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 iTRAQ4plex114@K 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 iTRAQ4plex114@C 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 iTRAQ4plex115@Y 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 -iTRAQ4plex115@Any N-term 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 +iTRAQ4plex115@Any_N-term 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 iTRAQ4plex115@K 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 iTRAQ4plex115@C 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 Dibromo@Y 155.821022 157.7921 H(-2)Br(2) 0.0 Chemical derivative 534 0.0 LRGG@K 383.228103 383.446 H(29)C(16)N(7)O(4) 0.0 Chemical derivative 535 0.0 CLIP_TRAQ_3@Y 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 -CLIP_TRAQ_3@Any N-term 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 +CLIP_TRAQ_3@Any_N-term 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 CLIP_TRAQ_3@K 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 -CLIP_TRAQ_4@Any N-term 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 +CLIP_TRAQ_4@Any_N-term 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 CLIP_TRAQ_4@K 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 CLIP_TRAQ_4@Y 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 Biotin:Cayman-10141@C 626.386577 626.8927 H(54)C(35)N(4)O(4)S(1) 0.0 Other 538 0.0 @@ -840,12 +840,12 @@ Gly->Val@G 42.04695 42.0797 H(6)C(3)N(0)O(0)S(0) 0.0 AA substitution 575 0.0 Gly->Asp@G 58.005479 58.0361 H(2)C(2)N(0)O(2)S(0) 0.0 AA substitution 576 0.0 Gly->Cys@G 45.987721 46.0916 H(2)C(1)N(0)O(0)S(1) 0.0 AA substitution 577 0.0 Gly->Arg@G 99.079647 99.1344 H(9)C(4)N(3)O(0)S(0) 0.0 AA substitution 578 0.0 -dNIC@Any N-term 109.048119 109.1205 H(1)2H(3)C(6)N(1)O(1) 0.0 Isotopic label 698 0.0 +dNIC@Any_N-term 109.048119 109.1205 H(1)2H(3)C(6)N(1)O(1) 0.0 Isotopic label 698 0.0 dNIC@K 109.048119 109.1205 H(1)2H(3)C(6)N(1)O(1) 0.0 Isotopic label 698 0.0 His->Pro@H -40.006148 -40.0241 H(0)C(-1)N(-2)O(0)S(0) 0.0 AA substitution 580 0.0 His->Tyr@H 26.004417 26.034 H(2)C(3)N(-2)O(1)S(0) 0.0 AA substitution 581 0.0 His->Gln@H -9.000334 -9.0101 H(1)C(-1)N(-1)O(1)S(0) 0.0 AA substitution 582 0.0 -NIC@Any N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 697 0.0 +NIC@Any_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 697 0.0 NIC@K 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 697 0.0 His->Arg@H 19.042199 19.0464 H(5)C(0)N(1)O(0)S(0) 0.0 AA substitution 584 0.0 His->Xle@H -23.974848 -23.9816 H(4)N(-2) 0.0 AA substitution 585 0.0 @@ -957,23 +957,23 @@ Tyr->Asp@Y -48.036386 -48.0859 H(-4)C(-5)N(0)O(1)S(0) 0.0 AA substitution 682 0 Tyr->Cys@Y -60.054144 -60.0304 H(-4)C(-6)N(0)O(-1)S(1) 0.0 AA substitution 683 0.0 BDMAPP@W 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 BDMAPP@Y 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 -BDMAPP@Protein N-term 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Chemical derivative 684 0.0 +BDMAPP@Protein_N-term 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Chemical derivative 684 0.0 BDMAPP@K 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Chemical derivative 684 0.0 BDMAPP@H 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 NA-LNO2@C 325.225309 325.443 H(31)C(18)N(1)O(4) 0.0 Post-translational 685 0.0 NA-LNO2@H 325.225309 325.443 H(31)C(18)N(1)O(4) 0.0 Post-translational 685 0.0 NA-OA-NO2@C 327.240959 327.4589 H(33)C(18)N(1)O(4) 0.0 Post-translational 686 0.0 NA-OA-NO2@H 327.240959 327.4589 H(33)C(18)N(1)O(4) 0.0 Post-translational 686 0.0 -ICPL:2H(4)@Any N-term 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 -ICPL:2H(4)@Protein N-term 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 +ICPL:2H(4)@Any_N-term 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 +ICPL:2H(4)@Protein_N-term 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 ICPL:2H(4)@K 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 CarboxymethylDTT@C 210.00205 210.2712 H(10)C(6)O(4)S(2) 0.0 Artefact 894 0.0 -iTRAQ8plex@Protein N-term 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@Protein_N-term 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 iTRAQ8plex@T 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 iTRAQ8plex@S 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 iTRAQ8plex@H 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 iTRAQ8plex@Y 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -iTRAQ8plex@Any N-term 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@Any_N-term 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 iTRAQ8plex@K 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 iTRAQ8plex@C 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 Label:13C(6)15N(1)@I 7.017164 6.9493 C(-6)13C(6)N(-1)15N(1) 0.0 Isotopic label 695 0.0 @@ -991,14 +991,14 @@ O-Dimethylphosphate@S 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivat O-Methylphosphate@Y 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 O-Methylphosphate@T 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 O-Methylphosphate@S 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 -Diethylphosphate@Any N-term 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 +Diethylphosphate@Any_N-term 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 Diethylphosphate@H 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 Diethylphosphate@C 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 Diethylphosphate@K 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 Diethylphosphate@Y 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 Diethylphosphate@T 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 Diethylphosphate@S 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 -Ethylphosphate@Any N-term 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 +Ethylphosphate@Any_N-term 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 Ethylphosphate@K 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 Ethylphosphate@Y 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 Ethylphosphate@T 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 @@ -1015,30 +1015,30 @@ O-Isopropylmethylphosphonate@Y 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemica O-Isopropylmethylphosphonate@T 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemical derivative 729 0.0 O-Isopropylmethylphosphonate@S 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemical derivative 729 0.0 iTRAQ8plex:13C(6)15N(2)@Y 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 -iTRAQ8plex:13C(6)15N(2)@Any N-term 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 +iTRAQ8plex:13C(6)15N(2)@Any_N-term 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 iTRAQ8plex:13C(6)15N(2)@K 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 iTRAQ8plex:13C(6)15N(2)@C 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 BEMAD_ST@T 136.001656 136.2357 H(8)C(4)O(1)S(2) 0.0 Chemical derivative 735 0.0 BEMAD_ST@S 136.001656 136.2357 H(8)C(4)O(1)S(2) 0.0 Chemical derivative 735 0.0 Ethanolamine@D 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 -Ethanolamine@Any C-term 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 +Ethanolamine@Any_C-term 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 Ethanolamine@E 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 Ethanolamine@C 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 TMT6plex@T 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 TMT6plex@S 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 TMT6plex@H 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 -TMT6plex@Protein N-term 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 -TMT6plex@Any N-term 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 +TMT6plex@Protein_N-term 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 +TMT6plex@Any_N-term 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 TMT6plex@K 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 BEMAD_C@C 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 736 0.0 TMT2plex@H 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 TMT2plex@S 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 TMT2plex@T 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT2plex@Protein N-term 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT2plex@Any N-term 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 +TMT2plex@Protein_N-term 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 +TMT2plex@Any_N-term 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 TMT2plex@K 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT@Protein N-term 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 -TMT@Any N-term 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 +TMT@Protein_N-term 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 +TMT@Any_N-term 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 TMT@K 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 TMT@H 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Isotopic label 739 0.0 TMT@S 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Isotopic label 739 0.0 @@ -1054,9 +1054,9 @@ NO_SMX_SIMD@C 267.031377 267.2612 H(9)C(10)N(3)O(4)S(1) 0.0 Chemical derivative Malonyl@C 86.000394 86.0462 H(2)C(3)O(3) 0.0 Chemical derivative 747 0.0 Malonyl@S 86.000394 86.0462 H(2)C(3)O(3) 0.0 Chemical derivative 747 0.0 Malonyl@K 86.000394 86.0462 H(2)C(3)O(3) 0.0 Post-translational 747 0.0 -3sulfo@Any N-term 183.983029 184.1693 H(4)C(7)O(4)S(1) 0.0 Chemical derivative 748 0.0 +3sulfo@Any_N-term 183.983029 184.1693 H(4)C(7)O(4)S(1) 0.0 Chemical derivative 748 0.0 trifluoro@L 53.971735 53.9714 H(-3)F(3) 0.0 Non-standard residue 750 0.0 -TNBS@Any N-term 210.986535 211.0886 H(1)C(6)N(3)O(6) 0.0 Chemical derivative 751 0.0 +TNBS@Any_N-term 210.986535 211.0886 H(1)C(6)N(3)O(6) 0.0 Chemical derivative 751 0.0 TNBS@K 210.986535 211.0886 H(1)C(6)N(3)O(6) 0.0 Chemical derivative 751 0.0 Biotin-phenacyl@C 626.263502 626.727 H(38)C(29)N(8)O(6)S(1) 0.0 Chemical derivative 774 0.0 Biotin-phenacyl@H 626.263502 626.727 H(38)C(29)N(8)O(6)S(1) 0.0 Chemical derivative 774 0.0 @@ -1069,14 +1069,14 @@ maleimide@C 97.016378 97.0721 H(3)C(4)N(1)O(2) 0.0 Chemical derivative 773 0.0 IDEnT@C 214.990469 216.064 H(7)C(9)N(1)O(1)Cl(2) 0.0 Isotopic label 762 0.0 BEMAD_ST:2H(6)@T 142.039317 142.2727 H(2)2H(6)C(4)O(1)S(2) 0.0 Isotopic label 763 0.0 BEMAD_ST:2H(6)@S 142.039317 142.2727 H(2)2H(6)C(4)O(1)S(2) 0.0 Isotopic label 763 0.0 -Met-loss@M^Protein N-term -131.040485 -131.1961 H(-9)C(-5)N(-1)O(-1)S(-1) 0.0 Co-translational 765 0.0 -Met-loss+Acetyl@M^Protein N-term -89.02992 -89.1594 H(-7)C(-3)N(-1)S(-1) 0.0 Co-translational 766 0.0 +Met-loss@M^Protein_N-term -131.040485 -131.1961 H(-9)C(-5)N(-1)O(-1)S(-1) 0.0 Co-translational 765 0.0 +Met-loss+Acetyl@M^Protein_N-term -89.02992 -89.1594 H(-7)C(-3)N(-1)S(-1) 0.0 Co-translational 766 0.0 Menadione-HQ@K 172.05243 172.18 H(8)C(11)O(2) 0.0 Chemical derivative 767 0.0 Menadione-HQ@C 172.05243 172.18 H(8)C(11)O(2) 0.0 Chemical derivative 767 0.0 Carboxymethyl:13C(2)@C 60.012189 60.0214 H(2)13C(2)O(2) 0.0 Chemical derivative 775 0.0 NEM:2H(5)@C 130.079062 130.1561 H(2)2H(5)C(6)N(1)O(2) 0.0 Chemical derivative 776 0.0 -Gly-loss+Amide@G^Any C-term -58.005479 -58.0361 H(-2)C(-2)O(-2) 0.0 Post-translational 822 0.0 -TMPP-Ac@Any N-term 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Chemical derivative 827 0.0 +Gly-loss+Amide@G^Any_C-term -58.005479 -58.0361 H(-2)C(-2)O(-2) 0.0 Post-translational 822 0.0 +TMPP-Ac@Any_N-term 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Chemical derivative 827 0.0 TMPP-Ac@K 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Artefact 827 0.0 TMPP-Ac@Y 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Artefact 827 0.0 Label:13C(6)+GG@K 120.063056 120.0586 H(6)C(-2)13C(6)N(2)O(2) 0.0 Isotopic label 799 0.0 @@ -1105,7 +1105,7 @@ cGMP@C 343.031785 343.1895 H(10)C(10)N(5)O(7)P(1) 0.0 Post-translational 849 0. cGMP+RMP-loss@C 150.041585 150.1182 H(4)C(5)N(5)O(1) 0.0 Post-translational 851 0.0 cGMP+RMP-loss@S 150.041585 150.1182 H(4)C(5)N(5)O(1) 0.0 Post-translational 851 0.0 mTRAQ@Y 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 -mTRAQ@Any N-term 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 +mTRAQ@Any_N-term 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 mTRAQ@K 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 mTRAQ@H 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 mTRAQ@S 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 @@ -1115,19 +1115,19 @@ Label:2H(4)+GG@K 118.068034 118.1273 H(2)2H(4)C(4)N(2)O(2) 0.0 Post-translation spermine@Q 185.189198 185.3097 H(23)C(10)N(3) 0.0 Chemical derivative 1420 0.0 Label:13C(1)2H(3)@M 4.022185 4.0111 H(-3)2H(3)C(-1)13C(1) 0.0 Isotopic label 862 0.0 ZGB@K 758.380841 758.7261 H(53)C(37)N(6)O(6)F(2)S(1)B(1) 0.0 Other 861 0.0 -ZGB@Any N-term 758.380841 758.7261 H(53)C(37)N(6)O(6)F(2)S(1)B(1) 0.0 Other 861 0.0 +ZGB@Any_N-term 758.380841 758.7261 H(53)C(37)N(6)O(6)F(2)S(1)B(1) 0.0 Other 861 0.0 MG-H1@R 54.010565 54.0474 H(2)C(3)O(1) 0.0 Other 859 0.0 G-H1@R 39.994915 40.0208 C(2)O(1) 0.0 Other 860 0.0 Label:13C(6)15N(2)+GG@K 122.057126 122.0454 H(6)C(-2)13C(6)15N(2)O(2) 0.0 Isotopic label 864 0.0 -ICPL:13C(6)2H(4)@Any N-term 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 +ICPL:13C(6)2H(4)@Any_N-term 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 ICPL:13C(6)2H(4)@K 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 -ICPL:13C(6)2H(4)@Protein N-term 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 +ICPL:13C(6)2H(4)@Protein_N-term 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 DyLight-maleimide@C 940.1999 941.0762 H(48)C(39)N(4)O(15)S(4) 0.0 Chemical derivative 890 0.0 mTRAQ:13C(3)15N(1)@S 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 mTRAQ:13C(3)15N(1)@T 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 mTRAQ:13C(3)15N(1)@H 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 mTRAQ:13C(3)15N(1)@Y 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 -mTRAQ:13C(3)15N(1)@Any N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 +mTRAQ:13C(3)15N(1)@Any_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 mTRAQ:13C(3)15N(1)@K 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 Methyl-PEO12-Maleimide@C 710.383719 710.8073 H(58)C(32)N(2)O(15) 0.0 Chemical derivative 891 0.0 MDCC@C 383.148121 383.3978 H(21)C(20)N(3)O(5) 0.0 Chemical derivative 887 0.0 @@ -1149,7 +1149,7 @@ Lys->CamCys@K 31.935685 32.0219 H(-4)C(-1)O(1)S(1) 0.0 Pre-translational 903 0. Phe->CamCys@F 12.962234 13.0204 H(-1)C(-4)N(1)O(1)S(1) 0.0 Pre-translational 904 0.0 Leu->MetOx@L 33.951335 34.0378 H(-2)C(-1)O(1)S(1) 0.0 Pre-translational 905 0.0 Lys->MetOx@K 18.940436 19.0232 H(-3)C(-1)N(-1)O(1)S(1) 0.0 Pre-translational 906 0.0 -Galactosyl@Any N-term 178.047738 178.14 H(10)C(6)O(6) 0.0 Other glycosylation 907 0.0 +Galactosyl@Any_N-term 178.047738 178.14 H(10)C(6)O(6) 0.0 Other glycosylation 907 0.0 Galactosyl@K 178.047738 178.14 H(10)C(6)O(6) 0.0 Other glycosylation 907 0.0 Xlink:SMCC[321]@C 321.205242 321.4146 H(27)C(17)N(3)O(3) 0.0 Chemical derivative 908 0.0 Bacillosamine@N 228.111007 228.245 H(16)C(10)N(2)O(4) 228.111007 H(16)C(10)N(2)O(4) N-linked glycosylation 910 0.5 @@ -1184,39 +1184,39 @@ DNPS@W 198.981352 199.164 H(3)C(6)N(2)O(4)S(1) 0.0 Chemical derivative 941 0.0 SulfoGMBS@C 458.162391 458.5306 H(26)C(22)N(4)O(5)S(1) 0.0 Other 942 0.0 DimethylamineGMBS@C 267.158292 267.3241 H(21)C(13)N(3)O(3) 0.0 Chemical derivative 943 0.0 Label:15N(2)2H(9)@K 11.050561 11.0423 H(-9)2H(9)N(-2)15N(2) 0.0 Isotopic label 944 0.0 -LG-anhydrolactam@Any N-term 314.188195 314.4186 H(26)C(20)O(3) 0.0 Post-translational 946 0.0 +LG-anhydrolactam@Any_N-term 314.188195 314.4186 H(26)C(20)O(3) 0.0 Post-translational 946 0.0 LG-anhydrolactam@K 314.188195 314.4186 H(26)C(20)O(3) 0.0 Post-translational 946 0.0 LG-pyrrole@C 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 -LG-pyrrole@Any N-term 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 +LG-pyrrole@Any_N-term 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 LG-pyrrole@K 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 -LG-anhyropyrrole@Any N-term 298.19328 298.4192 H(26)C(20)O(2) 0.0 Post-translational 948 0.0 +LG-anhyropyrrole@Any_N-term 298.19328 298.4192 H(26)C(20)O(2) 0.0 Post-translational 948 0.0 LG-anhyropyrrole@K 298.19328 298.4192 H(26)C(20)O(2) 0.0 Post-translational 948 0.0 3-deoxyglucosone@R 144.042259 144.1253 H(8)C(6)O(4) 0.0 Multiple 949 0.0 Cation:Li@D 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 Cation:Li@E 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 -Cation:Li@Any C-term 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 -Cation:Ca[II]@Any C-term 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 +Cation:Li@Any_C-term 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 +Cation:Ca[II]@Any_C-term 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 Cation:Ca[II]@E 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 Cation:Ca[II]@D 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 Cation:Fe[II]@D 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 Cation:Fe[II]@E 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 -Cation:Fe[II]@Any C-term 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 +Cation:Fe[II]@Any_C-term 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 Cation:Ni[II]@D 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 Cation:Ni[II]@E 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 -Cation:Ni[II]@Any C-term 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 -Cation:Zn[II]@Any C-term 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 +Cation:Ni[II]@Any_C-term 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 +Cation:Zn[II]@Any_C-term 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 Cation:Zn[II]@E 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 Cation:Zn[II]@D 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 Cation:Zn[II]@H 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 Cation:Ag@D 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 Cation:Ag@E 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 -Cation:Ag@Any C-term 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 +Cation:Ag@Any_C-term 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 Cation:Mg[II]@D 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 Cation:Mg[II]@E 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 -Cation:Mg[II]@Any C-term 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 +Cation:Mg[II]@Any_C-term 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 2-succinyl@C 116.010959 116.0722 H(4)C(4)O(4) 0.0 Chemical derivative 957 0.0 Propargylamine@D 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 -Propargylamine@Any C-term 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 +Propargylamine@Any_C-term 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 Propargylamine@E 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 Phosphopropargyl@T 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0.0 Phosphopropargyl@Y 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0.0 @@ -1224,11 +1224,11 @@ Phosphopropargyl@S 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0. SUMO2135@K 2135.920496 2137.2343 H(137)C(90)N(21)O(37)S(1) 0.0 Other 960 0.0 SUMO3549@K 3549.536568 3551.6672 H(224)C(150)N(38)O(60)S(1) 0.0 Other 961 0.0 serotonylation@Q 159.068414 159.1846 H(9)C(10)N(1)O(1) 0.0 Post-translational 1992 0.0 -BITC@Any N-term 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 +BITC@Any_N-term 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 BITC@K 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 BITC@C 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 Carbofuran@S 58.029289 58.0593 H(4)C(2)N(1)O(1) 0.0 Chemical derivative 977 0.0 -PEITC@Any N-term 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 +PEITC@Any_N-term 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 PEITC@K 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 PEITC@C 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 thioacylPA@K 159.035399 159.2062 H(9)C(6)N(1)O(2)S(1) 0.0 Chemical derivative 967 0.0 @@ -1236,15 +1236,15 @@ maleimide3@K 969.366232 969.8975 H(59)C(37)N(7)O(23) 0.0 Post-translational 971 maleimide3@C 969.366232 969.8975 H(59)C(37)N(7)O(23) 0.0 Post-translational 971 0.0 maleimide5@K 1293.471879 1294.1787 H(79)C(49)N(7)O(33) 0.0 Post-translational 972 0.0 maleimide5@C 1293.471879 1294.1787 H(79)C(49)N(7)O(33) 0.0 Post-translational 972 0.0 -Puromycin@Any C-term 453.212452 453.4943 H(27)C(22)N(7)O(4) 0.0 Co-translational 973 0.0 +Puromycin@Any_C-term 453.212452 453.4943 H(27)C(22)N(7)O(4) 0.0 Co-translational 973 0.0 glucosone@R 160.037173 160.1247 H(8)C(6)O(5) 0.0 Other 981 0.0 Label:13C(6)+Dimethyl@K 34.051429 34.0091 H(4)C(-4)13C(6) 0.0 Isotopic label 986 0.0 cysTMT@C 299.166748 299.4322 H(25)C(14)N(3)O(2)S(1) 0.0 Chemical derivative 984 0.0 cysTMT6plex@C 304.177202 304.3962 H(25)C(10)13C(4)N(2)15N(1)O(2)S(1) 0.0 Isotopic label 985 0.0 -ISD_z+2_ion@Any N-term -15.010899 -15.0146 H(-1)N(-1) 0.0 Artefact 991 0.0 +ISD_z+2_ion@Any_N-term -15.010899 -15.0146 H(-1)N(-1) 0.0 Artefact 991 0.0 Ammonium@E 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 Ammonium@D 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 -Ammonium@Any C-term 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 +Ammonium@Any_C-term 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 Biotin:Sigma-B1267@C 449.17329 449.5239 H(27)C(20)N(5)O(5)S(1) 0.0 Chemical derivative 993 0.0 Label:15N(1)@M 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 Label:15N(1)@E 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 @@ -1280,34 +1280,34 @@ Thiazolidine@Y 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 Thiazolidine@H 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 Thiazolidine@R 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 Thiazolidine@K 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -Thiazolidine@Protein N-term 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +Thiazolidine@Protein_N-term 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 Thiazolidine@C 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 Thiazolidine@F 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 DEDGFLYMVYASQETFG@K 1970.824411 1972.088 H(122)C(89)N(18)O(31)S(1) 18.010565 H(2)O(1) Post-translational 1010 0.5 Biotin:Invitrogen-M1602@C 523.210069 523.6024 H(33)C(23)N(5)O(7)S(1) 0.0 Chemical derivative 1012 0.0 Xlink:DSS[156]@K 156.078644 156.1791 H(12)C(8)O(3) 0.0 Chemical derivative 1020 0.0 -Xlink:DSS[156]@Protein N-term 156.078644 156.1791 H(12)C(8)O(3) 0.0 Chemical derivative 1020 0.0 +Xlink:DSS[156]@Protein_N-term 156.078644 156.1791 H(12)C(8)O(3) 0.0 Chemical derivative 1020 0.0 DMPO@H 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 DMPO@Y 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 DMPO@C 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 glycidamide@K 87.032028 87.0773 H(5)C(3)N(1)O(2) 0.0 Chemical derivative 1014 0.0 -glycidamide@Any N-term 87.032028 87.0773 H(5)C(3)N(1)O(2) 0.0 Chemical derivative 1014 0.0 -Ahx2+Hsl@Any C-term 309.205242 309.4039 H(27)C(16)N(3)O(3) 0.0 Non-standard residue 1015 0.0 +glycidamide@Any_N-term 87.032028 87.0773 H(5)C(3)N(1)O(2) 0.0 Chemical derivative 1014 0.0 +Ahx2+Hsl@Any_C-term 309.205242 309.4039 H(27)C(16)N(3)O(3) 0.0 Non-standard residue 1015 0.0 ICDID@C 138.06808 138.1638 H(10)C(8)O(2) 0.0 Isotopic label 1018 0.0 ICDID:2H(6)@C 144.10574 144.2008 H(4)2H(6)C(8)O(2) 0.0 Isotopic label 1019 0.0 -Xlink:EGS[244]@Protein N-term 244.058303 244.1981 H(12)C(10)O(7) 0.0 Chemical derivative 1021 0.0 +Xlink:EGS[244]@Protein_N-term 244.058303 244.1981 H(12)C(10)O(7) 0.0 Chemical derivative 1021 0.0 Xlink:EGS[244]@K 244.058303 244.1981 H(12)C(10)O(7) 0.0 Chemical derivative 1021 0.0 -Xlink:DST[132]@Protein N-term 132.005873 132.0716 H(4)C(4)O(5) 0.0 Chemical derivative 1022 0.0 +Xlink:DST[132]@Protein_N-term 132.005873 132.0716 H(4)C(4)O(5) 0.0 Chemical derivative 1022 0.0 Xlink:DST[132]@K 132.005873 132.0716 H(4)C(4)O(5) 0.0 Chemical derivative 1022 0.0 -Xlink:DTSSP[192]@Protein N-term 191.991486 192.2559 H(8)C(6)O(3)S(2) 0.0 Chemical derivative 1023 0.0 +Xlink:DTSSP[192]@Protein_N-term 191.991486 192.2559 H(8)C(6)O(3)S(2) 0.0 Chemical derivative 1023 0.0 Xlink:DTSSP[192]@K 191.991486 192.2559 H(8)C(6)O(3)S(2) 0.0 Chemical derivative 1023 0.0 Xlink:SMCC[237]@C 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 Xlink:SMCC[237]@K 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 -Xlink:SMCC[237]@Protein N-term 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 +Xlink:SMCC[237]@Protein_N-term 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 2-nitrobenzyl@Y 135.032028 135.1201 H(5)C(7)N(1)O(2) 0.0 Chemical derivative 1032 0.0 -Xlink:DMP[140]@Protein N-term 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Chemical derivative 1027 0.0 +Xlink:DMP[140]@Protein_N-term 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Chemical derivative 1027 0.0 Xlink:DMP[140]@K 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Chemical derivative 1027 0.0 -Xlink:EGS[115]@Protein N-term 115.026943 115.0874 H(5)C(4)N(1)O(3) 0.0 Chemical derivative 1028 0.0 +Xlink:EGS[115]@Protein_N-term 115.026943 115.0874 H(5)C(4)N(1)O(3) 0.0 Chemical derivative 1028 0.0 Xlink:EGS[115]@K 115.026943 115.0874 H(5)C(4)N(1)O(3) 0.0 Chemical derivative 1028 0.0 Cys->SecNEM@C 172.992127 172.0203 H(7)C(6)N(1)O(2)S(-1)Se(1) 0.0 Non-standard residue 1033 0.0 Cys->SecNEM:2H(5)@C 178.023511 177.0511 H(2)2H(5)C(6)N(1)O(2)S(-1)Se(1) 0.0 Chemical derivative 1034 0.0 @@ -1571,17 +1571,17 @@ BMP-piperidinol@C 263.131014 263.3337 H(17)C(18)N(1)O(1) 0.0 Chemical derivativ BMP-piperidinol@M 263.131014 263.3337 H(17)C(18)N(1)O(1) 0.0 Chemical derivative 1281 0.0 UgiJoullieProGlyProGly@D 308.148455 308.333 H(20)C(14)N(4)O(4) 0.0 Chemical derivative 1283 0.0 UgiJoullieProGlyProGly@E 308.148455 308.333 H(20)C(14)N(4)O(4) 0.0 Chemical derivative 1283 0.0 -Arg-loss@R^Any C-term -156.101111 -156.1857 H(-12)C(-6)N(-4)O(-1) 0.0 Other 1287 0.0 -Arg@Any N-term 156.101111 156.1857 H(12)C(6)N(4)O(1) 0.0 Other 1288 0.0 +Arg-loss@R^Any_C-term -156.101111 -156.1857 H(-12)C(-6)N(-4)O(-1) 0.0 Other 1287 0.0 +Arg@Any_N-term 156.101111 156.1857 H(12)C(6)N(4)O(1) 0.0 Other 1288 0.0 IMEHex(2)NeuAc(1)@K 688.199683 688.6527 H(40)C(25)N(2)O(18)S(1) 0.0 Other glycosylation 1286 0.0 Butyryl@K 70.041865 70.0898 H(6)C(4)O(1) 0.0 Post-translational 1289 0.0 Dicarbamidomethyl@K 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 Dicarbamidomethyl@H 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 Dicarbamidomethyl@C 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 Dicarbamidomethyl@R 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 -Dicarbamidomethyl@Any N-term 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 +Dicarbamidomethyl@Any_N-term 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 Dimethyl:2H(6)@K 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 -Dimethyl:2H(6)@Any N-term 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 +Dimethyl:2H(6)@Any_N-term 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 Dimethyl:2H(6)@R 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 GGQ@K 242.101505 242.2319 H(14)C(9)N(4)O(4) 0.0 Other 1292 0.0 QTGG@K 343.149184 343.3357 H(21)C(13)N(5)O(6) 0.0 Other 1293 0.0 @@ -1591,9 +1591,9 @@ Label:13C(3)@A 3.010064 2.978 C(-3)13C(3) 0.0 Isotopic label 1296 0.0 Label:13C(4)15N(1)@D 5.010454 4.964 C(-4)13C(4)N(-1)15N(1) 0.0 Isotopic label 1298 0.0 Label:2H(10)@L 10.062767 10.0616 H(-10)2H(10) 0.0 Isotopic label 1299 0.0 Label:2H(4)13C(1)@R 5.028462 5.0173 H(-4)2H(4)C(-1)13C(1) 0.0 Isotopic label 1300 0.0 -Lys@Any N-term 128.094963 128.1723 H(12)C(6)N(2)O(1) 0.0 Other 1301 0.0 +Lys@Any_N-term 128.094963 128.1723 H(12)C(6)N(2)O(1) 0.0 Other 1301 0.0 mTRAQ:13C(6)15N(2)@K 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 -mTRAQ:13C(6)15N(2)@Any N-term 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 +mTRAQ:13C(6)15N(2)@Any_N-term 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 mTRAQ:13C(6)15N(2)@Y 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 mTRAQ:13C(6)15N(2)@H 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 mTRAQ:13C(6)15N(2)@S 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 @@ -1606,11 +1606,11 @@ NeuGc@S 307.090331 307.254 H(17)C(11)N(1)O(9) 307.090331 H(17)C(11)N(1)O(9) O-li NeuGc@N 307.090331 307.254 H(17)C(11)N(1)O(9) 307.090331 H(17)C(11)N(1)O(9) N-linked glycosylation 1304 0.5 Propyl@D 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 Propyl@K 42.04695 42.0797 H(6)C(3) 0.0 Isotopic label 1305 0.0 -Propyl@Any N-term 42.04695 42.0797 H(6)C(3) 0.0 Isotopic label 1305 0.0 +Propyl@Any_N-term 42.04695 42.0797 H(6)C(3) 0.0 Isotopic label 1305 0.0 Propyl@E 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 -Propyl@Any C-term 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 -Propyl@Protein C-term 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 -Propyl:2H(6)@Any N-term 48.084611 48.1167 2H(6)C(3) 0.0 Isotopic label 1306 0.0 +Propyl@Any_C-term 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 +Propyl@Protein_C-term 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 +Propyl:2H(6)@Any_N-term 48.084611 48.1167 2H(6)C(3) 0.0 Isotopic label 1306 0.0 Propyl:2H(6)@K 48.084611 48.1167 2H(6)C(3) 0.0 Isotopic label 1306 0.0 Propiophenone@C 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 Propiophenone@W 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 @@ -1623,36 +1623,36 @@ PS_Hapten@H 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 PS_Hapten@C 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 PS_Hapten@K 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 Cy3-maleimide@C 753.262796 753.9046 H(45)C(37)N(4)O(9)S(2) 0.0 Chemical derivative 1348 0.0 -Delta:H(6)C(3)O(1)@Protein N-term 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 +Delta:H(6)C(3)O(1)@Protein_N-term 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 Delta:H(6)C(3)O(1)@K 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 Delta:H(6)C(3)O(1)@H 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 Delta:H(6)C(3)O(1)@C 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 -Delta:H(8)C(6)O(1)@Protein N-term 96.057515 96.1271 H(8)C(6)O(1) 0.0 Chemical derivative 1313 0.0 +Delta:H(8)C(6)O(1)@Protein_N-term 96.057515 96.1271 H(8)C(6)O(1) 0.0 Chemical derivative 1313 0.0 Delta:H(8)C(6)O(1)@K 96.057515 96.1271 H(8)C(6)O(1) 0.0 Chemical derivative 1313 0.0 biotinAcrolein298@H 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 biotinAcrolein298@K 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 -biotinAcrolein298@Protein N-term 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 +biotinAcrolein298@Protein_N-term 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 biotinAcrolein298@C 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 MM-diphenylpentanone@C 265.146664 265.3496 H(19)C(18)N(1)O(1) 0.0 Chemical derivative 1315 0.0 EHD-diphenylpentanone@M 266.13068 266.3343 H(18)C(18)O(2) 0.0 Chemical derivative 1317 0.0 EHD-diphenylpentanone@C 266.13068 266.3343 H(18)C(18)O(2) 0.0 Chemical derivative 1317 0.0 benzylguanidine@K 132.068748 132.1625 H(8)C(8)N(2) 0.0 Chemical derivative 1349 0.0 -CarboxymethylDMAP@Any N-term 162.079313 162.1885 H(10)C(9)N(2)O(1) 0.0 Chemical derivative 1350 0.0 +CarboxymethylDMAP@Any_N-term 162.079313 162.1885 H(10)C(9)N(2)O(1) 0.0 Chemical derivative 1350 0.0 Biotin:Thermo-21901+2H2O@C 561.246849 561.6489 H(39)C(23)N(5)O(9)S(1) 0.0 Chemical derivative 1320 0.0 DiLeu4plex115@K 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 -DiLeu4plex115@Any N-term 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 +DiLeu4plex115@Any_N-term 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 DiLeu4plex115@Y 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 -DiLeu4plex@Any N-term 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 +DiLeu4plex@Any_N-term 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 DiLeu4plex@K 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 DiLeu4plex@Y 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 DiLeu4plex117@K 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 -DiLeu4plex117@Any N-term 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 +DiLeu4plex117@Any_N-term 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 DiLeu4plex117@Y 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 DiLeu4plex118@K 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 -DiLeu4plex118@Any N-term 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 +DiLeu4plex118@Any_N-term 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 DiLeu4plex118@Y 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 Xlink:BuUrBu[213]@K 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 -Xlink:BuUrBu[213]@Protein N-term 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 +Xlink:BuUrBu[213]@Protein_N-term 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 bisANS-sulfonates@S 437.201774 437.5543 H(25)C(32)N(2) 0.0 Chemical derivative 1330 0.0 bisANS-sulfonates@T 437.201774 437.5543 H(25)C(32)N(2) 0.0 Chemical derivative 1330 0.0 bisANS-sulfonates@K 437.201774 437.5543 H(25)C(32)N(2) 0.0 Chemical derivative 1330 0.0 @@ -1691,7 +1691,7 @@ iodoTMT6plex@E 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical iodoTMT6plex@D 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 iodoTMT6plex@C 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 Label:13C(2)15N(2)@K 4.00078 3.9721 C(-2)13C(2)N(-2)15N(2) 0.0 Isotopic label 1787 0.0 -Phosphogluconoylation@Any N-term 258.014069 258.1199 H(11)C(6)O(9)P(1) 0.0 Post-translational 1344 0.0 +Phosphogluconoylation@Any_N-term 258.014069 258.1199 H(11)C(6)O(9)P(1) 0.0 Post-translational 1344 0.0 Phosphogluconoylation@K 258.014069 258.1199 H(11)C(6)O(9)P(1) 0.0 Post-translational 1344 0.0 Methyl:2H(3)+Acetyl:2H(3)@K 62.063875 62.1002 H(-2)2H(6)C(3)O(1) 0.0 Isotopic label 1368 0.0 dHex(1)Hex(1)@T 308.110732 308.2818 H(20)C(12)O(9) 308.110732 H(20)C(12)O(9) O-linked glycosylation 1367 0.5 @@ -1703,7 +1703,7 @@ Label:2H(3)+Oxidation@M 19.013745 19.0179 H(-3)2H(3)O(1) 0.0 Isotopic label 137 Trimethyl:2H(9)@R 51.103441 51.1352 H(-3)2H(9)C(3) 0.0 Isotopic label 1371 0.0 Trimethyl:2H(9)@K 51.103441 51.1352 H(-3)2H(9)C(3) 0.0 Isotopic label 1371 0.0 Acetyl:13C(2)@K 44.017274 44.022 H(2)13C(2)O(1) 0.0 Isotopic label 1372 0.0 -Acetyl:13C(2)@Protein N-term 44.017274 44.022 H(2)13C(2)O(1) 0.0 Isotopic label 1372 0.0 +Acetyl:13C(2)@Protein_N-term 44.017274 44.022 H(2)13C(2)O(1) 0.0 Isotopic label 1372 0.0 dHex(1)Hex(2)@T 470.163556 470.4224 H(30)C(18)O(14) 470.163556 H(30)C(18)O(14) O-linked glycosylation 1375 0.5 dHex(1)Hex(2)@S 470.163556 470.4224 H(30)C(18)O(14) 470.163556 H(30)C(18)O(14) O-linked glycosylation 1375 0.5 dHex(1)Hex(3)@T 632.216379 632.563 H(40)C(24)O(19) 632.216379 H(40)C(24)O(19) O-linked glycosylation 1376 0.5 @@ -1725,31 +1725,31 @@ Hydroxamic_acid@D 15.010899 15.0146 H(1)N(1) 0.0 Artefact 1385 0.0 Oxidation+NEM@C 141.042593 141.1247 H(7)C(6)N(1)O(3) 0.0 Chemical derivative 1390 0.0 NHS-fluorescein@K 471.131802 471.4581 H(21)C(27)N(1)O(7) 0.0 Chemical derivative 1391 0.0 DiART6plex@Y 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 -DiART6plex@Protein N-term 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 -DiART6plex@Any N-term 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 +DiART6plex@Protein_N-term 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 +DiART6plex@Any_N-term 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 DiART6plex@K 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 DiART6plex115@K 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 -DiART6plex115@Any N-term 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 -DiART6plex115@Protein N-term 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 +DiART6plex115@Any_N-term 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 +DiART6plex115@Protein_N-term 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 DiART6plex115@Y 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 DiART6plex116/119@Y 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 -DiART6plex116/119@Protein N-term 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 +DiART6plex116/119@Protein_N-term 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 DiART6plex116/119@K 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 -DiART6plex116/119@Any N-term 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 +DiART6plex116/119@Any_N-term 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 DiART6plex117@K 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 -DiART6plex117@Any N-term 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 -DiART6plex117@Protein N-term 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 +DiART6plex117@Any_N-term 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 +DiART6plex117@Protein_N-term 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 DiART6plex117@Y 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 DiART6plex118@K 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 -DiART6plex118@Any N-term 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 -DiART6plex118@Protein N-term 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 +DiART6plex118@Any_N-term 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 +DiART6plex118@Protein_N-term 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 DiART6plex118@Y 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 Iodoacetanilide@K 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Artefact 1397 0.0 Iodoacetanilide@C 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Chemical derivative 1397 0.0 -Iodoacetanilide@Any N-term 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Artefact 1397 0.0 +Iodoacetanilide@Any_N-term 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Artefact 1397 0.0 Iodoacetanilide:13C(6)@K 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Artefact 1398 0.0 Iodoacetanilide:13C(6)@C 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Chemical derivative 1398 0.0 -Iodoacetanilide:13C(6)@Any N-term 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Artefact 1398 0.0 +Iodoacetanilide:13C(6)@Any_N-term 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Artefact 1398 0.0 Dap-DSP@K 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Chemical derivative 1399 0.0 Dap-DSP@E 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Non-standard residue 1399 0.0 Dap-DSP@A 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Non-standard residue 1399 0.0 @@ -1769,11 +1769,11 @@ PhosphoHex(2)@T 404.071978 404.2611 H(21)C(12)O(13)P(1) 404.071978 H(21)C(12)O(1 PhosphoHex(2)@S 404.071978 404.2611 H(21)C(12)O(13)P(1) 404.071978 H(21)C(12)O(13)P(1) O-linked glycosylation 1413 0.5 Trimethyl:13C(3)2H(9)@K 54.113505 54.1132 H(-3)2H(9)13C(3) 0.0 Isotopic label 1414 0.0 Trimethyl:13C(3)2H(9)@R 54.113505 54.1132 H(-3)2H(9)13C(3) 0.0 Isotopic label 1414 0.0 -15N-oxobutanoic@S^Protein N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Post-translational 1419 0.0 -15N-oxobutanoic@C^Any N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Artefact 1419 0.0 -15N-oxobutanoic@T^Protein N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Post-translational 1419 0.0 +15N-oxobutanoic@S^Protein_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Post-translational 1419 0.0 +15N-oxobutanoic@C^Any_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Artefact 1419 0.0 +15N-oxobutanoic@T^Protein_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Post-translational 1419 0.0 spermidine@Q 128.131349 128.2153 H(16)C(7)N(2) 0.0 Chemical derivative 1421 0.0 -Biotin:Thermo-21330@Any N-term 473.219571 473.5835 H(35)C(21)N(3)O(7)S(1) 0.0 Chemical derivative 1423 0.0 +Biotin:Thermo-21330@Any_N-term 473.219571 473.5835 H(35)C(21)N(3)O(7)S(1) 0.0 Chemical derivative 1423 0.0 Biotin:Thermo-21330@K 473.219571 473.5835 H(35)C(21)N(3)O(7)S(1) 0.0 Chemical derivative 1423 0.0 Hex(1)Pent(2)@T 426.137341 426.3698 H(26)C(16)O(13) 426.137341 H(26)C(16)O(13) O-linked glycosylation 1428 0.5 Hex(1)Pent(2)@S 426.137341 426.3698 H(26)C(16)O(13) 426.137341 H(26)C(16)O(13) O-linked glycosylation 1428 0.5 @@ -1803,7 +1803,7 @@ Hex(1)HexNAc(1)dHex(1)Me(1)@T 525.205755 525.5009 H(35)C(21)N(1)O(14) 525.205755 Hex(1)HexNAc(1)dHex(1)Me(1)@S 525.205755 525.5009 H(35)C(21)N(1)O(14) 525.205755 H(35)C(21)N(1)O(14) O-linked glycosylation 1436 0.5 Hex(1)HexNAc(1)dHex(1)Me(2)@T 539.221405 539.5275 H(37)C(22)N(1)O(14) 539.221405 H(37)C(22)N(1)O(14) O-linked glycosylation 1437 0.5 Hex(1)HexNAc(1)dHex(1)Me(2)@S 539.221405 539.5275 H(37)C(22)N(1)O(14) 539.221405 H(37)C(22)N(1)O(14) O-linked glycosylation 1437 0.5 -Xlink:DSS[155]@Protein N-term 155.094629 155.1943 H(13)C(8)N(1)O(2) 0.0 Chemical derivative 1789 0.0 +Xlink:DSS[155]@Protein_N-term 155.094629 155.1943 H(13)C(8)N(1)O(2) 0.0 Chemical derivative 1789 0.0 Xlink:DSS[155]@K 155.094629 155.1943 H(13)C(8)N(1)O(2) 0.0 Chemical derivative 1789 0.0 Hex(2)HexNAc(1)@N 527.18502 527.4737 H(33)C(20)N(1)O(15) 527.18502 H(33)C(20)N(1)O(15) N-linked glycosylation 1438 0.5 Hex(2)HexNAc(1)@T 527.18502 527.4737 H(33)C(20)N(1)O(15) 527.18502 H(33)C(20)N(1)O(15) O-linked glycosylation 1438 0.5 @@ -2407,25 +2407,25 @@ Fluorescein-tyramine@Y 493.116152 493.4637 H(19)C(29)N(1)O(7) 0.0 Chemical deri dHex(1)Hex(7)HexNAc(4)@N 2092.745164 2093.8955 H(132)C(80)N(4)O(59) 0.0 N-linked glycosylation 1840 0.0 betaFNA@C 454.210387 454.5155 H(30)C(25)N(2)O(6) 0.0 Chemical derivative 1839 0.0 betaFNA@K 454.210387 454.5155 H(30)C(25)N(2)O(6) 0.0 Chemical derivative 1839 0.0 -Brij58@Any N-term 224.250401 224.4253 H(32)C(16) 0.0 Other 1838 0.0 -Brij35@Any N-term 168.187801 168.319 H(24)C(12) 0.0 Other 1837 0.0 -Triton@Any N-term 188.156501 188.3086 H(20)C(14) 0.0 Other 1836 0.0 -Triton@Any C-term 188.156501 188.3086 H(20)C(14) 0.0 Other 1836 0.0 -Tween80@Any C-term 263.237491 263.4381 H(31)C(18)O(1) 0.0 Other 1835 0.0 -Tween20@Any N-term 165.164326 165.2951 H(21)C(12) 0.0 Other 1834 0.0 +Brij58@Any_N-term 224.250401 224.4253 H(32)C(16) 0.0 Other 1838 0.0 +Brij35@Any_N-term 168.187801 168.319 H(24)C(12) 0.0 Other 1837 0.0 +Triton@Any_N-term 188.156501 188.3086 H(20)C(14) 0.0 Other 1836 0.0 +Triton@Any_C-term 188.156501 188.3086 H(20)C(14) 0.0 Other 1836 0.0 +Tween80@Any_C-term 263.237491 263.4381 H(31)C(18)O(1) 0.0 Other 1835 0.0 +Tween20@Any_N-term 165.164326 165.2951 H(21)C(12) 0.0 Other 1834 0.0 Tris@N 104.071154 104.1277 H(10)C(4)N(1)O(2) 0.0 Artefact 1831 0.0 Biotin-tyramide@Y 361.146012 361.4585 H(23)C(18)N(3)O(3)S(1) 0.0 Chemical derivative 1830 0.0 LRGG+dimethyl@K 411.259403 411.4991 H(33)C(18)N(7)O(4) 0.0 Post-translational 1829 0.0 -RNPXL@R^Any N-term 324.035867 324.1813 H(13)C(9)N(2)O(9)P(1) 324.035867 H(13)C(9)N(2)O(9)P(1) Other 1825 0.5 -RNPXL@K^Any N-term 324.035867 324.1813 H(13)C(9)N(2)O(9)P(1) 324.035867 H(13)C(9)N(2)O(9)P(1) Other 1825 0.5 +RNPXL@R^Any_N-term 324.035867 324.1813 H(13)C(9)N(2)O(9)P(1) 324.035867 H(13)C(9)N(2)O(9)P(1) Other 1825 0.5 +RNPXL@K^Any_N-term 324.035867 324.1813 H(13)C(9)N(2)O(9)P(1) 324.035867 H(13)C(9)N(2)O(9)P(1) Other 1825 0.5 GEE@Q 86.036779 86.0892 H(6)C(4)O(2) 0.0 Chemical derivative 1824 0.0 -Glu->pyro-Glu+Methyl@E^Any N-term -3.994915 -3.9887 C(1)O(-1) 0.0 Artefact 1826 0.0 -Glu->pyro-Glu+Methyl:2H(2)13C(1)@E^Any N-term -0.979006 -0.9837 H(-2)2H(2)13C(1)O(-1) 0.0 Artefact 1827 0.0 +Glu->pyro-Glu+Methyl@E^Any_N-term -3.994915 -3.9887 C(1)O(-1) 0.0 Artefact 1826 0.0 +Glu->pyro-Glu+Methyl:2H(2)13C(1)@E^Any_N-term -0.979006 -0.9837 H(-2)2H(2)13C(1)O(-1) 0.0 Artefact 1827 0.0 LRGG+methyl@K 397.243753 397.4725 H(31)C(17)N(7)O(4) 0.0 Post-translational 1828 0.0 -NP40@Any N-term 220.182715 220.3505 H(24)C(15)O(1) 0.0 Other 1833 0.0 +NP40@Any_N-term 220.182715 220.3505 H(24)C(15)O(1) 0.0 Other 1833 0.0 IASD@C 452.034807 452.4582 H(16)C(18)N(2)O(8)S(2) 0.0 Chemical derivative 1832 0.0 Biotin:Thermo-21328@K 389.090154 389.5564 H(23)C(15)N(3)O(3)S(3) 0.0 Chemical derivative 1841 0.0 -Biotin:Thermo-21328@Any N-term 389.090154 389.5564 H(23)C(15)N(3)O(3)S(3) 0.0 Chemical derivative 1841 0.0 +Biotin:Thermo-21328@Any_N-term 389.090154 389.5564 H(23)C(15)N(3)O(3)S(3) 0.0 Chemical derivative 1841 0.0 PhosphoCytidine@Y 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 PhosphoCytidine@T 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 PhosphoCytidine@S 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 @@ -2436,65 +2436,65 @@ hydroxyisobutyryl@K 86.036779 86.0892 H(6)C(4)O(2) 0.0 Post-translational 1849 MeMePhosphorothioate@S 107.979873 108.0993 H(5)C(2)O(1)P(1)S(1) 0.0 Chemical derivative 1868 0.0 Cation:Fe[III]@D 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 Cation:Fe[III]@E 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 -Cation:Fe[III]@Any C-term 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 +Cation:Fe[III]@Any_C-term 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 DTT@C 151.996571 152.2351 H(8)C(4)O(2)S(2) 0.0 Artefact 1871 0.0 DYn-2@C 161.09664 161.2203 H(13)C(11)O(1) 0.0 Other 1872 0.0 Xlink:DSSO[176]@K 176.01433 176.1903 H(8)C(6)O(4)S(1) 0.0 Chemical derivative 1878 0.0 -Xlink:DSSO[176]@Protein N-term 176.01433 176.1903 H(8)C(6)O(4)S(1) 0.0 Chemical derivative 1878 0.0 +Xlink:DSSO[176]@Protein_N-term 176.01433 176.1903 H(8)C(6)O(4)S(1) 0.0 Chemical derivative 1878 0.0 MesitylOxide@K 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 MesitylOxide@H 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 -MesitylOxide@Protein N-term 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 +MesitylOxide@Protein_N-term 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 Xlink:DSS[259]@K 259.141973 259.2988 H(21)C(12)N(1)O(5) 0.0 Chemical derivative 1877 0.0 -Xlink:DSS[259]@Protein N-term 259.141973 259.2988 H(21)C(12)N(1)O(5) 0.0 Chemical derivative 1877 0.0 +Xlink:DSS[259]@Protein_N-term 259.141973 259.2988 H(21)C(12)N(1)O(5) 0.0 Chemical derivative 1877 0.0 methylol@Y 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 methylol@W 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 methylol@K 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 Xlink:DSSO[175]@K 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1879 0.0 -Xlink:DSSO[175]@Protein N-term 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1879 0.0 +Xlink:DSSO[175]@Protein_N-term 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1879 0.0 Xlink:DSSO[279]@K 279.077658 279.3101 H(17)C(10)N(1)O(6)S(1) 0.0 Chemical derivative 1880 0.0 -Xlink:DSSO[279]@Protein N-term 279.077658 279.3101 H(17)C(10)N(1)O(6)S(1) 0.0 Chemical derivative 1880 0.0 -Xlink:DSSO[54]@Protein N-term 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 1881 0.0 +Xlink:DSSO[279]@Protein_N-term 279.077658 279.3101 H(17)C(10)N(1)O(6)S(1) 0.0 Chemical derivative 1880 0.0 +Xlink:DSSO[54]@Protein_N-term 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 1881 0.0 Xlink:DSSO[54]@K 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 1881 0.0 Xlink:DSSO[86]@K 85.982635 86.1124 H(2)C(3)O(1)S(1) 0.0 Chemical derivative 1882 0.0 -Xlink:DSSO[86]@Protein N-term 85.982635 86.1124 H(2)C(3)O(1)S(1) 0.0 Chemical derivative 1882 0.0 +Xlink:DSSO[86]@Protein_N-term 85.982635 86.1124 H(2)C(3)O(1)S(1) 0.0 Chemical derivative 1882 0.0 Xlink:DSSO[104]@K 103.9932 104.1277 H(4)C(3)O(2)S(1) 0.0 Chemical derivative 1883 0.0 -Xlink:DSSO[104]@Protein N-term 103.9932 104.1277 H(4)C(3)O(2)S(1) 0.0 Chemical derivative 1883 0.0 +Xlink:DSSO[104]@Protein_N-term 103.9932 104.1277 H(4)C(3)O(2)S(1) 0.0 Chemical derivative 1883 0.0 Xlink:BuUrBu[111]@K 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 -Xlink:BuUrBu[111]@Protein N-term 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 +Xlink:BuUrBu[111]@Protein_N-term 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 Xlink:BuUrBu[85]@K 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 -Xlink:BuUrBu[85]@Protein N-term 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 -Xlink:BuUrBu[214]@Protein N-term 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 +Xlink:BuUrBu[85]@Protein_N-term 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 +Xlink:BuUrBu[214]@Protein_N-term 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 Xlink:BuUrBu[214]@K 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 -Xlink:BuUrBu[317]@Protein N-term 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 +Xlink:BuUrBu[317]@Protein_N-term 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 Xlink:BuUrBu[317]@K 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 Xlink:DSSO[158]@K 158.003765 158.175 H(6)C(6)O(3)S(1) 0.0 Chemical derivative 1896 0.0 -Xlink:DSSO[158]@Protein N-term 158.003765 158.175 H(6)C(6)O(3)S(1) 0.0 Chemical derivative 1896 0.0 +Xlink:DSSO[158]@Protein_N-term 158.003765 158.175 H(6)C(6)O(3)S(1) 0.0 Chemical derivative 1896 0.0 Xlink:DSS[138]@K 138.06808 138.1638 H(10)C(8)O(2) 0.0 Chemical derivative 1898 0.0 -Xlink:DSS[138]@Protein N-term 138.06808 138.1638 H(10)C(8)O(2) 0.0 Chemical derivative 1898 0.0 -Xlink:BuUrBu[196]@Protein N-term 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 +Xlink:DSS[138]@Protein_N-term 138.06808 138.1638 H(10)C(8)O(2) 0.0 Chemical derivative 1898 0.0 +Xlink:BuUrBu[196]@Protein_N-term 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 Xlink:BuUrBu[196]@K 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 Xlink:DTBP[172]@K 172.01289 172.2711 H(8)C(6)N(2)S(2) 0.0 Chemical derivative 1900 0.0 -Xlink:DTBP[172]@Protein N-term 172.01289 172.2711 H(8)C(6)N(2)S(2) 0.0 Chemical derivative 1900 0.0 +Xlink:DTBP[172]@Protein_N-term 172.01289 172.2711 H(8)C(6)N(2)S(2) 0.0 Chemical derivative 1900 0.0 Xlink:DST[114]@K 113.995309 114.0563 H(2)C(4)O(4) 0.0 Chemical derivative 1901 0.0 -Xlink:DST[114]@Protein N-term 113.995309 114.0563 H(2)C(4)O(4) 0.0 Chemical derivative 1901 0.0 +Xlink:DST[114]@Protein_N-term 113.995309 114.0563 H(2)C(4)O(4) 0.0 Chemical derivative 1901 0.0 Xlink:DTSSP[174]@K 173.980921 174.2406 H(6)C(6)O(2)S(2) 0.0 Chemical derivative 1902 0.0 -Xlink:DTSSP[174]@Protein N-term 173.980921 174.2406 H(6)C(6)O(2)S(2) 0.0 Chemical derivative 1902 0.0 +Xlink:DTSSP[174]@Protein_N-term 173.980921 174.2406 H(6)C(6)O(2)S(2) 0.0 Chemical derivative 1902 0.0 Xlink:SMCC[219]@C 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 Xlink:SMCC[219]@K 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 -Xlink:SMCC[219]@Protein N-term 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 +Xlink:SMCC[219]@Protein_N-term 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 Cation:Al[III]@D 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 Cation:Al[III]@E 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 -Cation:Al[III]@Any C-term 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 -Xlink:BS2G[113]@Protein N-term 113.047679 113.1146 H(7)C(5)N(1)O(2) 0.0 Chemical derivative 1906 0.0 +Cation:Al[III]@Any_C-term 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 +Xlink:BS2G[113]@Protein_N-term 113.047679 113.1146 H(7)C(5)N(1)O(2) 0.0 Chemical derivative 1906 0.0 Xlink:BS2G[113]@K 113.047679 113.1146 H(7)C(5)N(1)O(2) 0.0 Chemical derivative 1906 0.0 -Xlink:BS2G[114]@Protein N-term 114.031694 114.0993 H(6)C(5)O(3) 0.0 Chemical derivative 1907 0.0 +Xlink:BS2G[114]@Protein_N-term 114.031694 114.0993 H(6)C(5)O(3) 0.0 Chemical derivative 1907 0.0 Xlink:BS2G[114]@K 114.031694 114.0993 H(6)C(5)O(3) 0.0 Chemical derivative 1907 0.0 -Xlink:BS2G[217]@Protein N-term 217.095023 217.2191 H(15)C(9)N(1)O(5) 0.0 Chemical derivative 1908 0.0 +Xlink:BS2G[217]@Protein_N-term 217.095023 217.2191 H(15)C(9)N(1)O(5) 0.0 Chemical derivative 1908 0.0 Xlink:BS2G[217]@K 217.095023 217.2191 H(15)C(9)N(1)O(5) 0.0 Chemical derivative 1908 0.0 Xlink:DMP[139]@K 139.110947 139.1982 H(13)C(7)N(3) 0.0 Chemical derivative 1911 0.0 -Xlink:DMP[139]@Protein N-term 139.110947 139.1982 H(13)C(7)N(3) 0.0 Chemical derivative 1911 0.0 +Xlink:DMP[139]@Protein_N-term 139.110947 139.1982 H(13)C(7)N(3) 0.0 Chemical derivative 1911 0.0 Xlink:DMP[122]@K 122.084398 122.1677 H(10)C(7)N(2) 0.0 Chemical derivative 1912 0.0 -Xlink:DMP[122]@Protein N-term 122.084398 122.1677 H(10)C(7)N(2) 0.0 Chemical derivative 1912 0.0 +Xlink:DMP[122]@Protein_N-term 122.084398 122.1677 H(10)C(7)N(2) 0.0 Chemical derivative 1912 0.0 glyoxalAGE@R 21.98435 22.0055 H(-2)C(2) 0.0 Post-translational 1913 0.0 Met->AspSA@M -32.008456 -32.1081 H(-4)C(-1)O(1)S(-1) 0.0 Chemical derivative 1914 0.0 Decarboxylation@D -30.010565 -30.026 H(-2)C(-1)O(-1) 0.0 Chemical derivative 1915 0.0 @@ -2589,44 +2589,44 @@ Hex(5)HexNAc(4)NeuAc(1)Ac(1)@N 1955.687589 1956.7643 H(121)C(75)N(5)O(54) 1955.6 Hex(3)HexNAc(3)NeuAc(3)@S 1968.682838 1969.7631 H(120)C(75)N(6)O(54) 1968.682838 H(120)C(75)N(6)O(54) O-linked glycosylation 1968 0.5 Hex(3)HexNAc(3)NeuAc(3)@T 1968.682838 1969.7631 H(120)C(75)N(6)O(54) 1968.682838 H(120)C(75)N(6)O(54) O-linked glycosylation 1968 0.5 Hex(5)HexNAc(4)NeuAc(1)Ac(2)@N 1997.698154 1998.801 H(123)C(77)N(5)O(55) 1997.698154 H(123)C(77)N(5)O(55) N-linked glycosylation 1969 0.5 -Unknown:162@Any C-term 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 +Unknown:162@Any_C-term 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 Unknown:162@E 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 Unknown:162@D 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 -Unknown:162@Any N-term 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 +Unknown:162@Any_N-term 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 Unknown:177@D 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 Unknown:177@E 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 -Unknown:177@Any C-term 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 -Unknown:177@Any N-term 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 +Unknown:177@Any_C-term 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 +Unknown:177@Any_N-term 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 Unknown:210@D 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 Unknown:210@E 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 -Unknown:210@Any C-term 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 -Unknown:210@Any N-term 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 +Unknown:210@Any_C-term 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 +Unknown:210@Any_N-term 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 Unknown:216@D 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 Unknown:216@E 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 -Unknown:216@Any C-term 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 -Unknown:216@Any N-term 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 +Unknown:216@Any_C-term 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 +Unknown:216@Any_N-term 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 Unknown:234@D 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 Unknown:234@E 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 -Unknown:234@Any C-term 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 -Unknown:234@Any N-term 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 +Unknown:234@Any_C-term 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 +Unknown:234@Any_N-term 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 Unknown:248@D 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 Unknown:248@E 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 -Unknown:248@Any C-term 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 -Unknown:248@Any N-term 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 +Unknown:248@Any_C-term 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 +Unknown:248@Any_N-term 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 Unknown:250@D 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 Unknown:250@E 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 -Unknown:250@Any C-term 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 -Unknown:250@Any N-term 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 +Unknown:250@Any_C-term 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 +Unknown:250@Any_N-term 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 Unknown:302@D 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 Unknown:302@E 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 -Unknown:302@Any C-term 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 -Unknown:302@Any N-term 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 +Unknown:302@Any_C-term 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 +Unknown:302@Any_N-term 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 Unknown:306@D 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 Unknown:306@E 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 -Unknown:306@Any C-term 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 -Unknown:306@Any N-term 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 -Unknown:420@Any N-term 420.051719 420.5888 H(24)C(12)N(2)O(6)S(4) 420.051719 H(24)C(12)N(2)O(6)S(4) Artefact 1979 0.5 -Unknown:420@Any C-term 420.051719 420.5888 H(24)C(12)N(2)O(6)S(4) 420.051719 H(24)C(12)N(2)O(6)S(4) Artefact 1979 0.5 +Unknown:306@Any_C-term 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 +Unknown:306@Any_N-term 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 +Unknown:420@Any_N-term 420.051719 420.5888 H(24)C(12)N(2)O(6)S(4) 420.051719 H(24)C(12)N(2)O(6)S(4) Artefact 1979 0.5 +Unknown:420@Any_C-term 420.051719 420.5888 H(24)C(12)N(2)O(6)S(4) 420.051719 H(24)C(12)N(2)O(6)S(4) Artefact 1979 0.5 Diethylphosphothione@Y 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 Diethylphosphothione@T 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 Diethylphosphothione@S 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 @@ -2649,9 +2649,9 @@ monomethylphosphothione@T 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical monomethylphosphothione@Y 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 TMPP-Ac:13C(9)@Y 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Artefact 1993 0.0 TMPP-Ac:13C(9)@K 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Artefact 1993 0.0 -TMPP-Ac:13C(9)@Any N-term 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Chemical derivative 1993 0.0 +TMPP-Ac:13C(9)@Any_N-term 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Chemical derivative 1993 0.0 ZQG@K 320.100836 320.2973 H(16)C(15)N(2)O(6) 134.036779 H(6)C(8)O(2) Chemical derivative 2001 0.5 -Xlink:DST[56]@Protein N-term 55.989829 56.0202 C(2)O(2) 0.0 Chemical derivative 1999 0.0 +Xlink:DST[56]@Protein_N-term 55.989829 56.0202 C(2)O(2) 0.0 Chemical derivative 1999 0.0 Xlink:DST[56]@K 55.989829 56.0202 C(2)O(2) 0.0 Chemical derivative 1999 0.0 Haloxon@Y 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 Haloxon@T 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 @@ -2666,19 +2666,19 @@ Methamidophos-O@K 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivativ Methamidophos-O@H 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 Methamidophos-O@C 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 Nitrene@Y 12.995249 12.9988 H(-1)N(1) 0.0 Artefact 2014 0.0 -shTMT@Any N-term 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 -shTMT@Protein N-term 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 +shTMT@Any_N-term 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 +shTMT@Protein_N-term 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 shTMT@K 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 TMTpro@S 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 TMTpro@H 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 -TMTpro@Protein N-term 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 -TMTpro@Any N-term 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 +TMTpro@Protein_N-term 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 +TMTpro@Any_N-term 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 TMTpro@K 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 TMTpro@T 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 TMTpro_zero@S 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 TMTpro_zero@H 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 -TMTpro_zero@Protein N-term 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 -TMTpro_zero@Any N-term 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 +TMTpro_zero@Protein_N-term 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 +TMTpro_zero@Any_N-term 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 TMTpro_zero@K 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 TMTpro_zero@T 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 Andro-H2O@C 332.19876 332.4339 H(28)C(20)O(4) 0.0 Chemical derivative 2025 0.0 diff --git a/scripts/unimod_to_tsv.ipynb b/scripts/unimod_to_tsv.ipynb index d07f983a..fe640ada 100644 --- a/scripts/unimod_to_tsv.ipynb +++ b/scripts/unimod_to_tsv.ipynb @@ -1,28 +1,45 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "import xml.etree.ElementTree as ET\n", "import yaml\n", - "import pandas as pd\n", - "\n", + "import pandas as pd" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ "def save_yaml(filename, unimod):\n", " with open(filename, \"w\") as file:\n", " yaml.dump(unimod, file)\n", "\n", - "xml = ET.parse('unimod.xml')\n", - "root = xml.getroot()\n", - "\n", "\n", "def get_composition(node):\n", " composition = \"\"\n", " for elem in node.findall(f'{xmlns}element'):\n", " composition += elem.attrib['symbol']+'('+elem.attrib['number']+')'\n", - " return composition\n", + " return composition" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2024-06-25T11:39:09.284411Z", + "start_time": "2024-06-25T11:39:08.507205Z" + } + }, + "source": [ + "xml = ET.parse('unimod.xml')\n", + "root = xml.getroot()\n", "\n", "xmlns = '{http://www.unimod.org/xmlns/schema/unimod_2}'\n", "unimod = {}\n", @@ -72,15 +89,78 @@ " unimod[mod_site]['modloss_composition'] = ptm_nl_composition\n", " unimod[mod_site]['classification'] = _class\n", " unimod[mod_site]['unimod_id'] = int(id)" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-06-25T11:56:30.184321Z", + "start_time": "2024-06-25T11:56:30.165972Z" + } + }, + "source": [ + "df = pd.DataFrame().from_dict(unimod, orient='index')\n", + "df.index = df.index.str.replace(\" \", \"_\", regex=False)\n", + "df['modloss_importance'] = 0.0\n", + "df.loc[df.modloss_composition != '','modloss_importance'] = 0.5\n", + "df.loc['Phospho@S','modloss_importance'] = 1e8\n", + "df.loc['Phospho@T','modloss_importance'] = 1e7\n", + "df.loc['GG@K','modloss_importance'] = 1e6\n", + "df.loc['GlyGly@K',:] = df.loc['GG@K']\n", + "df.loc['GlyGly@K','classification'] = 'Multiple'\n", + "df['mod_name'] = df.index.values\n", + "df = df[['mod_name']+[col for col in df.columns if col != 'mod_name']]\n", + "df['unimod_id'] = df.unimod_id.astype(int)\n", + "df" + ], "outputs": [ { "data": { + "text/plain": [ + " mod_name unimod_mass unimod_avge_mass \\\n", + "Acetyl@T Acetyl@T 42.010565 42.0367 \n", + "Acetyl@Protein_N-term Acetyl@Protein_N-term 42.010565 42.0367 \n", + "Acetyl@S Acetyl@S 42.010565 42.0367 \n", + "Acetyl@C Acetyl@C 42.010565 42.0367 \n", + "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 42.0367 \n", + "... ... ... ... \n", + "TMTpro_zero@K TMTpro_zero@K 295.189592 295.3773 \n", + "TMTpro_zero@T TMTpro_zero@T 295.189592 295.3773 \n", + "Andro-H2O@C Andro-H2O@C 332.198760 332.4339 \n", + "His+O(2)@H His+O(2)@H 169.048741 169.1381 \n", + "GlyGly@K GlyGly@K 114.042927 114.1026 \n", + "\n", + " composition unimod_modloss modloss_composition \\\n", + "Acetyl@T H(2)C(2)O(1) 0.0 \n", + "Acetyl@Protein_N-term H(2)C(2)O(1) 0.0 \n", + "Acetyl@S H(2)C(2)O(1) 0.0 \n", + "Acetyl@C H(2)C(2)O(1) 0.0 \n", + "Acetyl@Any_N-term H(2)C(2)O(1) 0.0 \n", + "... ... ... ... \n", + "TMTpro_zero@K H(25)C(15)N(3)O(3) 0.0 \n", + "TMTpro_zero@T H(25)C(15)N(3)O(3) 0.0 \n", + "Andro-H2O@C H(28)C(20)O(4) 0.0 \n", + "His+O(2)@H H(7)C(6)N(3)O(3) 0.0 \n", + "GlyGly@K H(6)C(4)N(2)O(2) 0.0 \n", + "\n", + " classification unimod_id modloss_importance \n", + "Acetyl@T Post-translational 1 0.0 \n", + "Acetyl@Protein_N-term Post-translational 1 0.0 \n", + "Acetyl@S Post-translational 1 0.0 \n", + "Acetyl@C Post-translational 1 0.0 \n", + "Acetyl@Any_N-term Multiple 1 0.0 \n", + "... ... ... ... \n", + "TMTpro_zero@K Chemical derivative 2017 0.0 \n", + "TMTpro_zero@T Chemical derivative 2017 0.0 \n", + "Andro-H2O@C Chemical derivative 2025 0.0 \n", + "His+O(2)@H Post-translational 2027 0.0 \n", + "GlyGly@K Multiple 121 1000000.0 \n", + "\n", + "[2685 rows x 9 columns]" + ], "text/html": [ "
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abundancemass
13C[0.01, 0.99][12.0, 13.00335483507]
14N[0.996337, 0.003663][14.00307400443, 15.00010889888]
15N[0.01, 0.99][14.00307400443, 15.00010889888]
18O[0.005, 0.005, 0.99][15.99491461957, 16.9991317565, 17.99915961286]
2H[0.01, 0.99][1.00782503223, 2.01410177812]
.........
Xe[0.000952, 0.00089, 0.019102, 0.264006, 0.0407...[123.905892, 125.9042983, 127.903531, 128.9047...
Y[1.0][88.9058403]
Yb[0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0....[167.9338896, 169.9347664, 170.9363302, 171.93...
Zn[0.4917, 0.2773, 0.0404, 0.1845, 0.0061][63.92914201, 65.92603381, 66.92712775, 67.924...
Zr[0.5145, 0.1122, 0.1715, 0.1738, 0.028][89.9046977, 90.9056396, 91.9050347, 93.906310...
\n", - "

109 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " abundance \\\n", - "13C [0.01, 0.99] \n", - "14N [0.996337, 0.003663] \n", - "15N [0.01, 0.99] \n", - "18O [0.005, 0.005, 0.99] \n", - "2H [0.01, 0.99] \n", - ".. ... \n", - "Xe [0.000952, 0.00089, 0.019102, 0.264006, 0.0407... \n", - "Y [1.0] \n", - "Yb [0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0.... \n", - "Zn [0.4917, 0.2773, 0.0404, 0.1845, 0.0061] \n", - "Zr [0.5145, 0.1122, 0.1715, 0.1738, 0.028] \n", - "\n", - " mass \n", - "13C [12.0, 13.00335483507] \n", - "14N [14.00307400443, 15.00010889888] \n", - "15N [14.00307400443, 15.00010889888] \n", - "18O [15.99491461957, 16.9991317565, 17.99915961286] \n", - "2H [1.00782503223, 2.01410177812] \n", - ".. ... \n", - "Xe [123.905892, 125.9042983, 127.903531, 128.9047... \n", - "Y [88.9058403] \n", - "Yb [167.9338896, 169.9347664, 170.9363302, 171.93... \n", - "Zn [63.92914201, 65.92603381, 66.92712775, 67.924... \n", - "Zr [89.9046977, 90.9056396, 91.9050347, 93.906310... \n", - "\n", - "[109 rows x 2 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "from alphabase.constants.atom import CHEM_INFO_DICT\n", - "pd.DataFrame().from_dict(CHEM_INFO_DICT, orient='index')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And their mono-isotopic mass are in `CHEM_MONO_MASS` (dict):" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0
13C13.003355
14N14.003074
15N15.000109
18O17.999160
2H2.014102
......
Xe131.904155
Y88.905840
Yb173.938866
Zn63.929142
Zr89.904698
\n", - "

109 rows × 1 columns

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" - ], - "text/plain": [ - " 0\n", - "13C 13.003355\n", - "14N 14.003074\n", - "15N 15.000109\n", - "18O 17.999160\n", - "2H 2.014102\n", - ".. ...\n", - "Xe 131.904155\n", - "Y 88.905840\n", - "Yb 173.938866\n", - "Zn 63.929142\n", - "Zr 89.904698\n", - "\n", - "[109 rows x 1 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.atom import CHEM_MONO_MASS\n", - "pd.DataFrame().from_dict(CHEM_MONO_MASS, orient='index')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These atom masses are used to calculate the masses of amino acids, modifications, and then subsequent masses of peptides and fragments." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Commonly used molecular masses" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.007276467, 1.0033, 17.02654910112, 18.01056468403)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.atom import (\n", - " MASS_PROTON, MASS_ISOTOPE, MASS_NH3, MASS_H2O\n", - ")\n", - "MASS_PROTON, MASS_ISOTOPE, MASS_NH3, MASS_H2O" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Amino Acids" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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aaformulamass
65AC(3)H(5)N(1)O(1)S(0)7.103711e+01
66BC(1000000)1.200000e+07
67CC(3)H(5)N(1)O(1)S(1)1.030092e+02
68DC(4)H(5)N(1)O(3)S(0)1.150269e+02
69EC(5)H(7)N(1)O(3)S(0)1.290426e+02
70FC(9)H(9)N(1)O(1)S(0)1.470684e+02
71GC(2)H(3)N(1)O(1)S(0)5.702146e+01
72HC(6)H(7)N(3)O(1)S(0)1.370589e+02
73IC(6)H(11)N(1)O(1)S(0)1.130841e+02
74JC(6)H(11)N(1)O(1)S(0)1.130841e+02
75KC(6)H(12)N(2)O(1)S(0)1.280950e+02
76LC(6)H(11)N(1)O(1)S(0)1.130841e+02
77MC(5)H(9)N(1)O(1)S(1)1.310405e+02
78NC(4)H(6)N(2)O(2)S(0)1.140429e+02
79OC(12)H(19)N(3)O(2)2.371477e+02
80PC(5)H(7)N(1)O(1)S(0)9.705276e+01
81QC(5)H(8)N(2)O(2)S(0)1.280586e+02
82RC(6)H(12)N(4)O(1)S(0)1.561011e+02
83SC(3)H(5)N(1)O(2)S(0)8.703203e+01
84TC(4)H(7)N(1)O(2)S(0)1.010477e+02
85UC(3)H(5)N(1)O(1)Se(1)1.509536e+02
86VC(5)H(9)N(1)O(1)S(0)9.906841e+01
87WC(11)H(10)N(2)O(1)S(0)1.860793e+02
88XC(1000000)1.200000e+07
89YC(9)H(9)N(1)O(2)S(0)1.630633e+02
90ZC(1000000)1.200000e+07
\n", - "
" - ], - "text/plain": [ - " aa formula mass\n", - "65 A C(3)H(5)N(1)O(1)S(0) 7.103711e+01\n", - "66 B C(1000000) 1.200000e+07\n", - "67 C C(3)H(5)N(1)O(1)S(1) 1.030092e+02\n", - "68 D C(4)H(5)N(1)O(3)S(0) 1.150269e+02\n", - "69 E C(5)H(7)N(1)O(3)S(0) 1.290426e+02\n", - "70 F C(9)H(9)N(1)O(1)S(0) 1.470684e+02\n", - "71 G C(2)H(3)N(1)O(1)S(0) 5.702146e+01\n", - "72 H C(6)H(7)N(3)O(1)S(0) 1.370589e+02\n", - "73 I C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", - "74 J C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", - "75 K C(6)H(12)N(2)O(1)S(0) 1.280950e+02\n", - "76 L C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", - "77 M C(5)H(9)N(1)O(1)S(1) 1.310405e+02\n", - "78 N C(4)H(6)N(2)O(2)S(0) 1.140429e+02\n", - "79 O C(12)H(19)N(3)O(2) 2.371477e+02\n", - "80 P C(5)H(7)N(1)O(1)S(0) 9.705276e+01\n", - "81 Q C(5)H(8)N(2)O(2)S(0) 1.280586e+02\n", - "82 R C(6)H(12)N(4)O(1)S(0) 1.561011e+02\n", - "83 S C(3)H(5)N(1)O(2)S(0) 8.703203e+01\n", - "84 T C(4)H(7)N(1)O(2)S(0) 1.010477e+02\n", - "85 U C(3)H(5)N(1)O(1)Se(1) 1.509536e+02\n", - "86 V C(5)H(9)N(1)O(1)S(0) 9.906841e+01\n", - "87 W C(11)H(10)N(2)O(1)S(0) 1.860793e+02\n", - "88 X C(1000000) 1.200000e+07\n", - "89 Y C(9)H(9)N(1)O(2)S(0) 1.630633e+02\n", - "90 Z C(1000000) 1.200000e+07" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.aa import AA_DF\n", - "AA_DF.loc[ord('A'):ord('Z')]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From `AA_DF`, we can see that amino acids are encoded by ASCII (128 characters). 65==ord('A'), ..., 90==ord('Z'). Unicode strings can be fastly converted to ascii int32 values using numpy:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([65, 66, 67, 88, 89, 90], dtype=int32)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "np.array(['ABCXYZ']).view(np.int32)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But users does not need to know this, as we provided easy to use functionalities to get residue masses from sequences." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate AA masses in batch" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[131.04048509, 71.03711379, 103.00918496, 115.02694302,\n", - " 129.04259309, 147.06841391, 57.02146372],\n", - " [131.04048509, 71.03711379, 128.09496302, 115.02694302,\n", - " 129.04259309, 147.06841391, 57.02146372],\n", - " [131.04048509, 71.03711379, 128.09496302, 115.02694302,\n", - " 129.04259309, 147.06841391, 156.10111102]])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.aa import calc_AA_masses_for_same_len_seqs\n", - "calc_AA_masses_for_same_len_seqs(\n", - " [\n", - " 'MACDEFG', 'MAKDEFG', 'MAKDEFR'\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Modifications\n", - "\n", - "In AlphaBase, we used `mod_name@aa` to represent a modification, the `mod_name` is from UniMod. We also used `mod_name@Protein N-term`, `mod_name@Any N-term` and `mod_name@Any C-term` for terminal modifications, which follow the UniMod terminal name schema.\n", - "\n", - "The default modification TSV is stored in `alphabase/constants/const_files/modification.tsv`, users can add more modifications into the tsv file (only `mod_name` and `composition` colums are required). Please https://github.com/MannLabs/alphabase/blob/main/alphabase/constants/const_files/modification.tsv." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mod_nameunimod_massunimod_avge_masscompositionunimod_modlossmodloss_compositionclassificationunimod_idmodloss_importancemassmodloss_originalmodloss
mod_name
Acetyl@TAcetyl@T42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
Acetyl@Protein N-termAcetyl@Protein N-term42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
Acetyl@SAcetyl@S42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
Acetyl@CAcetyl@C42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
Acetyl@Any N-termAcetyl@Any N-term42.01056542.0367H(2)C(2)O(1)0.0Multiple10.042.0105650.00.0
.......................................
TMTpro_zero@KTMTpro_zero@K295.189592295.3773H(25)C(15)N(3)O(3)0.0Chemical derivative20170.0295.1895920.00.0
TMTpro_zero@TTMTpro_zero@T295.189592295.3773H(25)C(15)N(3)O(3)0.0Chemical derivative20170.0295.1895920.00.0
Andro-H2O@CAndro-H2O@C332.198760332.4339H(28)C(20)O(4)0.0Chemical derivative20250.0332.1987590.00.0
His+O(2)@HHis+O(2)@H169.048741169.1381H(7)C(6)N(3)O(3)0.0Post-translational20270.0169.0487410.00.0
GlyGly@KGlyGly@K114.042927114.1026H(6)C(4)N(2)O(2)0.0Post-translational1211000000.0114.0429270.00.0
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2685 rows × 12 columns

\n", - "
" - ], - "text/plain": [ - " mod_name unimod_mass unimod_avge_mass \\\n", - "mod_name \n", - "Acetyl@T Acetyl@T 42.010565 42.0367 \n", - "Acetyl@Protein N-term Acetyl@Protein N-term 42.010565 42.0367 \n", - "Acetyl@S Acetyl@S 42.010565 42.0367 \n", - "Acetyl@C Acetyl@C 42.010565 42.0367 \n", - "Acetyl@Any N-term Acetyl@Any N-term 42.010565 42.0367 \n", - "... ... ... ... \n", - "TMTpro_zero@K TMTpro_zero@K 295.189592 295.3773 \n", - "TMTpro_zero@T TMTpro_zero@T 295.189592 295.3773 \n", - "Andro-H2O@C Andro-H2O@C 332.198760 332.4339 \n", - "His+O(2)@H His+O(2)@H 169.048741 169.1381 \n", - "GlyGly@K GlyGly@K 114.042927 114.1026 \n", - "\n", - " composition unimod_modloss modloss_composition \\\n", - "mod_name \n", - "Acetyl@T H(2)C(2)O(1) 0.0 \n", - "Acetyl@Protein N-term H(2)C(2)O(1) 0.0 \n", - "Acetyl@S H(2)C(2)O(1) 0.0 \n", - "Acetyl@C H(2)C(2)O(1) 0.0 \n", - "Acetyl@Any N-term H(2)C(2)O(1) 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K H(25)C(15)N(3)O(3) 0.0 \n", - "TMTpro_zero@T H(25)C(15)N(3)O(3) 0.0 \n", - "Andro-H2O@C H(28)C(20)O(4) 0.0 \n", - "His+O(2)@H H(7)C(6)N(3)O(3) 0.0 \n", - "GlyGly@K H(6)C(4)N(2)O(2) 0.0 \n", - "\n", - " classification unimod_id modloss_importance \\\n", - "mod_name \n", - "Acetyl@T Post-translational 1 0.0 \n", - "Acetyl@Protein N-term Post-translational 1 0.0 \n", - "Acetyl@S Post-translational 1 0.0 \n", - "Acetyl@C Post-translational 1 0.0 \n", - "Acetyl@Any N-term Multiple 1 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K Chemical derivative 2017 0.0 \n", - "TMTpro_zero@T Chemical derivative 2017 0.0 \n", - "Andro-H2O@C Chemical derivative 2025 0.0 \n", - "His+O(2)@H Post-translational 2027 0.0 \n", - "GlyGly@K Post-translational 121 1000000.0 \n", - "\n", - " mass modloss_original modloss \n", - "mod_name \n", - "Acetyl@T 42.010565 0.0 0.0 \n", - "Acetyl@Protein N-term 42.010565 0.0 0.0 \n", - "Acetyl@S 42.010565 0.0 0.0 \n", - "Acetyl@C 42.010565 0.0 0.0 \n", - "Acetyl@Any N-term 42.010565 0.0 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K 295.189592 0.0 0.0 \n", - "TMTpro_zero@T 295.189592 0.0 0.0 \n", - "Andro-H2O@C 332.198759 0.0 0.0 \n", - "His+O(2)@H 169.048741 0.0 0.0 \n", - "GlyGly@K 114.042927 0.0 0.0 \n", - "\n", - "[2685 rows x 12 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.modification import MOD_DF\n", - "MOD_DF" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Modification sites\n", - "\n", - "In alphabase, we use 0 and -1 to represent modification site of N-term and C-term, respectively. For other modification sites, we use 1 to n." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([42.01056468, 0. , 57.02146372, 0. , 0. ,\n", - " 0. , 0. ])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.modification import calc_modification_mass\n", - "sequence = 'MACDEFG'\n", - "mod_names = ['Acetyl@Any N-term', 'Carbamidomethyl@C']\n", - "mod_sites = [0,3]\n", - "calc_modification_mass(\n", - " nAA=len(sequence),\n", - " mod_names=mod_names,\n", - " mod_sites=mod_sites\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The modifications on the first amino acid and N-term will be added." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([58.0054793, 0. , 0. , 0. , 0. ,\n", - " 0. , 0. ])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sequence = 'MAKDEFG'\n", - "mod_names = ['Acetyl@Any N-term', 'Oxidation@M']\n", - "mod_sites = [0,1]\n", - "calc_modification_mass(\n", - " nAA=len(sequence),\n", - " mod_names=mod_names,\n", - " mod_sites=mod_sites\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Multiple modification at a single site is supported, for example, in the following example, `K3` contains both `GlyGly@K` and `Dimethyl@K`:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 0. , 142.07422757, 0. ,\n", - " 0. , 0. , 0. ])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sequence = 'MAKDEFR'\n", - "mod_names = ['GlyGly@K', 'Dimethyl@K']\n", - "mod_sites = [3,3]\n", - "calc_modification_mass(\n", - " nAA=len(sequence),\n", - " mod_names=mod_names,\n", - " mod_sites=mod_sites\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Caculate modification masses in batch" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 42.01056468, 0. , 57.02146372, 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 58.0054793 , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , 142.07422757, 0. ,\n", - " 0. , 0. , 0. ]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.modification import calc_mod_masses_for_same_len_seqs\n", - "calc_mod_masses_for_same_len_seqs(\n", - " nAA=7,\n", - " mod_names_list=[\n", - " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any N-term', 'Oxidation@M'],\n", - " ['GlyGly@K', 'Dimethyl@K'],\n", - " ],\n", - " mod_sites_list=[\n", - " [0, 3],\n", - " [0, 1],\n", - " [3, 3],\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mass calculation functionalities" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate AA and modification masses in batch" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[173.05104977, 71.03711379, 160.03064868, 115.02694302,\n", - " 129.04259309, 147.06841391, 57.02146372],\n", - " [189.04596439, 71.03711379, 128.09496302, 115.02694302,\n", - " 129.04259309, 147.06841391, 57.02146372],\n", - " [131.04048509, 71.03711379, 270.16919059, 115.02694302,\n", - " 129.04259309, 147.06841391, 156.10111102]])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.aa import calc_AA_masses_for_same_len_seqs\n", - "from alphabase.constants.modification import calc_mod_masses_for_same_len_seqs\n", - "mod_masses = calc_mod_masses_for_same_len_seqs(\n", - " nAA=7,\n", - " mod_names_list=[\n", - " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any N-term', 'Oxidation@M'],\n", - " ['GlyGly@K', 'Dimethyl@K'],\n", - " ],\n", - " mod_sites_list=[\n", - " [0, 3],\n", - " [0, 1],\n", - " [3, 3],\n", - " ]\n", - ")\n", - "aa_masses = calc_AA_masses_for_same_len_seqs(\n", - " [\n", - " 'MACDEFG', 'MAKDEFG', 'MAKDEFR'\n", - " ]\n", - ")\n", - "mod_masses+aa_masses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### np.cumsum to get b-ion neutral masses" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 173.05104977, 244.08816356, 404.11881224, 519.14575526,\n", - " 648.18834835, 795.25676227, 852.27822599],\n", - " [ 189.04596439, 260.08307818, 388.17804119, 503.20498422,\n", - " 632.24757731, 779.31599122, 836.33745494],\n", - " [ 131.04048509, 202.07759887, 472.24678946, 587.27373248,\n", - " 716.31632557, 863.38473949, 1019.48585051]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "np.cumsum(aa_masses+mod_masses, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Mass functionalities in 'mass_calc'\n", - "\n", - "The functionalities for peptide and fragment neutral masses have been implement in `alphabase.peptide.mass_calc`:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 870.28879067, 854.34801962, 1037.49641519])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.peptide.mass_calc import calc_peptide_masses_for_same_len_seqs\n", - "\n", - "peptide_masses = calc_peptide_masses_for_same_len_seqs(\n", - " ['MACDEFG', 'MAKDEFG', 'MAKDEFR'],\n", - " mod_list=[\n", - " 'Acetyl@Any N-term;Carbamidomethyl@C',\n", - " 'Acetyl@Any N-term;Oxidation@M',\n", - " 'GlyGly@K;Dimethyl@K',\n", - " ],\n", - ")\n", - "peptide_masses" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 870.28879067, 854.34801962, 1037.49641519])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.peptide.mass_calc import calc_b_y_and_peptide_masses_for_same_len_seqs\n", - "b_masses, y_masses, peptide_masses = calc_b_y_and_peptide_masses_for_same_len_seqs(\n", - " ['MACDEFG', 'MAKDEFG', 'MAKDEFR'],\n", - " mod_list=[\n", - " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any N-term', 'Oxidation@M'],\n", - " ['GlyGly@K', 'Dimethyl@K'],\n", - " ],\n", - " site_list=[\n", - " [0, 3],\n", - " [0, 1],\n", - " [3, 3],\n", - " ],\n", - ")\n", - "peptide_masses" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[173.05104977, 244.08816356, 404.11881224, 519.14575526,\n", - " 648.18834835, 795.25676227],\n", - " [189.04596439, 260.08307818, 388.17804119, 503.20498422,\n", - " 632.24757731, 779.31599122],\n", - " [131.04048509, 202.07759887, 472.24678946, 587.27373248,\n", - " 716.31632557, 863.38473949]])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b_masses" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[697.2377409 , 626.20062711, 466.16997843, 351.14303541,\n", - " 222.10044232, 75.0320284 ],\n", - " [665.30205523, 594.26494145, 466.16997843, 351.14303541,\n", - " 222.10044232, 75.0320284 ],\n", - " [906.45593011, 835.41881632, 565.24962574, 450.22268271,\n", - " 321.18008962, 174.11167571]])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_masses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Isotope distribution\n", - "\n", - "`alphabase.constants.isotope.IsotopeDistribution` will calculate the isotope distribution and the mono-isotopic idx in the distribution for a given atom composition. \n", - "\n", - "What is the mono-isotopic idx (mono_idx)? For an atom, the `mono_idx` points to the highest abundance isotope, so the value is `round(mass of highest isotope - mass of first isotope)`." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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abundancemassmono_idx
13C[0.01, 0.99][12.0, 13.00335483507]1
14N[0.996337, 0.003663][14.00307400443, 15.00010889888]0
15N[0.01, 0.99][14.00307400443, 15.00010889888]1
18O[0.005, 0.005, 0.99][15.99491461957, 16.9991317565, 17.99915961286]2
2H[0.01, 0.99][1.00782503223, 2.01410177812]1
............
Xe[0.000952, 0.00089, 0.019102, 0.264006, 0.0407...[123.905892, 125.9042983, 127.903531, 128.9047...8
Y[1.0][88.9058403]0
Yb[0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0....[167.9338896, 169.9347664, 170.9363302, 171.93...6
Zn[0.4917, 0.2773, 0.0404, 0.1845, 0.0061][63.92914201, 65.92603381, 66.92712775, 67.924...0
Zr[0.5145, 0.1122, 0.1715, 0.1738, 0.028][89.9046977, 90.9056396, 91.9050347, 93.906310...0
\n", - "

109 rows × 3 columns

\n", - "
" - ], - "text/plain": [ - " abundance \\\n", - "13C [0.01, 0.99] \n", - "14N [0.996337, 0.003663] \n", - "15N [0.01, 0.99] \n", - "18O [0.005, 0.005, 0.99] \n", - "2H [0.01, 0.99] \n", - ".. ... \n", - "Xe [0.000952, 0.00089, 0.019102, 0.264006, 0.0407... \n", - "Y [1.0] \n", - "Yb [0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0.... \n", - "Zn [0.4917, 0.2773, 0.0404, 0.1845, 0.0061] \n", - "Zr [0.5145, 0.1122, 0.1715, 0.1738, 0.028] \n", - "\n", - " mass mono_idx \n", - "13C [12.0, 13.00335483507] 1 \n", - "14N [14.00307400443, 15.00010889888] 0 \n", - "15N [14.00307400443, 15.00010889888] 1 \n", - "18O [15.99491461957, 16.9991317565, 17.99915961286] 2 \n", - "2H [1.00782503223, 2.01410177812] 1 \n", - ".. ... ... \n", - "Xe [123.905892, 125.9042983, 127.903531, 128.9047... 8 \n", - "Y [88.9058403] 0 \n", - "Yb [167.9338896, 169.9347664, 170.9363302, 171.93... 6 \n", - "Zn [63.92914201, 65.92603381, 66.92712775, 67.924... 0 \n", - "Zr [89.9046977, 90.9056396, 91.9050347, 93.906310... 0 \n", - "\n", - "[109 rows x 3 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "from alphabase.constants.atom import CHEM_INFO_DICT\n", - "atom_df = pd.DataFrame().from_dict(CHEM_INFO_DICT, orient='index')\n", - "def get_mono(masses_abundances):\n", - " masses, abundances = masses_abundances\n", - " return round(masses[np.argmax(abundances)]-masses[0])\n", - "atom_df['mono_idx'] = atom_df[['mass','abundance']].apply(\n", - " get_mono, axis=1\n", - ")\n", - "atom_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`mono_idx` of an atom composition refers to the sum of the `mono_idx` of all atoms. In AlphaBase, `alphabase.constants.isotope.IsotopeDistribution` calculate both isotope abundance and `mono_idx`. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For example, `Fe`'s `mono_idx` is 2," - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "abundance [0.05845, 0.91754, 0.02119, 0.00282]\n", - "mass [53.93960899, 55.93493633, 56.93539284, 57.933...\n", - "mono_idx 2\n", - "Name: Fe, dtype: object" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "atom_df.loc['Fe']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So `C(1)Fe(1)`'s `mono_idx` is also 2:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([5.78245850e-02, 6.25415000e-04, 9.07722322e-01, 3.07809450e-02,\n", - " 3.01655900e-03, 3.01740000e-05, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00]),\n", - " 2)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.isotope import IsotopeDistribution, parse_formula\n", - "iso = IsotopeDistribution()\n", - "iso.calc_formula_distribution(\n", - " [('C',1),('Fe',1)]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But `13C(1)Fe(1)`'s `mono_idx` should be 3:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([5.845000e-04, 5.786550e-02, 9.175400e-03, 9.085765e-01,\n", - " 2.100630e-02, 2.791800e-03, 0.000000e+00, 0.000000e+00,\n", - " 0.000000e+00, 0.000000e+00]),\n", - " 3)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iso.calc_formula_distribution(\n", - " [('13C',1),('Fe',1)]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `mono_idx` for most of the atom compositions is 0, no matter how big the compositions are." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('C', 100), ('H', 100), ('O', 50), ('Na', 1)]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.isotope import IsotopeDistribution, parse_formula\n", - "iso = IsotopeDistribution()\n", - "\n", - "formula = 'C(100)H(100)O(50)Na(1)'\n", - "formula = parse_formula(formula)\n", - "formula" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> `mono` isotope is not the `highest` isotope!!!" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0,\n", - " 1,\n", - " array([2.98521241e-01, 3.31991573e-01, 2.13532938e-01, 1.00604878e-01,\n", - " 3.82856126e-02, 1.23872292e-02, 3.51773755e-03, 8.95830236e-04,\n", - " 2.07763024e-04, 4.43944472e-05]))" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dist, mono = iso.calc_formula_distribution(formula)\n", - "mono, dist.argmax(), dist" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All these low-level functionalities have been integrated into DataFrame functionalities, see `tutorial_dev_dataframes.ipynb` or `Tutorial for Dev: Peptide and Fragment DataFrames`" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.3 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8a3b27e141e49c996c9b863f8707e97aabd49c4a7e8445b9b783b34e4a21a9b2" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/nbs/tutorial_dev_dataframes.ipynb b/docs/nbs/tutorial_dev_dataframes.ipynb deleted file mode 100644 index 5699217f..00000000 --- a/docs/nbs/tutorial_dev_dataframes.ipynb +++ /dev/null @@ -1,1159 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial for Dev: Peptide and Fragment DataFrames\n", - "\n", - "This notebook introduces functionalities for peptide and fragment DataFrames to developers." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peptide DataFrame\n", - "\n", - "Peptide dataframe must contain four columns: `sequence` for animo acid sequence (str), `mods` for modification names (str), `mod_sites` for modification sites (str), and `charge` for precursor charge states (int).\n", - "\n", - "We can easily build a peptide dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sequencemodsmod_sitescharge
0ACDEFHIKCarbamidomethyl@C21
1APDEFMNIK2
2SWDEFMNTIRAAAAKDDDDRPhospho@S;Oxidation@M1;63
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" - ], - "text/plain": [ - " sequence mods mod_sites charge\n", - "0 ACDEFHIK Carbamidomethyl@C 2 1\n", - "1 APDEFMNIK 2\n", - "2 SWDEFMNTIRAAAAKDDDDR Phospho@S;Oxidation@M 1;6 3" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "df = pd.DataFrame({\n", - " 'sequence': ['ACDEFHIK', 'APDEFMNIK', 'SWDEFMNTIRAAAAKDDDDR'],\n", - " 'mods': ['Carbamidomethyl@C', '', 'Phospho@S;Oxidation@M'],\n", - " 'mod_sites': ['2', '', '1;6'],\n", - " 'charge': [1,2,3],\n", - "})\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate precursor_mz and isotopes from peptide dataframe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`alphabase.peptide.precursor.update_precursor_mz()` calculates the precursor_mz for peptides." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sequencemodsmod_siteschargenAAprecursor_mz
0ACDEFHIKCarbamidomethyl@C2181019.461492
1APDEFMNIK29532.757692
2SWDEFMNTIRAAAAKDDDDRPhospho@S;Oxidation@M1;6320808.337166
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" - ], - "text/plain": [ - " sequence mods mod_sites charge nAA \\\n", - "0 ACDEFHIK Carbamidomethyl@C 2 1 8 \n", - "1 APDEFMNIK 2 9 \n", - "2 SWDEFMNTIRAAAAKDDDDR Phospho@S;Oxidation@M 1;6 3 20 \n", - "\n", - " precursor_mz \n", - "0 1019.461492 \n", - "1 532.757692 \n", - "2 808.337166 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.peptide.precursor import update_precursor_mz\n", - "\n", - "update_precursor_mz(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`alphabase.peptide.precursor.calc_precursor_isotope()` calculates the precursor isotope information for peptides. It will add `i_*` columns for peptides." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sequencemodsmod_siteschargenAAprecursor_mzi_0i_1i_2i_3i_4i_5mono_isotope_idx
0ACDEFHIKCarbamidomethyl@C2181019.4614920.5448900.2942080.1169000.0343400.0080770.0015840
1APDEFMNIK29532.7576920.5278390.3008260.1230180.0373590.0091040.0018540
2SWDEFMNTIRAAAAKDDDDRPhospho@S;Oxidation@M1;6320808.3371660.2710280.3237750.2256410.1154410.0475530.0165610
\n", - "
" - ], - "text/plain": [ - " sequence mods mod_sites charge nAA \\\n", - "0 ACDEFHIK Carbamidomethyl@C 2 1 8 \n", - "1 APDEFMNIK 2 9 \n", - "2 SWDEFMNTIRAAAAKDDDDR Phospho@S;Oxidation@M 1;6 3 20 \n", - "\n", - " precursor_mz i_0 i_1 i_2 i_3 i_4 i_5 \\\n", - "0 1019.461492 0.544890 0.294208 0.116900 0.034340 0.008077 0.001584 \n", - "1 532.757692 0.527839 0.300826 0.123018 0.037359 0.009104 0.001854 \n", - "2 808.337166 0.271028 0.323775 0.225641 0.115441 0.047553 0.016561 \n", - "\n", - " mono_isotope_idx \n", - "0 0 \n", - "1 0 \n", - "2 0 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.peptide.precursor import calc_precursor_isotope\n", - "\n", - "calc_precursor_isotope(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Computing isotope patterns is very time-consuming for millions of peptides, so we provided `calc_precursor_isotope_mp` with multiprocessing for users." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fragment DataFrame\n", - "\n", - "`alphabase.peptide.fragment.create_fragment_mz_dataframe()` is the only function we need to calculate fragment_mz dataframe. It has two key parameters:\n", - "\n", - "- precursor_df (pd.DataFrame): the peptide or precursor dataframe.\n", - "- charged_frag_types (list of str): The charged fragments to be considered into the fragment dataframe columns. The schema is `Type[_LossType]_z[n]`, where \n", - " - `Type` can be `b,y,c,z`\n", - " - `_LossType` can be `_modloss,_H2O,_NH3`, this is optional.\n", - " - `z[n]` is the charge state. If precursor charge is less than `n`, the corresponding mz will be set as zero." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sequencemodsmod_siteschargenAAprecursor_mzfrag_start_idxfrag_stop_idx
0ACDEFHIKCarbamidomethyl@C2181019.46149207
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a_z1b_z1c_z1b_z2x_z1y_z1y_H2O_z1z_z1
044.04947772.04438889.0709380.0974.403625948.424377930.413818932.405640
1204.080124232.075043249.1015930.0814.372986788.393738770.383179772.375000
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" - ], - "text/plain": [ - " a_z1 b_z1 c_z1 b_z2 x_z1 y_z1 \\\n", - "0 44.049477 72.044388 89.070938 0.0 974.403625 948.424377 \n", - "1 204.080124 232.075043 249.101593 0.0 814.372986 788.393738 \n", - "2 319.107056 347.101990 364.128540 0.0 699.346069 673.366760 \n", - "3 448.149658 476.144562 493.171112 0.0 570.303467 544.324219 \n", - "4 595.218079 623.213013 640.239563 0.0 423.235046 397.255768 \n", - "5 732.276978 760.271912 777.298462 0.0 286.176147 260.196869 \n", - "6 845.361023 873.355957 890.382507 0.0 173.092072 147.112808 \n", - "\n", - " y_H2O_z1 z_z1 \n", - "0 930.413818 932.405640 \n", - "1 770.383179 772.375000 \n", - "2 655.356201 657.348083 \n", - "3 526.313660 528.305481 \n", - "4 379.245209 381.237061 \n", - "5 242.186310 244.178146 \n", - "6 129.102234 131.094086 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "start,stop = df[['frag_start_idx','frag_stop_idx']].values[0] #first peptide\n", - "frag_mz_df.iloc[start:stop]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that all N-term (a/b/c) fragment mz values are in ascending order, e.g. from b[1] to b[n-1]; and all C-term (x/y/z) fragments are in descending order, e.g. from y[n-1] to y[1]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All dataframe functionalities use low-level APIs of AlphaBase, see `tutorial_dev_basic_definations.ipynb` or `Tutorial for Dev: Basic Definations`. \n", - "\n", - "Spectral library functionalities provide higher-level APIs which encapsulate these dataframe functionalities, see `tutorial_dev_spectral_libraries.ipynb` or `Tutorial for Dev: Spectral Libraries`." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.3 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": 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AlphaBase calculates all mass values from atoms. And the masses of amino acids and modifications are calculated from their atom compositions, repectively. Eventually, the masses of peptides or precursors as well as their fragments can be calculated by the amino acid sequences with or without modifications (See figure below).\n", + "\n", + "Calculating masses from atoms makes it much easier to switch between unlabeled and heavy-labeled peptides, as we did in Steller MS for 15N-labeled peptides as the reference for targeted proteomics (https://www.biorxiv.org/content/10.1101/2024.06.02.597029v2.full).\n", + "\n", + "The other advantage of starting from atoms is that AlphaBase can calculate isotope distributions of peptides based on a pre-defined isotope distribution list of atoms (e.g., NIST atom table in https://physics.nist.gov/cgi-bin/Compositions/stand_alone.pl). The isotope information has been applied in our AlphaDIA search engine to boost the identification of DIA-MS data (https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='atom-to-peptides.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Atoms/Elements\n", + "\n", + "The masses of all amino acids and modifications are calculated from their atom compositions.\n", + "\n", + "The atom information are defined in https://github.com/MannLabs/alphabase/blob/main/alphabase/constants/const_files/nist_element.yaml which is parsed from NIST, see https://github.com/MannLabs/alphabase/blob/main/scripts/nist_chem_to_yaml.ipynb.\n", + "\n", + "After adding some heavy isotopes, including 13C, 15N, 2H, and 18O, we obtain 109 kinds of atoms:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    abundancemass
    13C[0.01, 0.99][12.0, 13.00335483507]
    14N[0.996337, 0.003663][14.00307400443, 15.00010889888]
    15N[0.01, 0.99][14.00307400443, 15.00010889888]
    18O[0.005, 0.005, 0.99][15.99491461957, 16.9991317565, 17.99915961286]
    2H[0.01, 0.99][1.00782503223, 2.01410177812]
    .........
    Xe[0.000952, 0.00089, 0.019102, 0.264006, 0.0407...[123.905892, 125.9042983, 127.903531, 128.9047...
    Y[1.0][88.9058403]
    Yb[0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0....[167.9338896, 169.9347664, 170.9363302, 171.93...
    Zn[0.4917, 0.2773, 0.0404, 0.1845, 0.0061][63.92914201, 65.92603381, 66.92712775, 67.924...
    Zr[0.5145, 0.1122, 0.1715, 0.1738, 0.028][89.9046977, 90.9056396, 91.9050347, 93.906310...
    \n", + "

    109 rows × 2 columns

    \n", + "
    " + ], + "text/plain": [ + " abundance \\\n", + "13C [0.01, 0.99] \n", + "14N [0.996337, 0.003663] \n", + "15N [0.01, 0.99] \n", + "18O [0.005, 0.005, 0.99] \n", + "2H [0.01, 0.99] \n", + ".. ... \n", + "Xe [0.000952, 0.00089, 0.019102, 0.264006, 0.0407... \n", + "Y [1.0] \n", + "Yb [0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0.... \n", + "Zn [0.4917, 0.2773, 0.0404, 0.1845, 0.0061] \n", + "Zr [0.5145, 0.1122, 0.1715, 0.1738, 0.028] \n", + "\n", + " mass \n", + "13C [12.0, 13.00335483507] \n", + "14N [14.00307400443, 15.00010889888] \n", + "15N [14.00307400443, 15.00010889888] \n", + "18O [15.99491461957, 16.9991317565, 17.99915961286] \n", + "2H [1.00782503223, 2.01410177812] \n", + ".. ... \n", + "Xe [123.905892, 125.9042983, 127.903531, 128.9047... \n", + "Y [88.9058403] \n", + "Yb [167.9338896, 169.9347664, 170.9363302, 171.93... \n", + "Zn [63.92914201, 65.92603381, 66.92712775, 67.924... \n", + "Zr [89.9046977, 90.9056396, 91.9050347, 93.906310... \n", + "\n", + "[109 rows x 2 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from alphabase.constants.atom import CHEM_INFO_DICT\n", + "pd.DataFrame().from_dict(CHEM_INFO_DICT, orient='index')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And their mono-isotopic mass are in `CHEM_MONO_MASS` (dict):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    0
    13C13.003355
    14N14.003074
    15N15.000109
    18O17.999160
    2H2.014102
    ......
    Xe131.904155
    Y88.905840
    Yb173.938866
    Zn63.929142
    Zr89.904698
    \n", + "

    109 rows × 1 columns

    \n", + "
    " + ], + "text/plain": [ + " 0\n", + "13C 13.003355\n", + "14N 14.003074\n", + "15N 15.000109\n", + "18O 17.999160\n", + "2H 2.014102\n", + ".. ...\n", + "Xe 131.904155\n", + "Y 88.905840\n", + "Yb 173.938866\n", + "Zn 63.929142\n", + "Zr 89.904698\n", + "\n", + "[109 rows x 1 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.atom import CHEM_MONO_MASS\n", + "pd.DataFrame().from_dict(CHEM_MONO_MASS, orient='index')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These atom masses are used to calculate the masses of amino acids, modifications, and then subsequent masses of peptides and fragments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Commonly used molecular masses" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.007276467, 1.0033, 17.02654910112, 18.01056468403)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.atom import (\n", + " MASS_PROTON, MASS_ISOTOPE, MASS_NH3, MASS_H2O\n", + ")\n", + "MASS_PROTON, MASS_ISOTOPE, MASS_NH3, MASS_H2O" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Amino Acids" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    aaformulamass
    65AC(3)H(5)N(1)O(1)S(0)7.103711e+01
    66BC(1000000)1.200000e+07
    67CC(3)H(5)N(1)O(1)S(1)1.030092e+02
    68DC(4)H(5)N(1)O(3)S(0)1.150269e+02
    69EC(5)H(7)N(1)O(3)S(0)1.290426e+02
    70FC(9)H(9)N(1)O(1)S(0)1.470684e+02
    71GC(2)H(3)N(1)O(1)S(0)5.702146e+01
    72HC(6)H(7)N(3)O(1)S(0)1.370589e+02
    73IC(6)H(11)N(1)O(1)S(0)1.130841e+02
    74JC(6)H(11)N(1)O(1)S(0)1.130841e+02
    75KC(6)H(12)N(2)O(1)S(0)1.280950e+02
    76LC(6)H(11)N(1)O(1)S(0)1.130841e+02
    77MC(5)H(9)N(1)O(1)S(1)1.310405e+02
    78NC(4)H(6)N(2)O(2)S(0)1.140429e+02
    79OC(12)H(19)N(3)O(2)2.371477e+02
    80PC(5)H(7)N(1)O(1)S(0)9.705276e+01
    81QC(5)H(8)N(2)O(2)S(0)1.280586e+02
    82RC(6)H(12)N(4)O(1)S(0)1.561011e+02
    83SC(3)H(5)N(1)O(2)S(0)8.703203e+01
    84TC(4)H(7)N(1)O(2)S(0)1.010477e+02
    85UC(3)H(5)N(1)O(1)Se(1)1.509536e+02
    86VC(5)H(9)N(1)O(1)S(0)9.906841e+01
    87WC(11)H(10)N(2)O(1)S(0)1.860793e+02
    88XC(1000000)1.200000e+07
    89YC(9)H(9)N(1)O(2)S(0)1.630633e+02
    90ZC(1000000)1.200000e+07
    \n", + "
    " + ], + "text/plain": [ + " aa formula mass\n", + "65 A C(3)H(5)N(1)O(1)S(0) 7.103711e+01\n", + "66 B C(1000000) 1.200000e+07\n", + "67 C C(3)H(5)N(1)O(1)S(1) 1.030092e+02\n", + "68 D C(4)H(5)N(1)O(3)S(0) 1.150269e+02\n", + "69 E C(5)H(7)N(1)O(3)S(0) 1.290426e+02\n", + "70 F C(9)H(9)N(1)O(1)S(0) 1.470684e+02\n", + "71 G C(2)H(3)N(1)O(1)S(0) 5.702146e+01\n", + "72 H C(6)H(7)N(3)O(1)S(0) 1.370589e+02\n", + "73 I C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", + "74 J C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", + "75 K C(6)H(12)N(2)O(1)S(0) 1.280950e+02\n", + "76 L C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", + "77 M C(5)H(9)N(1)O(1)S(1) 1.310405e+02\n", + "78 N C(4)H(6)N(2)O(2)S(0) 1.140429e+02\n", + "79 O C(12)H(19)N(3)O(2) 2.371477e+02\n", + "80 P C(5)H(7)N(1)O(1)S(0) 9.705276e+01\n", + "81 Q C(5)H(8)N(2)O(2)S(0) 1.280586e+02\n", + "82 R C(6)H(12)N(4)O(1)S(0) 1.561011e+02\n", + "83 S C(3)H(5)N(1)O(2)S(0) 8.703203e+01\n", + "84 T C(4)H(7)N(1)O(2)S(0) 1.010477e+02\n", + "85 U C(3)H(5)N(1)O(1)Se(1) 1.509536e+02\n", + "86 V C(5)H(9)N(1)O(1)S(0) 9.906841e+01\n", + "87 W C(11)H(10)N(2)O(1)S(0) 1.860793e+02\n", + "88 X C(1000000) 1.200000e+07\n", + "89 Y C(9)H(9)N(1)O(2)S(0) 1.630633e+02\n", + "90 Z C(1000000) 1.200000e+07" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.aa import AA_DF\n", + "AA_DF.loc[ord('A'):ord('Z')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In `AA_DF`, amino acids are encoded by ASCII (128 characters), thus 65==ord('A'), ..., 90==ord('Z'). Unicode strings can be fastly converted to ascii int32 values using `np.array.view()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([65, 66, 67, 88, 89, 90], dtype=int32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "np.array(['ABCXYZ']).view(np.int32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But users does not need to know this, as we provided easy to use functionalities to get residue masses from sequences." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate AA masses in batch" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[131.04048509, 71.03711379, 103.00918496, 115.02694302,\n", + " 129.04259309, 147.06841391, 57.02146372],\n", + " [131.04048509, 71.03711379, 128.09496302, 115.02694302,\n", + " 129.04259309, 147.06841391, 57.02146372],\n", + " [131.04048509, 71.03711379, 128.09496302, 115.02694302,\n", + " 129.04259309, 147.06841391, 156.10111102]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.aa import calc_AA_masses_for_same_len_seqs\n", + "calc_AA_masses_for_same_len_seqs(\n", + " [\n", + " 'MACDEFG', 'MAKDEFG', 'MAKDEFR'\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modifications\n", + "\n", + "In AlphaBase, we used `mod_name@aa` to represent a modification, the `mod_name` is from UniMod. We also used `mod_name@Protein N-term`, `mod_name@Any N-term` and `mod_name@Any C-term` for terminal modifications, which follow the UniMod terminal name schema.\n", + "\n", + "The default modification TSV is stored in `alphabase/constants/const_files/modification.tsv`, users can add more modifications into the tsv file (only `mod_name` and `composition` colums are required). Please https://github.com/MannLabs/alphabase/blob/main/alphabase/constants/const_files/modification.tsv." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    mod_nameunimod_massunimod_avge_masscompositionunimod_modlossmodloss_compositionclassificationunimod_idmodloss_importancemassmodloss_originalmodloss
    mod_name
    Acetyl@TAcetyl@T42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
    Acetyl@Protein N-termAcetyl@Protein N-term42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
    Acetyl@SAcetyl@S42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
    Acetyl@CAcetyl@C42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
    Acetyl@Any N-termAcetyl@Any N-term42.01056542.0367H(2)C(2)O(1)0.0Multiple10.042.0105650.00.0
    .......................................
    TMTpro_zero@KTMTpro_zero@K295.189592295.3773H(25)C(15)N(3)O(3)0.0Chemical derivative20170.0295.1895920.00.0
    TMTpro_zero@TTMTpro_zero@T295.189592295.3773H(25)C(15)N(3)O(3)0.0Chemical derivative20170.0295.1895920.00.0
    Andro-H2O@CAndro-H2O@C332.198760332.4339H(28)C(20)O(4)0.0Chemical derivative20250.0332.1987590.00.0
    His+O(2)@HHis+O(2)@H169.048741169.1381H(7)C(6)N(3)O(3)0.0Post-translational20270.0169.0487410.00.0
    GlyGly@KGlyGly@K114.042927114.1026H(6)C(4)N(2)O(2)0.0Post-translational1211000000.0114.0429270.00.0
    \n", + "

    2685 rows × 12 columns

    \n", + "
    " + ], + "text/plain": [ + " mod_name unimod_mass unimod_avge_mass \\\n", + "mod_name \n", + "Acetyl@T Acetyl@T 42.010565 42.0367 \n", + "Acetyl@Protein N-term Acetyl@Protein N-term 42.010565 42.0367 \n", + "Acetyl@S Acetyl@S 42.010565 42.0367 \n", + "Acetyl@C Acetyl@C 42.010565 42.0367 \n", + "Acetyl@Any N-term Acetyl@Any N-term 42.010565 42.0367 \n", + "... ... ... ... \n", + "TMTpro_zero@K TMTpro_zero@K 295.189592 295.3773 \n", + "TMTpro_zero@T TMTpro_zero@T 295.189592 295.3773 \n", + "Andro-H2O@C Andro-H2O@C 332.198760 332.4339 \n", + "His+O(2)@H His+O(2)@H 169.048741 169.1381 \n", + "GlyGly@K GlyGly@K 114.042927 114.1026 \n", + "\n", + " composition unimod_modloss modloss_composition \\\n", + "mod_name \n", + "Acetyl@T H(2)C(2)O(1) 0.0 \n", + "Acetyl@Protein N-term H(2)C(2)O(1) 0.0 \n", + "Acetyl@S H(2)C(2)O(1) 0.0 \n", + "Acetyl@C H(2)C(2)O(1) 0.0 \n", + "Acetyl@Any N-term H(2)C(2)O(1) 0.0 \n", + "... ... ... ... \n", + "TMTpro_zero@K H(25)C(15)N(3)O(3) 0.0 \n", + "TMTpro_zero@T H(25)C(15)N(3)O(3) 0.0 \n", + "Andro-H2O@C H(28)C(20)O(4) 0.0 \n", + "His+O(2)@H H(7)C(6)N(3)O(3) 0.0 \n", + "GlyGly@K H(6)C(4)N(2)O(2) 0.0 \n", + "\n", + " classification unimod_id modloss_importance \\\n", + "mod_name \n", + "Acetyl@T Post-translational 1 0.0 \n", + "Acetyl@Protein N-term Post-translational 1 0.0 \n", + "Acetyl@S Post-translational 1 0.0 \n", + "Acetyl@C Post-translational 1 0.0 \n", + "Acetyl@Any N-term Multiple 1 0.0 \n", + "... ... ... ... \n", + "TMTpro_zero@K Chemical derivative 2017 0.0 \n", + "TMTpro_zero@T Chemical derivative 2017 0.0 \n", + "Andro-H2O@C Chemical derivative 2025 0.0 \n", + "His+O(2)@H Post-translational 2027 0.0 \n", + "GlyGly@K Post-translational 121 1000000.0 \n", + "\n", + " mass modloss_original modloss \n", + "mod_name \n", + "Acetyl@T 42.010565 0.0 0.0 \n", + "Acetyl@Protein N-term 42.010565 0.0 0.0 \n", + "Acetyl@S 42.010565 0.0 0.0 \n", + "Acetyl@C 42.010565 0.0 0.0 \n", + "Acetyl@Any N-term 42.010565 0.0 0.0 \n", + "... ... ... ... \n", + "TMTpro_zero@K 295.189592 0.0 0.0 \n", + "TMTpro_zero@T 295.189592 0.0 0.0 \n", + "Andro-H2O@C 332.198759 0.0 0.0 \n", + "His+O(2)@H 169.048741 0.0 0.0 \n", + "GlyGly@K 114.042927 0.0 0.0 \n", + "\n", + "[2685 rows x 12 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.modification import MOD_DF\n", + "MOD_DF" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Modification sites\n", + "\n", + "In alphabase, we use 0 and -1 to represent modification site of N-term and C-term, respectively. For other modification sites, we use 1 to n." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([42.01056468, 0. , 57.02146372, 0. , 0. ,\n", + " 0. , 0. ])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.modification import calc_modification_mass\n", + "sequence = 'MACDEFG'\n", + "mod_names = ['Acetyl@Any N-term', 'Carbamidomethyl@C']\n", + "mod_sites = [0,3]\n", + "calc_modification_mass(\n", + " nAA=len(sequence),\n", + " mod_names=mod_names,\n", + " mod_sites=mod_sites\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The modifications on the first amino acid and N-term will be added." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([58.0054793, 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequence = 'MAKDEFG'\n", + "mod_names = ['Acetyl@Any N-term', 'Oxidation@M']\n", + "mod_sites = [0,1]\n", + "calc_modification_mass(\n", + " nAA=len(sequence),\n", + " mod_names=mod_names,\n", + " mod_sites=mod_sites\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Multiple modification at a single site is supported, for example, in the following example, `K3` contains both `GlyGly@K` and `Dimethyl@K`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0. , 142.07422757, 0. ,\n", + " 0. , 0. , 0. ])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequence = 'MAKDEFR'\n", + "mod_names = ['GlyGly@K', 'Dimethyl@K']\n", + "mod_sites = [3,3]\n", + "calc_modification_mass(\n", + " nAA=len(sequence),\n", + " mod_names=mod_names,\n", + " mod_sites=mod_sites\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Caculate modification masses in batch" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 42.01056468, 0. , 57.02146372, 0. ,\n", + " 0. , 0. , 0. ],\n", + " [ 58.0054793 , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. ],\n", + " [ 0. , 0. , 142.07422757, 0. ,\n", + " 0. , 0. , 0. ]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.modification import calc_mod_masses_for_same_len_seqs\n", + "calc_mod_masses_for_same_len_seqs(\n", + " nAA=7,\n", + " mod_names_list=[\n", + " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", + " ['Acetyl@Any N-term', 'Oxidation@M'],\n", + " ['GlyGly@K', 'Dimethyl@K'],\n", + " ],\n", + " mod_sites_list=[\n", + " [0, 3],\n", + " [0, 1],\n", + " [3, 3],\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mass calculation functionalities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate AA and modification masses in batch" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[173.05104977, 71.03711379, 160.03064868, 115.02694302,\n", + " 129.04259309, 147.06841391, 57.02146372],\n", + " [189.04596439, 71.03711379, 128.09496302, 115.02694302,\n", + " 129.04259309, 147.06841391, 57.02146372],\n", + " [131.04048509, 71.03711379, 270.16919059, 115.02694302,\n", + " 129.04259309, 147.06841391, 156.10111102]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.aa import calc_AA_masses_for_same_len_seqs\n", + "from alphabase.constants.modification import calc_mod_masses_for_same_len_seqs\n", + "mod_masses = calc_mod_masses_for_same_len_seqs(\n", + " nAA=7,\n", + " mod_names_list=[\n", + " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", + " ['Acetyl@Any N-term', 'Oxidation@M'],\n", + " ['GlyGly@K', 'Dimethyl@K'],\n", + " ],\n", + " mod_sites_list=[\n", + " [0, 3],\n", + " [0, 1],\n", + " [3, 3],\n", + " ]\n", + ")\n", + "aa_masses = calc_AA_masses_for_same_len_seqs(\n", + " [\n", + " 'MACDEFG', 'MAKDEFG', 'MAKDEFR'\n", + " ]\n", + ")\n", + "mod_masses+aa_masses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### np.cumsum to get b-ion neutral masses" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 173.05104977, 244.08816356, 404.11881224, 519.14575526,\n", + " 648.18834835, 795.25676227, 852.27822599],\n", + " [ 189.04596439, 260.08307818, 388.17804119, 503.20498422,\n", + " 632.24757731, 779.31599122, 836.33745494],\n", + " [ 131.04048509, 202.07759887, 472.24678946, 587.27373248,\n", + " 716.31632557, 863.38473949, 1019.48585051]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.cumsum(aa_masses+mod_masses, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Mass functionalities in 'mass_calc'\n", + "\n", + "The functionalities for peptide and fragment neutral masses have been implement in `alphabase.peptide.mass_calc`:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 870.28879067, 854.34801962, 1037.49641519])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.peptide.mass_calc import calc_peptide_masses_for_same_len_seqs\n", + "\n", + "peptide_masses = calc_peptide_masses_for_same_len_seqs(\n", + " ['MACDEFG', 'MAKDEFG', 'MAKDEFR'],\n", + " mod_list=[\n", + " 'Acetyl@Any N-term;Carbamidomethyl@C',\n", + " 'Acetyl@Any N-term;Oxidation@M',\n", + " 'GlyGly@K;Dimethyl@K',\n", + " ],\n", + ")\n", + "peptide_masses" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 870.28879067, 854.34801962, 1037.49641519])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.peptide.mass_calc import calc_b_y_and_peptide_masses_for_same_len_seqs\n", + "b_masses, y_masses, peptide_masses = calc_b_y_and_peptide_masses_for_same_len_seqs(\n", + " ['MACDEFG', 'MAKDEFG', 'MAKDEFR'],\n", + " mod_list=[\n", + " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", + " ['Acetyl@Any N-term', 'Oxidation@M'],\n", + " ['GlyGly@K', 'Dimethyl@K'],\n", + " ],\n", + " site_list=[\n", + " [0, 3],\n", + " [0, 1],\n", + " [3, 3],\n", + " ],\n", + ")\n", + "peptide_masses" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[173.05104977, 244.08816356, 404.11881224, 519.14575526,\n", + " 648.18834835, 795.25676227],\n", + " [189.04596439, 260.08307818, 388.17804119, 503.20498422,\n", + " 632.24757731, 779.31599122],\n", + " [131.04048509, 202.07759887, 472.24678946, 587.27373248,\n", + " 716.31632557, 863.38473949]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b_masses" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[697.2377409 , 626.20062711, 466.16997843, 351.14303541,\n", + " 222.10044232, 75.0320284 ],\n", + " [665.30205523, 594.26494145, 466.16997843, 351.14303541,\n", + " 222.10044232, 75.0320284 ],\n", + " [906.45593011, 835.41881632, 565.24962574, 450.22268271,\n", + " 321.18008962, 174.11167571]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_masses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Isotope distribution\n", + "\n", + "`alphabase.constants.isotope.IsotopeDistribution` will calculate the isotope distribution and the mono-isotopic idx in the distribution for a given atom composition. \n", + "\n", + "What is the mono-isotopic idx (mono_idx)? For an atom, the `mono_idx` points to the highest abundance isotope, so the value is `round(mass of highest isotope - mass of first isotope)`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    abundancemassmono_idx
    13C[0.01, 0.99][12.0, 13.00335483507]1
    14N[0.996337, 0.003663][14.00307400443, 15.00010889888]0
    15N[0.01, 0.99][14.00307400443, 15.00010889888]1
    18O[0.005, 0.005, 0.99][15.99491461957, 16.9991317565, 17.99915961286]2
    2H[0.01, 0.99][1.00782503223, 2.01410177812]1
    ............
    Xe[0.000952, 0.00089, 0.019102, 0.264006, 0.0407...[123.905892, 125.9042983, 127.903531, 128.9047...8
    Y[1.0][88.9058403]0
    Yb[0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0....[167.9338896, 169.9347664, 170.9363302, 171.93...6
    Zn[0.4917, 0.2773, 0.0404, 0.1845, 0.0061][63.92914201, 65.92603381, 66.92712775, 67.924...0
    Zr[0.5145, 0.1122, 0.1715, 0.1738, 0.028][89.9046977, 90.9056396, 91.9050347, 93.906310...0
    \n", + "

    109 rows × 3 columns

    \n", + "
    " + ], + "text/plain": [ + " abundance \\\n", + "13C [0.01, 0.99] \n", + "14N [0.996337, 0.003663] \n", + "15N [0.01, 0.99] \n", + "18O [0.005, 0.005, 0.99] \n", + "2H [0.01, 0.99] \n", + ".. ... \n", + "Xe [0.000952, 0.00089, 0.019102, 0.264006, 0.0407... \n", + "Y [1.0] \n", + "Yb [0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0.... \n", + "Zn [0.4917, 0.2773, 0.0404, 0.1845, 0.0061] \n", + "Zr [0.5145, 0.1122, 0.1715, 0.1738, 0.028] \n", + "\n", + " mass mono_idx \n", + "13C [12.0, 13.00335483507] 1 \n", + "14N [14.00307400443, 15.00010889888] 0 \n", + "15N [14.00307400443, 15.00010889888] 1 \n", + "18O [15.99491461957, 16.9991317565, 17.99915961286] 2 \n", + "2H [1.00782503223, 2.01410177812] 1 \n", + ".. ... ... \n", + "Xe [123.905892, 125.9042983, 127.903531, 128.9047... 8 \n", + "Y [88.9058403] 0 \n", + "Yb [167.9338896, 169.9347664, 170.9363302, 171.93... 6 \n", + "Zn [63.92914201, 65.92603381, 66.92712775, 67.924... 0 \n", + "Zr [89.9046977, 90.9056396, 91.9050347, 93.906310... 0 \n", + "\n", + "[109 rows x 3 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from alphabase.constants.atom import CHEM_INFO_DICT\n", + "atom_df = pd.DataFrame().from_dict(CHEM_INFO_DICT, orient='index')\n", + "def get_mono(masses_abundances):\n", + " masses, abundances = masses_abundances\n", + " return round(masses[np.argmax(abundances)]-masses[0])\n", + "atom_df['mono_idx'] = atom_df[['mass','abundance']].apply(\n", + " get_mono, axis=1\n", + ")\n", + "atom_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`mono_idx` of an atom composition refers to the sum of the `mono_idx` of all atoms. In AlphaBase, `alphabase.constants.isotope.IsotopeDistribution` calculate both isotope abundance and `mono_idx`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, `Fe`'s `mono_idx` is 2," + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "abundance [0.05845, 0.91754, 0.02119, 0.00282]\n", + "mass [53.93960899, 55.93493633, 56.93539284, 57.933...\n", + "mono_idx 2\n", + "Name: Fe, dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "atom_df.loc['Fe']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So `C(1)Fe(1)`'s `mono_idx` is also 2:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([5.78245850e-02, 6.25415000e-04, 9.07722322e-01, 3.07809450e-02,\n", + " 3.01655900e-03, 3.01740000e-05, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00]),\n", + " 2)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.isotope import IsotopeDistribution, parse_formula\n", + "iso = IsotopeDistribution()\n", + "iso.calc_formula_distribution(\n", + " [('C',1),('Fe',1)]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But `13C(1)Fe(1)`'s `mono_idx` should be 3:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([5.845000e-04, 5.786550e-02, 9.175400e-03, 9.085765e-01,\n", + " 2.100630e-02, 2.791800e-03, 0.000000e+00, 0.000000e+00,\n", + " 0.000000e+00, 0.000000e+00]),\n", + " 3)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso.calc_formula_distribution(\n", + " [('13C',1),('Fe',1)]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `mono_idx` for most of the atom compositions is 0, no matter how big the compositions are." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('C', 100), ('H', 100), ('O', 50), ('Na', 1)]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.constants.isotope import IsotopeDistribution, parse_formula\n", + "iso = IsotopeDistribution()\n", + "\n", + "formula = 'C(100)H(100)O(50)Na(1)'\n", + "formula = parse_formula(formula)\n", + "formula" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> `mono` isotope is not the `highest` isotope!!!" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0,\n", + " 1,\n", + " array([2.98521241e-01, 3.31991573e-01, 2.13532938e-01, 1.00604878e-01,\n", + " 3.82856126e-02, 1.23872292e-02, 3.51773755e-03, 8.95830236e-04,\n", + " 2.07763024e-04, 4.43944472e-05]))" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dist, mono = iso.calc_formula_distribution(formula)\n", + "mono, dist.argmax(), dist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All these low-level functionalities have been integrated into DataFrame functionalities, see `tutorial_dev_dataframes.ipynb` or `Tutorial for Dev: Peptide and Fragment DataFrames`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.3 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8a3b27e141e49c996c9b863f8707e97aabd49c4a7e8445b9b783b34e4a21a9b2" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/dataframe_structures.ipynb b/docs/tutorials/dataframe_structures.ipynb new file mode 100644 index 00000000..f95a50aa --- /dev/null +++ b/docs/tutorials/dataframe_structures.ipynb @@ -0,0 +1,1186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Peptide and Fragment DataFrames\n", + "\n", + "We use dataframe, a tabular-like data structure. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='peptide-fragment-dataframe.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Peptide DataFrame\n", + "\n", + "Peptide dataframe must contain four columns: \n", + " - `sequence` for animo acid sequence (str);\n", + " - `mods` for modification names (str, separated by `;`);\n", + " - `mod_sites` for modification sites (str, separated by `;`);\n", + " - `charge` for precursor charge states (int).\n", + "\n", + "Other columns like `precursor_mz` can be flexibly added into the dataframe if necessary; and AlphaBase provides functionalities to calculate other columns like `precursor_mz` and isotopes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    sequencemodsmod_sitescharge
    0ACDEFHIKCarbamidomethyl@C21
    1APDEFMNIK2
    2WDSEFMNTIRAAAAKDDDDRPhospho@S;Oxidation@M3;63
    \n", + "
    " + ], + "text/plain": [ + " sequence mods mod_sites charge\n", + "0 ACDEFHIK Carbamidomethyl@C 2 1\n", + "1 APDEFMNIK 2\n", + "2 WDSEFMNTIRAAAAKDDDDR Phospho@S;Oxidation@M 3;6 3" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.DataFrame({\n", + " 'sequence': ['ACDEFHIK', 'APDEFMNIK', 'WDSEFMNTIRAAAAKDDDDR'],\n", + " 'mods': ['Carbamidomethyl@C', '', 'Phospho@S;Oxidation@M'],\n", + " 'mod_sites': ['2', '', '3;6'],\n", + " 'charge': [1,2,3],\n", + "})\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fragment DataFrame\n", + "\n", + "Fragment is also orginized in dataframe structure. The column names of the dataframe represent the fragment type, wich schema `Type[_LossType]_z[n]`, where:\n", + " - `Type` can be `b,y,c,z`\n", + " - `_LossType` can be `_modloss`, `_H2O`, or `_NH3`, this is optional.\n", + " - `z[n]` is the charge state. If precursor charge is less than `n`.\n", + "\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, + { + "data": { + "text/html": [ + "
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    a_z1b_z1c_z1b_z2x_z1y_z1y_H2O_z1y_modloss_z1z_z1
    044.04947772.04438889.0709380.000000974.403625948.424377930.4138180.000000932.405640
    1204.080124232.075043249.1015930.000000814.372986788.393738770.3831790.000000772.375000
    2319.107056347.101990364.1285400.000000699.346069673.366760655.3562010.000000657.348083
    3448.149658476.144562493.1711120.000000570.303467544.324219526.3136600.000000528.305481
    4595.218079623.213013640.2395630.000000423.235046397.255768379.2452090.000000381.237061
    5732.276978760.271912777.2984620.000000286.176147260.196869242.1863100.000000244.178146
    6845.361023873.355957890.3825070.000000173.092072147.112808129.1022340.000000131.094086
    744.04947772.04438889.07093836.5258331019.450256993.471008975.4604490.000000977.452271
    8141.102234169.097153186.12370385.052216922.397522896.418213878.4076540.000000880.399536
    9256.129181284.124084301.150635142.565689807.370544781.391296763.3807370.000000765.372559
    10385.171783413.166687430.193237207.086990678.327942652.348694634.3381350.000000636.329956
    11532.240173560.235107577.261658280.621185531.259521505.280273487.2697140.000000489.261566
    12663.280701691.275574708.302124346.141418400.219055374.239807356.2292180.000000358.221069
    13777.323608805.318542822.345093403.162903286.176147260.196869242.1863100.000000244.178146
    14890.407654918.402588935.429138459.704926173.092072147.112808129.1022340.000000131.094086
    15159.091675187.086594204.11314494.0469362262.8969732236.9177252218.9069822138.9406742220.898926
    16274.118622302.113525319.140076151.5604102147.8698732121.8906252103.8801272023.9138182105.872070
    17441.116974469.111877486.138428235.0595861980.8715821954.8923341936.8817140.0000001938.873657
    18570.159546598.154480615.181030299.5808721851.8289791825.8497311807.8391110.0000001809.831055
    19717.227966745.222900762.249451373.1150821704.7606201678.7813721660.7707520.0000001662.762573
    20864.263367892.258301909.284851446.6327821557.7252201531.7459721513.7353520.0000001515.727173
    21978.3063351006.3012081023.327759503.6542661443.6822511417.7030031399.6923830.0000001401.684326
    221079.3540041107.3488771124.375488554.1781011342.6345211316.6552731298.6447750.0000001300.636597
    231192.4381101220.4329831237.459473610.7200931229.5505371203.5712891185.5606690.0000001187.552490
    241348.5391851376.5340581393.560669688.7706911073.4494631047.4700931029.4595950.0000001031.451416
    251419.5762941447.5711671464.597778724.2892461002.412292976.433044958.4224850.000000960.414307
    261490.6134031518.6082761535.634888759.807800931.375183905.395935887.3853760.000000889.377197
    271561.6505131589.6453861606.671997795.326355860.338074834.358826816.3482670.000000818.340088
    281632.6876221660.6824951677.709106830.844910789.300964763.321716745.3110960.000000747.302979
    291760.7825931788.7774661805.804077894.892395661.205994635.226746617.2161870.000000619.208008
    301875.8095701903.8044431920.830933952.405884546.179016520.199768502.1892090.000000504.181061
    311990.8364262018.8314212035.8579101009.919312431.152100405.172852387.1622620.000000389.154114
    322105.8635252133.8583982150.8847661067.432861316.125153290.145905272.1353450.000000274.127167
    332220.8903812248.8852542265.9118651124.946289201.098221175.118958157.1083830.000000159.100235
    \n", + "
    " + ], + "text/plain": [ + " a_z1 b_z1 c_z1 b_z2 x_z1 \\\n", + "0 44.049477 72.044388 89.070938 0.000000 974.403625 \n", + "1 204.080124 232.075043 249.101593 0.000000 814.372986 \n", + "2 319.107056 347.101990 364.128540 0.000000 699.346069 \n", + "3 448.149658 476.144562 493.171112 0.000000 570.303467 \n", + "4 595.218079 623.213013 640.239563 0.000000 423.235046 \n", + "5 732.276978 760.271912 777.298462 0.000000 286.176147 \n", + "6 845.361023 873.355957 890.382507 0.000000 173.092072 \n", + "7 44.049477 72.044388 89.070938 36.525833 1019.450256 \n", + "8 141.102234 169.097153 186.123703 85.052216 922.397522 \n", + "9 256.129181 284.124084 301.150635 142.565689 807.370544 \n", + "10 385.171783 413.166687 430.193237 207.086990 678.327942 \n", + "11 532.240173 560.235107 577.261658 280.621185 531.259521 \n", + "12 663.280701 691.275574 708.302124 346.141418 400.219055 \n", + "13 777.323608 805.318542 822.345093 403.162903 286.176147 \n", + "14 890.407654 918.402588 935.429138 459.704926 173.092072 \n", + "15 159.091675 187.086594 204.113144 94.046936 2262.896973 \n", + "16 274.118622 302.113525 319.140076 151.560410 2147.869873 \n", + "17 441.116974 469.111877 486.138428 235.059586 1980.871582 \n", + "18 570.159546 598.154480 615.181030 299.580872 1851.828979 \n", + "19 717.227966 745.222900 762.249451 373.115082 1704.760620 \n", + "20 864.263367 892.258301 909.284851 446.632782 1557.725220 \n", + "21 978.306335 1006.301208 1023.327759 503.654266 1443.682251 \n", + "22 1079.354004 1107.348877 1124.375488 554.178101 1342.634521 \n", + "23 1192.438110 1220.432983 1237.459473 610.720093 1229.550537 \n", + "24 1348.539185 1376.534058 1393.560669 688.770691 1073.449463 \n", + "25 1419.576294 1447.571167 1464.597778 724.289246 1002.412292 \n", + "26 1490.613403 1518.608276 1535.634888 759.807800 931.375183 \n", + "27 1561.650513 1589.645386 1606.671997 795.326355 860.338074 \n", + "28 1632.687622 1660.682495 1677.709106 830.844910 789.300964 \n", + "29 1760.782593 1788.777466 1805.804077 894.892395 661.205994 \n", + "30 1875.809570 1903.804443 1920.830933 952.405884 546.179016 \n", + "31 1990.836426 2018.831421 2035.857910 1009.919312 431.152100 \n", + "32 2105.863525 2133.858398 2150.884766 1067.432861 316.125153 \n", + "33 2220.890381 2248.885254 2265.911865 1124.946289 201.098221 \n", + "\n", + " y_z1 y_H2O_z1 y_modloss_z1 z_z1 \n", + "0 948.424377 930.413818 0.000000 932.405640 \n", + "1 788.393738 770.383179 0.000000 772.375000 \n", + "2 673.366760 655.356201 0.000000 657.348083 \n", + "3 544.324219 526.313660 0.000000 528.305481 \n", + "4 397.255768 379.245209 0.000000 381.237061 \n", + "5 260.196869 242.186310 0.000000 244.178146 \n", + "6 147.112808 129.102234 0.000000 131.094086 \n", + "7 993.471008 975.460449 0.000000 977.452271 \n", + "8 896.418213 878.407654 0.000000 880.399536 \n", + "9 781.391296 763.380737 0.000000 765.372559 \n", + "10 652.348694 634.338135 0.000000 636.329956 \n", + "11 505.280273 487.269714 0.000000 489.261566 \n", + "12 374.239807 356.229218 0.000000 358.221069 \n", + "13 260.196869 242.186310 0.000000 244.178146 \n", + "14 147.112808 129.102234 0.000000 131.094086 \n", + "15 2236.917725 2218.906982 2138.940674 2220.898926 \n", + "16 2121.890625 2103.880127 2023.913818 2105.872070 \n", + "17 1954.892334 1936.881714 0.000000 1938.873657 \n", + "18 1825.849731 1807.839111 0.000000 1809.831055 \n", + "19 1678.781372 1660.770752 0.000000 1662.762573 \n", + "20 1531.745972 1513.735352 0.000000 1515.727173 \n", + "21 1417.703003 1399.692383 0.000000 1401.684326 \n", + "22 1316.655273 1298.644775 0.000000 1300.636597 \n", + "23 1203.571289 1185.560669 0.000000 1187.552490 \n", + "24 1047.470093 1029.459595 0.000000 1031.451416 \n", + "25 976.433044 958.422485 0.000000 960.414307 \n", + "26 905.395935 887.385376 0.000000 889.377197 \n", + "27 834.358826 816.348267 0.000000 818.340088 \n", + "28 763.321716 745.311096 0.000000 747.302979 \n", + "29 635.226746 617.216187 0.000000 619.208008 \n", + "30 520.199768 502.189209 0.000000 504.181061 \n", + "31 405.172852 387.162262 0.000000 389.154114 \n", + "32 290.145905 272.135345 0.000000 274.127167 \n", + "33 175.118958 157.108383 0.000000 159.100235 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from alphabase.peptide.fragment import create_fragment_mz_dataframe\n", + "frag_mz_df = create_fragment_mz_dataframe(\n", + " df,\n", + " charged_frag_types=['a_z1','b_z1','c_z1','b_z2','x_z1','y_z1', 'y_H2O_z1','y_modloss_z1','z_z1']\n", + ")\n", + "frag_mz_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that all N-term (a/b/c) fragment mz values are in ascending order, e.g. from b[1] to b[n-1]; and all C-term (x/y/z) fragments are in descending order, e.g. from y[n-1] to y[1]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fragment dataframe is connected to the peptide (precursor) dataframe by `frag_start_idx` and `frag_stop_idx` in the peptide dataframe. These two values can locate all fragments in the fragment dataframe of a peptide, as shown in the figure." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    sequencemodsmod_siteschargenAAprecursor_mzi_0i_1i_2i_3i_4i_5mono_isotope_idxfrag_start_idxfrag_stop_idx
    0ACDEFHIKCarbamidomethyl@C2181019.4614920.5448900.2942080.1169000.0343400.0080770.001584007
    1APDEFMNIK29532.7576920.5278390.3008260.1230180.0373590.0091040.0018540715
    2WDSEFMNTIRAAAAKDDDDRPhospho@S;Oxidation@M3;6320808.3371660.2710280.3237750.2256410.1154410.0475530.01656101534
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    " + ], + "text/plain": [ + " sequence mods mod_sites charge nAA \\\n", + "0 ACDEFHIK Carbamidomethyl@C 2 1 8 \n", + "1 APDEFMNIK 2 9 \n", + "2 WDSEFMNTIRAAAAKDDDDR Phospho@S;Oxidation@M 3;6 3 20 \n", + "\n", + " precursor_mz i_0 i_1 i_2 i_3 i_4 i_5 \\\n", + "0 1019.461492 0.544890 0.294208 0.116900 0.034340 0.008077 0.001584 \n", + "1 532.757692 0.527839 0.300826 0.123018 0.037359 0.009104 0.001854 \n", + "2 808.337166 0.271028 0.323775 0.225641 0.115441 0.047553 0.016561 \n", + "\n", + " mono_isotope_idx frag_start_idx frag_stop_idx \n", + "0 0 0 7 \n", + "1 0 7 15 \n", + "2 0 15 34 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    a_z1b_z1c_z1b_z2x_z1y_z1y_H2O_z1y_modloss_z1z_z1
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    16274.118622302.113525319.140076151.5604102147.8698732121.8906252103.8801272023.9138182105.872070
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    \n", + "
    " + ], + "text/plain": [ + " a_z1 b_z1 c_z1 b_z2 x_z1 \\\n", + "15 159.091675 187.086594 204.113144 94.046936 2262.896973 \n", + "16 274.118622 302.113525 319.140076 151.560410 2147.869873 \n", + "17 441.116974 469.111877 486.138428 235.059586 1980.871582 \n", + "18 570.159546 598.154480 615.181030 299.580872 1851.828979 \n", + "19 717.227966 745.222900 762.249451 373.115082 1704.760620 \n", + "20 864.263367 892.258301 909.284851 446.632782 1557.725220 \n", + "21 978.306335 1006.301208 1023.327759 503.654266 1443.682251 \n", + "22 1079.354004 1107.348877 1124.375488 554.178101 1342.634521 \n", + "23 1192.438110 1220.432983 1237.459473 610.720093 1229.550537 \n", + "24 1348.539185 1376.534058 1393.560669 688.770691 1073.449463 \n", + "25 1419.576294 1447.571167 1464.597778 724.289246 1002.412292 \n", + "26 1490.613403 1518.608276 1535.634888 759.807800 931.375183 \n", + "27 1561.650513 1589.645386 1606.671997 795.326355 860.338074 \n", + "28 1632.687622 1660.682495 1677.709106 830.844910 789.300964 \n", + "29 1760.782593 1788.777466 1805.804077 894.892395 661.205994 \n", + "30 1875.809570 1903.804443 1920.830933 952.405884 546.179016 \n", + "31 1990.836426 2018.831421 2035.857910 1009.919312 431.152100 \n", + "32 2105.863525 2133.858398 2150.884766 1067.432861 316.125153 \n", + "33 2220.890381 2248.885254 2265.911865 1124.946289 201.098221 \n", + "\n", + " y_z1 y_H2O_z1 y_modloss_z1 z_z1 \n", + "15 2236.917725 2218.906982 2138.940674 2220.898926 \n", + "16 2121.890625 2103.880127 2023.913818 2105.872070 \n", + "17 1954.892334 1936.881714 0.000000 1938.873657 \n", + "18 1825.849731 1807.839111 0.000000 1809.831055 \n", + "19 1678.781372 1660.770752 0.000000 1662.762573 \n", + "20 1531.745972 1513.735352 0.000000 1515.727173 \n", + "21 1417.703003 1399.692383 0.000000 1401.684326 \n", + "22 1316.655273 1298.644775 0.000000 1300.636597 \n", + "23 1203.571289 1185.560669 0.000000 1187.552490 \n", + "24 1047.470093 1029.459595 0.000000 1031.451416 \n", + "25 976.433044 958.422485 0.000000 960.414307 \n", + "26 905.395935 887.385376 0.000000 889.377197 \n", + "27 834.358826 816.348267 0.000000 818.340088 \n", + "28 763.321716 745.311096 0.000000 747.302979 \n", + "29 635.226746 617.216187 0.000000 619.208008 \n", + "30 520.199768 502.189209 0.000000 504.181061 \n", + "31 405.172852 387.162262 0.000000 389.154114 \n", + "32 290.145905 272.135345 0.000000 274.127167 \n", + "33 175.118958 157.108383 0.000000 159.100235 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pep_id = -1 # last peptide\n", + "start = df['frag_start_idx'].values[pep_id]\n", + "stop = df['frag_stop_idx'].values[pep_id]\n", + "frag_mz_df.iloc[start:stop]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.3 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + 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zYj)5M_s}zXlf>4t%GyowE1!bt4e~lJmB*7*`1%R5IQfB(7%y=XZ35CyPoVuwv4l-Q zU-9t2c`2nx0VFhJt!AIPG?o`%k$I`qx5dO^vW%#ue&Z4SqcY z%!-JKMjeVL!h!;I+I2z~{^H%uzDoB}NK~}XkE@zZ>0mV{d6qnRmi<3Vm!yu=%idz@ z*V&Fi#HdlTHx9@ak;B3nRp^B zd}A(dpFOpdf%8&K3=3eELw1K8bXkZn}8{n zTLT-{hav+xKR1{Or(dm;)4}`Rff^b6l;a8XB&iE8q9&919^oJU&pCI1&oKo=_2&cK zANZKOiWGu6YBJ9I!N-;;s&-+5?7C%`UDhw(#vu)iPT^qR602%8nF!t$s#erOk4EZ~ z%76DQ(exSp=1m_vm<}0xX~oYn zW-U|Tpg99a%)n)2;K+)J2e#s0{Q%N1B5#6J<5tbZ?Oc%BRmR0`F-=8f_zxO5iR*RVT9% zI9(|B@xhfX_;M{?=fLw_A1L4TSC(W&qbEet-RZ6a%y;fK7N)xBDT8!(CX0cRu-x2R z({Ka=@u7G?kQ Date: Tue, 16 Jul 2024 12:13:56 +0200 Subject: [PATCH 36/53] Merge development updates --- alphabase/spectral_library/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/alphabase/spectral_library/base.py b/alphabase/spectral_library/base.py index 832c8741..87297d74 100644 --- a/alphabase/spectral_library/base.py +++ b/alphabase/spectral_library/base.py @@ -693,7 +693,7 @@ def _replace_mod_name_whitespaces(mod_seq_df: pd.DataFrame) -> None: "Support for whitespaces in modifications will be dropped in the next major release of alphabase. " "Please use underscores in your spectral libraries instead." ) - warn( + warnings.warn( msg, DeprecationWarning, stacklevel=2, From 9d30aa120cf4cfbb14b2cb43ed06c5dab2e49b89 Mon Sep 17 00:00:00 2001 From: mschwoerer <82171591+mschwoer@users.noreply.github.com> Date: Wed, 17 Jul 2024 10:31:34 +0200 Subject: [PATCH 37/53] some more replacements in notebook outputs --- docs/nbs/library_reader.ipynb | 8 ++++---- nbs_tests/constants/modification.ipynb | 20 ++++++++++---------- nbs_tests/psm_reader/alphapept_reader.ipynb | 2 +- nbs_tests/psm_reader/maxquant_reader.ipynb | 2 +- 4 files changed, 16 insertions(+), 16 deletions(-) diff --git a/docs/nbs/library_reader.ipynb b/docs/nbs/library_reader.ipynb index b31554f6..499d5375 100644 --- a/docs/nbs/library_reader.ipynb +++ b/docs/nbs/library_reader.ipynb @@ -2065,7 +2065,7 @@ " 4\n", " 2.957368\n", " NaN\n", - " Dimethyl:2H(4)@Any N-term\n", + " Dimethyl:2H(4)@Any_N-term\n", " 0\n", " 9\n", " 0\n", @@ -2080,7 +2080,7 @@ " 3\n", " 10.904905\n", " NaN\n", - " Dimethyl:2H(4)@Any N-term\n", + " Dimethyl:2H(4)@Any_N-term\n", " 0\n", " 7\n", " 8\n", @@ -2095,8 +2095,8 @@ ], "text/plain": [ " sequence charge rt mobility mods \\\n", - "0 EDTEEHHLR 4 2.957368 NaN Dimethyl:2H(4)@Any N-term \n", - "1 MQHLIAR 3 10.904905 NaN Dimethyl:2H(4)@Any N-term \n", + "0 EDTEEHHLR 4 2.957368 NaN Dimethyl:2H(4)@Any_N-term \n", + "1 MQHLIAR 3 10.904905 NaN Dimethyl:2H(4)@Any_N-term \n", "\n", " mod_sites nAA frag_start_idx frag_stop_idx rt_norm precursor_mz ccs \n", "0 0 9 0 8 0.271196 300.150369 NaN \n", diff --git a/nbs_tests/constants/modification.ipynb b/nbs_tests/constants/modification.ipynb index 9a68d9bb..a3996002 100644 --- a/nbs_tests/constants/modification.ipynb +++ b/nbs_tests/constants/modification.ipynb @@ -156,8 +156,8 @@ " 0.0\n", " \n", " \n", - " Acetyl@Any N-term\n", - " Acetyl@Any N-term\n", + " Acetyl@Any_N-term\n", + " Acetyl@Any_N-term\n", " 42.010565\n", " 42.0367\n", " H(2)C(2)O(1)\n", @@ -570,8 +570,8 @@ " 0.0\n", " \n", " \n", - " Oxidation@G^Any C-term\n", - " Oxidation@G^Any C-term\n", + " Oxidation@G^Any_C-term\n", + " Oxidation@G^Any_C-term\n", " 15.994915\n", " 15.9994\n", " O(1)\n", @@ -716,7 +716,7 @@ "Acetyl@Y Acetyl@Y 42.010565 \n", "Acetyl@R Acetyl@R 42.010565 \n", "ICAT-G@C ICAT-G@C 486.251206 \n", - "Oxidation@G^Any C-term Oxidation@G^Any C-term 15.994915 \n", + "Oxidation@G^Any_C-term Oxidation@G^Any_C-term 15.994915 \n", "Hex@C Hex@C 162.052824 \n", "Hex@T Hex@T 162.052824 \n", "Hex@N Hex@N 162.052824 \n", @@ -733,7 +733,7 @@ "Acetyl@Y 42.0367 H(2)C(2)O(1) \n", "Acetyl@R 42.0367 H(2)C(2)O(1) \n", "ICAT-G@C 486.6253 H(38)C(22)N(4)O(6)S(1) \n", - "Oxidation@G^Any C-term 15.9994 O(1) \n", + "Oxidation@G^Any_C-term 15.9994 O(1) \n", "Hex@C 162.1406 H(10)C(6)O(5) \n", "Hex@T 162.1406 H(10)C(6)O(5) \n", "Hex@N 162.1406 H(10)C(6)O(5) \n", @@ -750,7 +750,7 @@ "Acetyl@Y 0.000000 \n", "Acetyl@R 0.000000 \n", "ICAT-G@C 0.000000 \n", - "Oxidation@G^Any C-term 0.000000 \n", + "Oxidation@G^Any_C-term 0.000000 \n", "Hex@C 0.000000 \n", "Hex@T 162.052824 H(10)C(6)O(5) \n", "Hex@N 162.052824 H(10)C(6)O(5) \n", @@ -767,7 +767,7 @@ "Acetyl@Y Chemical derivative 1 \n", "Acetyl@R Artefact 1 \n", "ICAT-G@C Isotopic label 8 \n", - "Oxidation@G^Any C-term Pre-translational 35 \n", + "Oxidation@G^Any_C-term Pre-translational 35 \n", "Hex@C Other glycosylation 41 \n", "Hex@T O-linked glycosylation 41 \n", "Hex@N N-linked glycosylation 41 \n", @@ -784,7 +784,7 @@ "Acetyl@Y 0.0 42.010565 \n", "Acetyl@R 0.0 42.010565 \n", "ICAT-G@C 0.0 486.251206 \n", - "Oxidation@G^Any C-term 0.0 15.994915 \n", + "Oxidation@G^Any_C-term 0.0 15.994915 \n", "Hex@C 0.0 162.052823 \n", "Hex@T 0.0 162.052823 \n", "Hex@N 0.0 162.052823 \n", @@ -801,7 +801,7 @@ "Acetyl@Y 0.000000 0.0 \n", "Acetyl@R 0.000000 0.0 \n", "ICAT-G@C 0.000000 0.0 \n", - "Oxidation@G^Any C-term 0.000000 0.0 \n", + "Oxidation@G^Any_C-term 0.000000 0.0 \n", "Hex@C 0.000000 0.0 \n", "Hex@T 162.052823 0.0 \n", "Hex@N 162.052823 0.0 \n", diff --git a/nbs_tests/psm_reader/alphapept_reader.ipynb b/nbs_tests/psm_reader/alphapept_reader.ipynb index f19aed9c..d1af6d84 100644 --- a/nbs_tests/psm_reader/alphapept_reader.ipynb +++ b/nbs_tests/psm_reader/alphapept_reader.ipynb @@ -76,7 +76,7 @@ " 'Phospho@S': 'pS',\n", " 'Phospho@T': 'pT',\n", " 'Phospho@Y': 'pY',\n", - " 'Acetyl@Protein N-term': 'a'}" + " 'Acetyl@Protein_N-term': 'a'}" ] }, "execution_count": null, diff --git a/nbs_tests/psm_reader/maxquant_reader.ipynb b/nbs_tests/psm_reader/maxquant_reader.ipynb index 7d5fbced..90c9c5f0 100644 --- a/nbs_tests/psm_reader/maxquant_reader.ipynb +++ b/nbs_tests/psm_reader/maxquant_reader.ipynb @@ -75,7 +75,7 @@ "text/plain": [ "{'Dimethyl@K': ['K(Dimethyl)'],\n", " 'Dimethyl@R': ['R(Dimethyl)'],\n", - " 'Dimethyl@Any N-term': ['(Dimethyl)'],\n", + " 'Dimethyl@Any_N-term': ['(Dimethyl)'],\n", " 'Acetyl@Protein_N-term': ['_(Acetyl (Protein_N-term))', '_(ac)'],\n", " 'Carbamidomethyl@C': ['C(Carbamidomethyl (C))', 'C(Carbamidomethyl)'],\n", " 'Oxidation@M': ['M(Oxidation)', 'M(Oxidation (M))', 'M(ox)'],\n", From 4a223e2e446c2b1073847a1f078a3e31b856807e Mon Sep 17 00:00:00 2001 From: jalew188 Date: Wed, 17 Jul 2024 11:31:33 +0200 Subject: [PATCH 38/53] #198 add a new param save_mod_seq_in_other_df=False in SpecLibBase.save_hdf --- alphabase/spectral_library/base.py | 103 ++++--- nbs_tests/spectral_library/library_base.ipynb | 251 ++++++++++++++++-- 2 files changed, 294 insertions(+), 60 deletions(-) diff --git a/alphabase/spectral_library/base.py b/alphabase/spectral_library/base.py index 7edf6fae..fc111af6 100644 --- a/alphabase/spectral_library/base.py +++ b/alphabase/spectral_library/base.py @@ -59,6 +59,7 @@ class SpecLibBase: "isotope_right_most_mz", "isotope_right_most_intensity", "isotope_right_most_offset", + "mono_isotope_idx", "miss_cleavage", "mobility_pred", "mobility", @@ -68,7 +69,7 @@ class SpecLibBase: "rt_norm_pred", "rt", "labeling_channel", - ] + ] + [f"i_{i}" for i in range(10)] """ list of str: Key numeric columns to be saved into library/precursor_df in the hdf file for fast loading, @@ -581,51 +582,71 @@ def load_df_from_hdf(self, hdf_file: str, df_name: str) -> pd.DataFrame: """ return self._get_hdf_to_load(hdf_file).__getattribute__(df_name).values - def save_hdf(self, hdf_file: str): + def save_hdf(self, hdf_file: str, save_mod_seq_in_other_df: bool = False): """Save library dataframes into hdf_file. - For `self.precursor_df`, this method will save it into two hdf groups in hdf_file: - `library/precursor_df` and `library/mod_seq_df`. - - `library/precursor_df` contains all essential numberic columns those - can be loaded faster from hdf file into memory: - - 'precursor_mz', 'charge', 'mod_seq_hash', 'mod_seq_charge_hash', - 'frag_start_idx', 'frag_stop_idx', 'decoy', 'rt_pred', 'ccs_pred', - 'mobility_pred', 'miss_cleave', 'nAA', - ['isotope_mz_m1', 'isotope_intensity_m1'], ... - - `library/mod_seq_df` contains all string columns and the other - not essential columns: - 'sequence','mods','mod_sites', ['proteins', 'genes']... - as well as 'mod_seq_hash', 'mod_seq_charge_hash' columns to map - back to `precursor_df` Parameters ---------- hdf_file : str - the hdf file path to save + The hdf file path to save + + save_mod_seq_in_other_df : bool + If True: save `self.precursor_df` into two hdf groups in hdf_file, + `library/precursor_df` and `library/mod_seq_df`. + + `library/precursor_df` contains all essential numberic columns those + can be loaded faster from hdf file into memory: + + 'precursor_mz', 'charge', 'mod_seq_hash', 'mod_seq_charge_hash', + 'frag_start_idx', 'frag_stop_idx', 'decoy', 'rt_pred', 'ccs_pred', + 'mobility_pred', 'miss_cleave', 'nAA', + ['isotope_mz_m1', 'isotope_intensity_m1'], ... + + `library/mod_seq_df` contains all string columns and the other + not essential columns: + + - 'sequence' + - 'mods' + - 'mod_sites' + - 'proteins', 'genes', ...: optional columns + - 'mod_seq_hash': one-to-one map back to `precursor_df` + - 'mod_seq_charge_hash': one-to-one map back to `precursor_df` + If False: + All columns of `self.precursor_df` will be saved into `library/precursor_df`. + + Defaults to False. """ _hdf = HDF_File(hdf_file, read_only=False, truncate=True, delete_existing=True) if "mod_seq_charge_hash" not in self._precursor_df.columns: self.hash_precursor_df() - key_columns = self.key_numeric_columns + ["mod_seq_hash", "mod_seq_charge_hash"] - - _hdf.library = { - "mod_seq_df": self._precursor_df[ - [ - col - for col in self._precursor_df.columns - if col not in self.key_numeric_columns - ] - ], - "precursor_df": self._precursor_df[ - [col for col in self._precursor_df.columns if col in key_columns] - ], - "fragment_mz_df": self._fragment_mz_df, - "fragment_intensity_df": self._fragment_intensity_df, - } + if save_mod_seq_in_other_df: + key_columns = self.key_numeric_columns + [ + "mod_seq_hash", + "mod_seq_charge_hash", + ] + + _hdf.library = { + "mod_seq_df": self._precursor_df[ + [ + col + for col in self._precursor_df.columns + if col not in self.key_numeric_columns + ] + ], + "precursor_df": self._precursor_df[ + [col for col in self._precursor_df.columns if col in key_columns] + ], + "fragment_mz_df": self._fragment_mz_df, + "fragment_intensity_df": self._fragment_intensity_df, + } + else: + _hdf.library = { + "precursor_df": self._precursor_df, + "fragment_mz_df": self._fragment_mz_df, + "fragment_intensity_df": self._fragment_intensity_df, + } def load_hdf(self, hdf_file: str, load_mod_seq: bool = True): """Load the hdf library from hdf_file @@ -636,16 +657,14 @@ def load_hdf(self, hdf_file: str, load_mod_seq: bool = True): hdf library path to load load_mod_seq : bool, optional - For performance reason, the susbset of non key numeric columns is stored in mod_seq_df. - For fast loading, set load_mod_seq to False to skip loading mod_seq_df. + By default, `mod_seq_df` is not used in the :meth:`save_hdf`, so this param is not used. + However, for performance reason, users can save the susbset of non key numeric columns + in mod_seq_df. For fast loading, set load_mod_seq to False to skip loading mod_seq_df. Defaults to True. - """ - _hdf = HDF_File( - hdf_file, - ) + _hdf = HDF_File(hdf_file) self._precursor_df: pd.DataFrame = _hdf.library.precursor_df.values - if load_mod_seq: + if load_mod_seq and hasattr(_hdf.library, "mod_seq_df"): key_columns = self.key_numeric_columns + [ "mod_seq_hash", "mod_seq_charge_hash", diff --git a/nbs_tests/spectral_library/library_base.ipynb b/nbs_tests/spectral_library/library_base.ipynb index 68fc21fb..31235054 100644 --- a/nbs_tests/spectral_library/library_base.ipynb +++ b/nbs_tests/spectral_library/library_base.ipynb @@ -21,9 +21,17 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], "source": [ - "from alphabase.spectral_library.base import *" + "from alphabase.spectral_library.base import SpecLibBase, annotate_fragments_from_speclib" ] }, { @@ -41,6 +49,7 @@ "outputs": [], "source": [ "import pandas as pd\n", + "import numpy as np\n", "import os" ] }, @@ -60,6 +69,13 @@ "assert np.allclose(lib.precursor_df.precursor_mz.values, [1000,1500,2000])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test `save_df(...,save_mod_seq_in_other_df=True)`" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -225,7 +241,206 @@ "target_lib._fragment_intensity_df = pd.DataFrame()\n", "if not os.path.isdir('sandbox'):\n", " os.makedirs('sandbox')\n", - "target_lib.save_hdf('sandbox/test_lib.hdf')\n", + "target_lib.save_hdf('sandbox/test_lib.hdf', save_mod_seq_in_other_df=True)\n", + "target_lib.save_df_to_hdf('sandbox/test_lib.hdf','protein_df',pd.DataFrame(\n", + " {\n", + " 'id':[1,2],\n", + " 'full_name': [1,2],\n", + " 'description': [1,2],\n", + " 'sequence': [1,2]\n", + " })\n", + ")\n", + "\n", + "new_lib = SpecLibBase([])\n", + "new_lib.load_hdf('sandbox/test_lib.hdf', load_mod_seq=True)\n", + "assert len(new_lib.precursor_df) > 0\n", + "assert len(new_lib.fragment_mz_df) == 0\n", + "assert len(new_lib.fragment_intensity_df) == 0\n", + "\n", + "assert 'sequence' in new_lib.precursor_df.columns\n", + "assert 'mod_seq_hash' in new_lib.precursor_df.columns\n", + "\n", + "df = target_lib.load_df_from_hdf('sandbox/test_lib.hdf', 'precursor_df')\n", + "assert len(precursor_df)==len(df)\n", + "df = target_lib.load_df_from_hdf('sandbox/test_lib.hdf', 'protein_df')\n", + "assert len(df)==2\n", + "#os.remove('sandbox/test_lib.hdf')\n", + "precursor_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test `save_df(...,save_mod_seq_in_other_df=False)`, the default setting" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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    sequencemodsmod_sitesnAAchargeprecursor_mzmod_seq_hashmod_seq_charge_hash
    0AGHCEWQMKAcetyl@Protein N-term;Carbamidomethyl@C;Oxidat...0;4;892602.7473331561237102547049316815612371025470493170
    1AGHCEWQMKAcetyl@Protein N-term;Carbamidomethyl@C;Oxidat...0;4;892602.7473331561237102547049316815612371025470493170
    2AGHCEWQMKAcetyl@Protein N-term;Carbamidomethyl@C;Oxidat...0;4;892602.7473331561237102547049316815612371025470493170
    3AGHCEWQMKAADER142816.35629968316588246732441356831658824673244137
    4AGHCEWQMKAADER142816.35629968316588246732441356831658824673244137
    5AGHCEWQMKAADER142816.35629968316588246732441356831658824673244137
    \n", + "
    " + ], + "text/plain": [ + " sequence mods \\\n", + "0 AGHCEWQMK Acetyl@Protein N-term;Carbamidomethyl@C;Oxidat... \n", + "1 AGHCEWQMK Acetyl@Protein N-term;Carbamidomethyl@C;Oxidat... \n", + "2 AGHCEWQMK Acetyl@Protein N-term;Carbamidomethyl@C;Oxidat... \n", + "3 AGHCEWQMKAADER \n", + "4 AGHCEWQMKAADER \n", + "5 AGHCEWQMKAADER \n", + "\n", + " mod_sites nAA charge precursor_mz mod_seq_hash \\\n", + "0 0;4;8 9 2 602.747333 15612371025470493168 \n", + "1 0;4;8 9 2 602.747333 15612371025470493168 \n", + "2 0;4;8 9 2 602.747333 15612371025470493168 \n", + "3 14 2 816.356299 6831658824673244135 \n", + "4 14 2 816.356299 6831658824673244135 \n", + "5 14 2 816.356299 6831658824673244135 \n", + "\n", + " mod_seq_charge_hash \n", + "0 15612371025470493170 \n", + "1 15612371025470493170 \n", + "2 15612371025470493170 \n", + "3 6831658824673244137 \n", + "4 6831658824673244137 \n", + "5 6831658824673244137 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "repeat = 3\n", + "peptides = ['AGHCEWQMK']*repeat\n", + "mods = ['Acetyl@Protein N-term;Carbamidomethyl@C;Oxidation@M']*repeat\n", + "sites = ['0;4;8']*repeat\n", + "peptides += ['AGHCEWQMKAADER']*repeat\n", + "mods += ['']*repeat\n", + "sites += ['']*repeat\n", + "\n", + "precursor_df = pd.DataFrame({\n", + " 'sequence': peptides,\n", + " 'mods': mods,\n", + " 'mod_sites': sites\n", + "})\n", + "precursor_df['nAA'] = precursor_df['sequence'].str.len()\n", + "precursor_df['charge'] = 2\n", + "target_lib = SpecLibBase(\n", + " ['b_z1','b_z2','y_z1','y_z2'],\n", + " decoy='pseudo_reverse'\n", + ")\n", + "target_lib._precursor_df = precursor_df\n", + "target_lib.calc_precursor_mz()\n", + "target_lib._fragment_mz_df = pd.DataFrame()\n", + "target_lib._fragment_intensity_df = pd.DataFrame()\n", + "if not os.path.isdir('sandbox'):\n", + " os.makedirs('sandbox')\n", + "target_lib.save_hdf('sandbox/test_lib.hdf', save_mod_seq_in_other_df=False)\n", "target_lib.save_df_to_hdf('sandbox/test_lib.hdf','protein_df',pd.DataFrame(\n", " {\n", " 'id':[1,2],\n", @@ -254,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -482,7 +697,7 @@ "11 6831658824673244137 1 " ] }, - "execution_count": null, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -495,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -625,7 +840,7 @@ "[126 rows x 4 columns]" ] }, - "execution_count": null, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -639,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -694,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -739,7 +954,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -780,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -799,14 +1014,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The columns are not compatible. {'y_z1', 'y_z2', 'b_z2', 'b_z1'} are missing in the dataframe which should be appended.\n" + "The columns are not compatible. {'y_z2', 'b_z2', 'b_z1', 'y_z1'} are missing in the dataframe which should be appended.\n" ] } ], @@ -826,7 +1041,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -853,14 +1068,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/wenfengzeng/workspace/alphabase/alphabase/spectral_library/base.py:190: UserWarning: Unmatched columns in second dataframe will be dropped: {'sequence'}.\n", + "/Users/wenfengzeng/workspace/alphabase/alphabase/spectral_library/base.py:245: UserWarning: Unmatched columns in second dataframe will be dropped: {'sequence'}.\n", " warnings.warn(\n" ] } @@ -874,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -929,7 +1144,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.4" } }, "nbformat": 4, From 5b1fa19cfd379b92e81a53e6b2857c142fb0b261 Mon Sep 17 00:00:00 2001 From: GeorgWa Date: Wed, 17 Jul 2024 13:20:39 +0200 Subject: [PATCH 39/53] update unimod to tsv --- .../constants/const_files/modification.tsv | 5459 +++++++++-------- scripts/unimod_to_tsv.ipynb | 341 +- 2 files changed, 2848 insertions(+), 2952 deletions(-) diff --git a/alphabase/constants/const_files/modification.tsv b/alphabase/constants/const_files/modification.tsv index 8ba1b0cc..6d28216b 100644 --- a/alphabase/constants/const_files/modification.tsv +++ b/alphabase/constants/const_files/modification.tsv @@ -1,2686 +1,2773 @@ -mod_name unimod_mass unimod_avge_mass composition unimod_modloss modloss_composition classification unimod_id modloss_importance -Acetyl@T 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 -Acetyl@Protein_N-term 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 -Acetyl@S 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 -Acetyl@C 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 -Acetyl@Any_N-term 42.010565 42.0367 H(2)C(2)O(1) 0.0 Multiple 1 0.0 -Acetyl@K 42.010565 42.0367 H(2)C(2)O(1) 0.0 Multiple 1 0.0 -Acetyl@Y 42.010565 42.0367 H(2)C(2)O(1) 0.0 Chemical derivative 1 0.0 -Acetyl@H 42.010565 42.0367 H(2)C(2)O(1) 0.0 Chemical derivative 1 0.0 -Acetyl@R 42.010565 42.0367 H(2)C(2)O(1) 0.0 Artefact 1 0.0 -Amidated@Any_C-term -0.984016 -0.9848 H(1)N(1)O(-1) 0.0 Artefact 2 0.0 -Amidated@Protein_C-term -0.984016 -0.9848 H(1)N(1)O(-1) 0.0 Post-translational 2 0.0 -Biotin@Any_N-term 226.077598 226.2954 H(14)C(10)N(2)O(2)S(1) 0.0 Chemical derivative 3 0.0 -Biotin@K 226.077598 226.2954 H(14)C(10)N(2)O(2)S(1) 0.0 Post-translational 3 0.0 -Carbamidomethyl@Y 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@T 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@S 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@E 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@D 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@H 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@Any_N-term 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@K 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 -Carbamidomethyl@C 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 4 0.0 -Carbamidomethyl@U 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 4 0.0 -Carbamidomethyl@M 57.021464 57.0513 H(3)C(2)N(1)O(1) 105.024835 H(7)C(3)N(1)O(1)S(1) Chemical derivative 4 0.5 -Carbamyl@Y 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Chemical derivative 5 0.0 -Carbamyl@T 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Chemical derivative 5 0.0 -Carbamyl@S 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Chemical derivative 5 0.0 -Carbamyl@M 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 -Carbamyl@C 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 -Carbamyl@R 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 -Carbamyl@Any_N-term 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Multiple 5 0.0 -Carbamyl@K 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Multiple 5 0.0 -Carbamyl@Protein_N-term 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Post-translational 5 0.0 -Carboxymethyl@Any_N-term 58.005479 58.0361 H(2)C(2)O(2) 0.0 Artefact 6 0.0 -Carboxymethyl@K 58.005479 58.0361 H(2)C(2)O(2) 0.0 Artefact 6 0.0 -Carboxymethyl@C 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 -Carboxymethyl@W 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 -Carboxymethyl@U 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 -Deamidated@Q 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Artefact 7 0.0 -Deamidated@R 0.984016 0.9848 H(-1)N(-1)O(1) 43.005814 H(1)C(1)N(1)O(1) Post-translational 7 0.5 -Deamidated@N 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Artefact 7 0.0 -Deamidated@F^Protein_N-term 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Post-translational 7 0.0 -ICAT-G@C 486.251206 486.6253 H(38)C(22)N(4)O(6)S(1) 0.0 Isotopic label 8 0.0 -ICAT-G:2H(8)@C 494.30142 494.6746 H(30)2H(8)C(22)N(4)O(6)S(1) 0.0 Isotopic label 9 0.0 -Met->Hse@M^Any_C-term -29.992806 -30.0922 H(-2)C(-1)O(1)S(-1) 0.0 Chemical derivative 10 0.0 -Met->Hsl@M^Any_C-term -48.003371 -48.1075 H(-4)C(-1)S(-1) 0.0 Chemical derivative 11 0.0 -ICAT-D:2H(8)@C 450.275205 450.6221 H(26)2H(8)C(20)N(4)O(5)S(1) 0.0 Isotopic label 12 0.0 -ICAT-D@C 442.224991 442.5728 H(34)C(20)N(4)O(5)S(1) 0.0 Isotopic label 13 0.0 -NIPCAM@C 99.068414 99.1311 H(9)C(5)N(1)O(1) 0.0 Chemical derivative 17 0.0 -PEO-Iodoacetyl-LC-Biotin@C 414.193691 414.5196 H(30)C(18)N(4)O(5)S(1) 0.0 Chemical derivative 20 0.0 -Phospho@E 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 -Phospho@R 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 -Phospho@K 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 -Phospho@H 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 -Phospho@C 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 -Phospho@D 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 -Phospho@Y 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 -Phospho@T 79.966331 79.9799 H(1)O(3)P(1) 97.976896 H(3)O(4)P(1) Post-translational 21 10000000.0 -Phospho@S 79.966331 79.9799 H(1)O(3)P(1) 97.976896 H(3)O(4)P(1) Post-translational 21 100000000.0 -Methamidophos-S@Y 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 -Methamidophos-S@T 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 -Methamidophos-S@S 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 -Methamidophos-S@K 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 -Methamidophos-S@H 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 -Methamidophos-S@C 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 -Dehydrated@D -18.010565 -18.0153 H(-2)O(-1) 0.0 Chemical derivative 23 0.0 -Dehydrated@Y -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 -Dehydrated@T -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 -Dehydrated@S -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 -Dehydrated@N^Protein_C-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 -Dehydrated@Q^Protein_C-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 -Dehydrated@C^Any_N-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Artefact 23 0.0 -Propionamide@C 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Artefact 24 0.0 -Propionamide@K 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Chemical derivative 24 0.0 -Propionamide@Any_N-term 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Chemical derivative 24 0.0 -Pyridylacetyl@Any_N-term 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 25 0.0 -Pyridylacetyl@K 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 25 0.0 -Pyro-carbamidomethyl@C^Any_N-term 39.994915 40.0208 C(2)O(1) 0.0 Artefact 26 0.0 -Glu->pyro-Glu@E^Any_N-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Artefact 27 0.0 -Gln->pyro-Glu@Q^Any_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Artefact 28 0.0 -SMA@Any_N-term 127.063329 127.1412 H(9)C(6)N(1)O(2) 0.0 Chemical derivative 29 0.0 -SMA@K 127.063329 127.1412 H(9)C(6)N(1)O(2) 0.0 Chemical derivative 29 0.0 -Cation:Na@D 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 -Cation:Na@Any_C-term 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 -Cation:Na@E 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 -Pyridylethyl@C 105.057849 105.1372 H(7)C(7)N(1) 0.0 Chemical derivative 31 0.0 -Methyl@E 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@D 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@Any_C-term 14.01565 14.0266 H(2)C(1) 0.0 Multiple 34 0.0 -Methyl@Protein_N-term 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@L 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@I 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@R 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@Q 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@Any_N-term 14.01565 14.0266 H(2)C(1) 0.0 Chemical derivative 34 0.0 -Methyl@N 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@K 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@H 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@C 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@S 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Methyl@T 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 -Oxidation@T 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 -Oxidation@E 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 -Oxidation@S 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 -Oxidation@Q 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 -Oxidation@L 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 -Oxidation@I 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 -Oxidation@U 15.994915 15.9994 O(1) 0.0 Multiple 35 0.0 -Oxidation@G^Any_C-term 15.994915 15.9994 O(1) 0.0 Pre-translational 35 0.0 -Oxidation@W 15.994915 15.9994 O(1) 0.0 Artefact 35 0.0 -Oxidation@C 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 -Oxidation@H 15.994915 15.9994 O(1) 0.0 Artefact 35 0.0 -Oxidation@V 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 -Oxidation@R 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 -Oxidation@M 15.994915 15.9994 O(1) 63.998285 H(4)C(1)O(1)S(1) Artefact 35 0.5 -Oxidation@Y 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 -Oxidation@F 15.994915 15.9994 O(1) 0.0 Artefact 35 0.0 -Oxidation@P 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 -Oxidation@N 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 -Oxidation@K 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 -Oxidation@D 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 -Dimethyl@Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Isotopic label 36 0.0 -Dimethyl@P^Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 -Dimethyl@N 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 -Dimethyl@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Isotopic label 36 0.0 -Dimethyl@K 28.0313 28.0532 H(4)C(2) 0.0 Multiple 36 0.0 -Dimethyl@R 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 -Trimethyl@A^Protein_N-term 42.04695 42.0797 H(6)C(3) 0.0 Post-translational 37 0.0 -Trimethyl@R 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 37 0.0 -Trimethyl@K 42.04695 42.0797 H(6)C(3) 59.073499 H(9)C(3)N(1) Post-translational 37 0.5 -Methylthio@C 45.987721 46.0916 H(2)C(1)S(1) 0.0 Multiple 39 0.0 -Methylthio@N 45.987721 46.0916 H(2)C(1)S(1) 0.0 Post-translational 39 0.0 -Methylthio@D 45.987721 46.0916 H(2)C(1)S(1) 0.0 Post-translational 39 0.0 -Methylthio@K 45.987721 46.0916 H(2)C(1)S(1) 0.0 Artefact 39 0.0 -Methylthio@Any_N-term 45.987721 46.0916 H(2)C(1)S(1) 0.0 Artefact 39 0.0 -Sulfo@S 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 -Sulfo@T 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 -Sulfo@Y 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 -Sulfo@C 79.956815 80.0632 O(3)S(1) 0.0 Post-translational 40 0.0 -Hex@C 162.052824 162.1406 H(10)C(6)O(5) 0.0 Other glycosylation 41 0.0 -Hex@W 162.052824 162.1406 H(10)C(6)O(5) 0.0 Other glycosylation 41 0.0 -Hex@T 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) O-linked glycosylation 41 0.5 -Hex@S 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) O-linked glycosylation 41 0.5 -Hex@Any_N-term 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 -Hex@N 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) N-linked glycosylation 41 0.5 -Hex@R 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 -Hex@K 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 -Hex@Y 162.052824 162.1406 H(10)C(6)O(5) 0.0 O-linked glycosylation 41 0.0 -Lipoyl@K 188.032956 188.3103 H(12)C(8)O(1)S(2) 0.0 Post-translational 42 0.0 -HexNAc@C 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) Other glycosylation 43 0.5 -HexNAc@T 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) O-linked glycosylation 43 0.5 -HexNAc@S 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) O-linked glycosylation 43 0.5 -HexNAc@N 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) N-linked glycosylation 43 0.5 -Farnesyl@C 204.187801 204.3511 H(24)C(15) 0.0 Post-translational 44 0.0 -Myristoyl@C 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 -Myristoyl@K 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 -Myristoyl@G^Any_N-term 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 -PyridoxalPhosphate@K 229.014009 229.1266 H(8)C(8)N(1)O(5)P(1) 0.0 Post-translational 46 0.0 -Palmitoyl@T 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 -Palmitoyl@S 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 -Palmitoyl@K 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 -Palmitoyl@C 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 -Palmitoyl@Protein_N-term 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 -GeranylGeranyl@C 272.250401 272.4681 H(32)C(20) 0.0 Post-translational 48 0.0 -Phosphopantetheine@S 340.085794 340.333 H(21)C(11)N(2)O(6)P(1)S(1) 0.0 Post-translational 49 0.0 -FAD@Y 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 -FAD@H 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 -FAD@C 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 -Tripalmitate@C^Protein_N-term 788.725777 789.3049 H(96)C(51)O(5) 0.0 Post-translational 51 0.0 -Guanidinyl@K 42.021798 42.04 H(2)C(1)N(2) 0.0 Chemical derivative 52 0.0 -Guanidinyl@Any_N-term 42.021798 42.04 H(2)C(1)N(2) 0.0 Chemical derivative 52 0.0 -HNE@K 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 -HNE@H 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 -HNE@C 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 -HNE@A 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 -HNE@L 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 -Glucuronyl@T 176.032088 176.1241 H(8)C(6)O(6) 176.032088 H(8)C(6)O(6) O-linked glycosylation 54 0.5 -Glucuronyl@S 176.032088 176.1241 H(8)C(6)O(6) 176.032088 H(8)C(6)O(6) O-linked glycosylation 54 0.5 -Glucuronyl@Protein_N-term 176.032088 176.1241 H(8)C(6)O(6) 0.0 Other glycosylation 54 0.0 -Glutathione@C 305.068156 305.3076 H(15)C(10)N(3)O(6)S(1) 0.0 Post-translational 55 0.0 -Acetyl:2H(3)@T 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Acetyl:2H(3)@S 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Acetyl:2H(3)@H 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Acetyl:2H(3)@Any_N-term 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Acetyl:2H(3)@K 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Acetyl:2H(3)@Y 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Acetyl:2H(3)@Protein_N-term 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 -Propionyl@Any_N-term 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 -Propionyl@K 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 -Propionyl@S 56.026215 56.0633 H(4)C(3)O(1) 0.0 Chemical derivative 58 0.0 -Propionyl@T 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 -Propionyl@Protein_N-term 56.026215 56.0633 H(4)C(3)O(1) 0.0 Multiple 58 0.0 -Propionyl:13C(3)@Any_N-term 59.036279 59.0412 H(4)13C(3)O(1) 0.0 Isotopic label 59 0.0 -Propionyl:13C(3)@K 59.036279 59.0412 H(4)13C(3)O(1) 0.0 Isotopic label 59 0.0 -GIST-Quat@Any_N-term 127.099714 127.1842 H(13)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 60 0.5 -GIST-Quat@K 127.099714 127.1842 H(13)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 60 0.5 -GIST-Quat:2H(3)@Any_N-term 130.118544 130.2027 H(10)2H(3)C(7)N(1)O(1) 62.09233 H(6)2H(3)C(3)N(1) Isotopic label 61 0.5 -GIST-Quat:2H(3)@K 130.118544 130.2027 H(10)2H(3)C(7)N(1)O(1) 62.09233 H(6)2H(3)C(3)N(1) Isotopic label 61 0.5 -GIST-Quat:2H(6)@Any_N-term 133.137375 133.2212 H(7)2H(6)C(7)N(1)O(1) 65.11116 H(3)2H(6)C(3)N(1) Isotopic label 62 0.5 -GIST-Quat:2H(6)@K 133.137375 133.2212 H(7)2H(6)C(7)N(1)O(1) 65.11116 H(3)2H(6)C(3)N(1) Isotopic label 62 0.5 -GIST-Quat:2H(9)@Any_N-term 136.156205 136.2397 H(4)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 63 0.5 -GIST-Quat:2H(9)@K 136.156205 136.2397 H(4)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 63 0.5 -Succinyl@Protein_N-term 100.016044 100.0728 H(4)C(4)O(3) 0.0 Post-translational 64 0.0 -Succinyl@Any_N-term 100.016044 100.0728 H(4)C(4)O(3) 0.0 Isotopic label 64 0.0 -Succinyl@K 100.016044 100.0728 H(4)C(4)O(3) 0.0 Isotopic label 64 0.0 -Succinyl:2H(4)@Any_N-term 104.041151 104.0974 2H(4)C(4)O(3) 0.0 Isotopic label 65 0.0 -Succinyl:2H(4)@K 104.041151 104.0974 2H(4)C(4)O(3) 0.0 Isotopic label 65 0.0 -Succinyl:13C(4)@Any_N-term 104.029463 104.0434 H(4)13C(4)O(3) 0.0 Isotopic label 66 0.0 -Succinyl:13C(4)@K 104.029463 104.0434 H(4)13C(4)O(3) 0.0 Isotopic label 66 0.0 -probiotinhydrazide@P 258.115047 258.3405 H(18)C(10)N(4)O(2)S(1) 0.0 Chemical derivative 357 0.0 -Pro->pyro-Glu@P 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 359 0.0 -His->Asn@H -23.015984 -23.0366 H(-1)C(-2)N(-1)O(1) 0.0 AA substitution 348 0.0 -His->Asp@H -22.031969 -22.0519 H(-2)C(-2)N(-2)O(2) 0.0 AA substitution 349 0.0 -Trp->Hydroxykynurenin@W 19.989829 19.9881 C(-1)O(2) 0.0 Chemical derivative 350 0.0 -Delta:H(4)C(3)@K 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 -Delta:H(4)C(3)@H 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 -Delta:H(4)C(3)@Protein_N-term 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 -Delta:H(4)C(2)@K 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 -Delta:H(4)C(2)@H 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 -Delta:H(4)C(2)@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 -Cys->Dha@C -33.987721 -34.0809 H(-2)S(-1) 0.0 Chemical derivative 368 0.0 -Arg->GluSA@R -43.053433 -43.0711 H(-5)C(-1)N(-3)O(1) 0.0 Chemical derivative 344 0.0 -Trioxidation@Y 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 -Trioxidation@W 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 -Trioxidation@C 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 -Trioxidation@F 47.984744 47.9982 O(3) 0.0 Artefact 345 0.0 -Iminobiotin@Any_N-term 225.093583 225.3106 H(15)C(10)N(3)O(1)S(1) 0.0 Chemical derivative 89 0.0 -Iminobiotin@K 225.093583 225.3106 H(15)C(10)N(3)O(1)S(1) 0.0 Chemical derivative 89 0.0 -ESP@Any_N-term 338.177647 338.4682 H(26)C(16)N(4)O(2)S(1) 0.0 Isotopic label 90 0.0 -ESP@K 338.177647 338.4682 H(26)C(16)N(4)O(2)S(1) 0.0 Isotopic label 90 0.0 -ESP:2H(10)@Any_N-term 348.240414 348.5299 H(16)2H(10)C(16)N(4)O(2)S(1) 0.0 Isotopic label 91 0.0 -ESP:2H(10)@K 348.240414 348.5299 H(16)2H(10)C(16)N(4)O(2)S(1) 0.0 Isotopic label 91 0.0 -NHS-LC-Biotin@Any_N-term 339.161662 339.453 H(25)C(16)N(3)O(3)S(1) 0.0 Chemical derivative 92 0.0 -NHS-LC-Biotin@K 339.161662 339.453 H(25)C(16)N(3)O(3)S(1) 0.0 Chemical derivative 92 0.0 -EDT-maleimide-PEO-biotin@T 601.206246 601.8021 H(39)C(25)N(5)O(6)S(3) 0.0 Chemical derivative 93 0.0 -EDT-maleimide-PEO-biotin@S 601.206246 601.8021 H(39)C(25)N(5)O(6)S(3) 0.0 Chemical derivative 93 0.0 -IMID@K 68.037448 68.0773 H(4)C(3)N(2) 0.0 Isotopic label 94 0.0 -IMID:2H(4)@K 72.062555 72.1019 2H(4)C(3)N(2) 0.0 Isotopic label 95 0.0 -Lysbiotinhydrazide@K 241.088497 241.31 H(15)C(10)N(3)O(2)S(1) 0.0 Chemical derivative 353 0.0 -Propionamide:2H(3)@C 74.055944 74.0964 H(2)2H(3)C(3)N(1)O(1) 0.0 Isotopic label 97 0.0 -Nitro@Y 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Chemical derivative 354 0.0 -Nitro@W 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Chemical derivative 354 0.0 -Nitro@F 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Artefact 354 0.0 -ICAT-C@C 227.126991 227.2603 H(17)C(10)N(3)O(3) 0.0 Isotopic label 105 0.0 -Delta:H(2)C(2)@Protein_N-term 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 -Delta:H(2)C(2)@K 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 -Delta:H(2)C(2)@H 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 -Delta:H(2)C(2)@Any_N-term 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 -Trp->Kynurenin@W 3.994915 3.9887 C(-1)O(1) 0.0 Chemical derivative 351 0.0 -Lys->Allysine@K -1.031634 -1.0311 H(-3)N(-1)O(1) 0.0 Post-translational 352 0.0 -ICAT-C:13C(9)@C 236.157185 236.1942 H(17)C(1)13C(9)N(3)O(3) 0.0 Isotopic label 106 0.0 -FormylMet@Protein_N-term 159.035399 159.2062 H(9)C(6)N(1)O(2)S(1) 0.0 Pre-translational 107 0.0 -Nethylmaleimide@C 125.047679 125.1253 H(7)C(6)N(1)O(2) 0.0 Chemical derivative 108 0.0 -OxLysBiotinRed@K 354.172562 354.4676 H(26)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 112 0.0 -IBTP@C 316.138088 316.3759 H(21)C(22)P(1) 0.0 Chemical derivative 119 0.0 -OxLysBiotin@K 352.156911 352.4518 H(24)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 113 0.0 -OxProBiotinRed@P 371.199111 371.4982 H(29)C(16)N(5)O(3)S(1) 0.0 Chemical derivative 114 0.0 -OxProBiotin@P 369.183461 369.4823 H(27)C(16)N(5)O(3)S(1) 0.0 Chemical derivative 115 0.0 -OxArgBiotin@R 310.135113 310.4118 H(22)C(15)N(2)O(3)S(1) 0.0 Chemical derivative 116 0.0 -OxArgBiotinRed@R 312.150763 312.4277 H(24)C(15)N(2)O(3)S(1) 0.0 Chemical derivative 117 0.0 -EDT-iodoacetyl-PEO-biotin@T 490.174218 490.7034 H(34)C(20)N(4)O(4)S(3) 0.0 Chemical derivative 118 0.0 -EDT-iodoacetyl-PEO-biotin@S 490.174218 490.7034 H(34)C(20)N(4)O(4)S(3) 0.0 Chemical derivative 118 0.0 -GG@C 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 -GG@T 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 -GG@S 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 -GG@K 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 1000000.0 -GG@Protein_N-term 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Post-translational 121 0.0 -Formyl@Protein_N-term 27.994915 28.0101 C(1)O(1) 0.0 Post-translational 122 0.0 -Formyl@T 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 -Formyl@K 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 -Formyl@Any_N-term 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 -Formyl@S 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 -ICAT-H@C 345.097915 345.7754 H(20)C(15)N(1)O(6)Cl(1) 0.0 Isotopic label 123 0.0 -ICAT-H:13C(6)@C 351.118044 351.7313 H(20)C(9)13C(6)N(1)O(6)Cl(1) 0.0 Isotopic label 124 0.0 -Cation:K@Any_C-term 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 -Cation:K@E 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 -Cation:K@D 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 -Xlink:DTSSP[88]@Protein_N-term 87.998285 88.1283 H(4)C(3)O(1)S(1) 0.0 Chemical derivative 126 0.0 -Xlink:DTSSP[88]@K 87.998285 88.1283 H(4)C(3)O(1)S(1) 0.0 Chemical derivative 126 0.0 -Xlink:EGS[226]@K 226.047738 226.1828 H(10)C(10)O(6) 0.0 Chemical derivative 1897 0.0 -Xlink:EGS[226]@Protein_N-term 226.047738 226.1828 H(10)C(10)O(6) 0.0 Chemical derivative 1897 0.0 -Fluoro@Y 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 -Fluoro@W 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 -Fluoro@F 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 -Fluoro@A 17.990578 17.9905 H(-1)F(1) 0.0 Chemical derivative 127 0.0 -Fluorescein@C 387.074287 387.3417 H(13)C(22)N(1)O(6) 0.0 Chemical derivative 128 0.0 -Iodo@H 125.896648 125.8965 H(-1)I(1) 0.0 Chemical derivative 129 0.0 -Iodo@Y 125.896648 125.8965 H(-1)I(1) 0.0 Chemical derivative 129 0.0 -Diiodo@Y 251.793296 251.7931 H(-2)I(2) 0.0 Chemical derivative 130 0.0 -Diiodo@H 251.793296 251.7931 H(-2)I(2) 0.0 Chemical derivative 130 0.0 -Triiodo@Y 377.689944 377.6896 H(-3)I(3) 0.0 Chemical derivative 131 0.0 -Myristoleyl@G^Protein_N-term 208.182715 208.3398 H(24)C(14)O(1) 0.0 Co-translational 134 0.0 -Pro->Pyrrolidinone@P -30.010565 -30.026 H(-2)C(-1)O(-1) 0.0 Chemical derivative 360 0.0 -Myristoyl+Delta:H(-4)@G^Protein_N-term 206.167065 206.3239 H(22)C(14)O(1) 0.0 Co-translational 135 0.0 -Benzoyl@Any_N-term 104.026215 104.1061 H(4)C(7)O(1) 0.0 Isotopic label 136 0.0 -Benzoyl@K 104.026215 104.1061 H(4)C(7)O(1) 0.0 Isotopic label 136 0.0 -Hex(5)HexNAc(2)@N 1216.422863 1217.088 H(76)C(46)N(2)O(35) 1216.422863 H(76)C(46)N(2)O(35) N-linked glycosylation 137 0.5 -Dansyl@Any_N-term 233.051049 233.2862 H(11)C(12)N(1)O(2)S(1) 0.0 Chemical derivative 139 0.0 -Dansyl@K 233.051049 233.2862 H(11)C(12)N(1)O(2)S(1) 0.0 Chemical derivative 139 0.0 -a-type-ion@Any_C-term -46.005479 -46.0254 H(-2)C(-1)O(-2) 0.0 Other 140 0.0 -Amidine@Any_N-term 41.026549 41.0519 H(3)C(2)N(1) 0.0 Chemical derivative 141 0.0 -Amidine@K 41.026549 41.0519 H(3)C(2)N(1) 0.0 Chemical derivative 141 0.0 -HexNAc(1)dHex(1)@T 349.137281 349.3337 H(23)C(14)N(1)O(9) 349.137281 H(23)C(14)N(1)O(9) O-linked glycosylation 142 0.5 -HexNAc(1)dHex(1)@S 349.137281 349.3337 H(23)C(14)N(1)O(9) 349.137281 H(23)C(14)N(1)O(9) O-linked glycosylation 142 0.5 -HexNAc(1)dHex(1)@N 349.137281 349.3337 H(23)C(14)N(1)O(9) 349.137281 H(23)C(14)N(1)O(9) N-linked glycosylation 142 0.5 -HexNAc(2)@T 406.158745 406.385 H(26)C(16)N(2)O(10) 406.158745 H(26)C(16)N(2)O(10) O-linked glycosylation 143 0.5 -HexNAc(2)@S 406.158745 406.385 H(26)C(16)N(2)O(10) 406.158745 H(26)C(16)N(2)O(10) O-linked glycosylation 143 0.5 -HexNAc(2)@N 406.158745 406.385 H(26)C(16)N(2)O(10) 406.158745 H(26)C(16)N(2)O(10) N-linked glycosylation 143 0.5 -Hex(3)@T 486.158471 486.4218 H(30)C(18)O(15) 486.158471 H(30)C(18)O(15) O-linked glycosylation 144 0.5 -Hex(3)@S 486.158471 486.4218 H(30)C(18)O(15) 486.158471 H(30)C(18)O(15) O-linked glycosylation 144 0.5 -Hex(3)@N 486.158471 486.4218 H(30)C(18)O(15) 486.158471 H(30)C(18)O(15) N-linked glycosylation 144 0.5 -HexNAc(1)dHex(2)@N 495.19519 495.4749 H(33)C(20)N(1)O(13) 495.19519 H(33)C(20)N(1)O(13) N-linked glycosylation 145 0.5 -Hex(1)HexNAc(1)dHex(1)@T 511.190105 511.4743 H(33)C(20)N(1)O(14) 511.190105 H(33)C(20)N(1)O(14) O-linked glycosylation 146 0.5 -Hex(1)HexNAc(1)dHex(1)@S 511.190105 511.4743 H(33)C(20)N(1)O(14) 511.190105 H(33)C(20)N(1)O(14) O-linked glycosylation 146 0.5 -Hex(1)HexNAc(1)dHex(1)@N 511.190105 511.4743 H(33)C(20)N(1)O(14) 511.190105 H(33)C(20)N(1)O(14) N-linked glycosylation 146 0.5 -HexNAc(2)dHex(1)@N 552.216654 552.5262 H(36)C(22)N(2)O(14) 552.216654 H(36)C(22)N(2)O(14) N-linked glycosylation 147 0.5 -Hex(1)HexNAc(2)@T 568.211569 568.5256 H(36)C(22)N(2)O(15) 568.211569 H(36)C(22)N(2)O(15) O-linked glycosylation 148 0.5 -Hex(1)HexNAc(2)@S 568.211569 568.5256 H(36)C(22)N(2)O(15) 568.211569 H(36)C(22)N(2)O(15) O-linked glycosylation 148 0.5 -Hex(1)HexNAc(2)@N 568.211569 568.5256 H(36)C(22)N(2)O(15) 568.211569 H(36)C(22)N(2)O(15) N-linked glycosylation 148 0.5 -Hex(1)HexNAc(1)NeuAc(1)@T 656.227613 656.5877 H(40)C(25)N(2)O(18) 656.227613 H(40)C(25)N(2)O(18) O-linked glycosylation 149 0.5 -Hex(1)HexNAc(1)NeuAc(1)@S 656.227613 656.5877 H(40)C(25)N(2)O(18) 656.227613 H(40)C(25)N(2)O(18) O-linked glycosylation 149 0.5 -Hex(1)HexNAc(1)NeuAc(1)@N 656.227613 656.5877 H(40)C(25)N(2)O(18) 656.227613 H(40)C(25)N(2)O(18) N-linked glycosylation 149 0.5 -HexNAc(2)dHex(2)@N 698.274563 698.6674 H(46)C(28)N(2)O(18) 698.274563 H(46)C(28)N(2)O(18) N-linked glycosylation 150 0.5 -Hex(1)HexNAc(2)Pent(1)@N 700.253828 700.6403 H(44)C(27)N(2)O(19) 700.253828 H(44)C(27)N(2)O(19) N-linked glycosylation 151 0.5 -Hex(1)HexNAc(2)dHex(1)@T 714.269478 714.6668 H(46)C(28)N(2)O(19) 714.269478 H(46)C(28)N(2)O(19) O-linked glycosylation 152 0.5 -Hex(1)HexNAc(2)dHex(1)@S 714.269478 714.6668 H(46)C(28)N(2)O(19) 714.269478 H(46)C(28)N(2)O(19) O-linked glycosylation 152 0.5 -Hex(1)HexNAc(2)dHex(1)@N 714.269478 714.6668 H(46)C(28)N(2)O(19) 714.269478 H(46)C(28)N(2)O(19) N-linked glycosylation 152 0.5 -Hex(2)HexNAc(2)@T 730.264392 730.6662 H(46)C(28)N(2)O(20) 730.264392 H(46)C(28)N(2)O(20) O-linked glycosylation 153 0.5 -Hex(2)HexNAc(2)@S 730.264392 730.6662 H(46)C(28)N(2)O(20) 730.264392 H(46)C(28)N(2)O(20) O-linked glycosylation 153 0.5 -Hex(2)HexNAc(2)@N 730.264392 730.6662 H(46)C(28)N(2)O(20) 730.264392 H(46)C(28)N(2)O(20) N-linked glycosylation 153 0.5 -Hex(3)HexNAc(1)Pent(1)@N 821.280102 821.7289 H(51)C(31)N(1)O(24) 821.280102 H(51)C(31)N(1)O(24) N-linked glycosylation 154 0.5 -Hex(1)HexNAc(2)dHex(1)Pent(1)@N 846.311736 846.7815 H(54)C(33)N(2)O(23) 846.311736 H(54)C(33)N(2)O(23) N-linked glycosylation 155 0.5 -Hex(1)HexNAc(2)dHex(2)@T 860.327386 860.808 H(56)C(34)N(2)O(23) 860.327386 H(56)C(34)N(2)O(23) O-linked glycosylation 156 0.5 -Hex(1)HexNAc(2)dHex(2)@S 860.327386 860.808 H(56)C(34)N(2)O(23) 860.327386 H(56)C(34)N(2)O(23) O-linked glycosylation 156 0.5 -Hex(1)HexNAc(2)dHex(2)@N 860.327386 860.808 H(56)C(34)N(2)O(23) 860.327386 H(56)C(34)N(2)O(23) N-linked glycosylation 156 0.5 -Hex(2)HexNAc(2)Pent(1)@N 862.306651 862.7809 H(54)C(33)N(2)O(24) 862.306651 H(54)C(33)N(2)O(24) N-linked glycosylation 157 0.5 -Hex(2)HexNAc(2)dHex(1)@T 876.322301 876.8074 H(56)C(34)N(2)O(24) 876.322301 H(56)C(34)N(2)O(24) O-linked glycosylation 158 0.5 -Hex(2)HexNAc(2)dHex(1)@S 876.322301 876.8074 H(56)C(34)N(2)O(24) 876.322301 H(56)C(34)N(2)O(24) O-linked glycosylation 158 0.5 -Hex(2)HexNAc(2)dHex(1)@N 876.322301 876.8074 H(56)C(34)N(2)O(24) 876.322301 H(56)C(34)N(2)O(24) N-linked glycosylation 158 0.5 -Hex(3)HexNAc(2)@T 892.317216 892.8068 H(56)C(34)N(2)O(25) 892.317216 H(56)C(34)N(2)O(25) O-linked glycosylation 159 0.5 -Hex(3)HexNAc(2)@S 892.317216 892.8068 H(56)C(34)N(2)O(25) 892.317216 H(56)C(34)N(2)O(25) O-linked glycosylation 159 0.5 -Hex(3)HexNAc(2)@N 892.317216 892.8068 H(56)C(34)N(2)O(25) 892.317216 H(56)C(34)N(2)O(25) N-linked glycosylation 159 0.5 -Hex(1)HexNAc(1)NeuAc(2)@T 947.323029 947.8423 H(57)C(36)N(3)O(26) 947.323029 H(57)C(36)N(3)O(26) O-linked glycosylation 160 0.5 -Hex(1)HexNAc(1)NeuAc(2)@S 947.323029 947.8423 H(57)C(36)N(3)O(26) 947.323029 H(57)C(36)N(3)O(26) O-linked glycosylation 160 0.5 -Hex(1)HexNAc(1)NeuAc(2)@N 947.323029 947.8423 H(57)C(36)N(3)O(26) 947.323029 H(57)C(36)N(3)O(26) N-linked glycosylation 160 0.5 -Hex(3)HexNAc(2)Phos(1)@N 972.283547 972.7867 H(57)C(34)N(2)O(28)P(1) 972.283547 H(57)C(34)N(2)O(28)P(1) N-linked glycosylation 161 0.5 -Delta:S(-1)Se(1)@M 47.944449 46.895 S(-1)Se(1) 0.0 Non-standard residue 162 0.0 -Delta:S(-1)Se(1)@C 47.944449 46.895 S(-1)Se(1) 0.0 Non-standard residue 162 0.0 -NBS:13C(6)@W 159.008578 159.1144 H(3)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 171 0.0 -Methyl:2H(3)13C(1)@K 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 -Methyl:2H(3)13C(1)@R 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 -Methyl:2H(3)13C(1)@Any_N-term 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 -Dimethyl:2H(6)13C(2)@Protein_N-term 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 -Dimethyl:2H(6)13C(2)@Any_N-term 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 -Dimethyl:2H(6)13C(2)@R 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 -Dimethyl:2H(6)13C(2)@K 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 -NBS@W 152.988449 153.1585 H(3)C(6)N(1)O(2)S(1) 0.0 Chemical derivative 172 0.0 -Delta:H(1)N(-1)18O(1)@N 2.988261 2.9845 H(-1)N(-1)18O(1) 0.0 Isotopic label 170 0.0 -QAT@C 171.149738 171.26 H(19)C(9)N(2)O(1) 0.0 Chemical derivative 195 0.0 -BHT@H 218.167065 218.3346 H(22)C(15)O(1) 0.0 Other 176 0.0 -BHT@K 218.167065 218.3346 H(22)C(15)O(1) 0.0 Other 176 0.0 -BHT@C 218.167065 218.3346 H(22)C(15)O(1) 0.0 Other 176 0.0 -Delta:H(4)C(2)O(-1)S(1)@S 44.008456 44.1188 H(4)C(2)O(-1)S(1) 0.0 Chemical derivative 327 0.0 -DAET@T 87.050655 87.1866 H(9)C(4)N(1)O(-1)S(1) 0.0 Chemical derivative 178 0.0 -DAET@S 87.050655 87.1866 H(9)C(4)N(1)O(-1)S(1) 0.0 Chemical derivative 178 0.0 -Pro->Pyrrolidone@P -27.994915 -28.0101 C(-1)O(-1) 0.0 Chemical derivative 369 0.0 -Label:13C(9)@Y 9.030193 8.9339 C(-9)13C(9) 0.0 Isotopic label 184 0.0 -Label:13C(9)@F 9.030193 8.9339 C(-9)13C(9) 0.0 Isotopic label 184 0.0 -Label:13C(9)+Phospho@Y 88.996524 88.9138 H(1)C(-9)13C(9)O(3)P(1) 0.0 Isotopic label 185 0.0 -Label:13C(6)@I 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 -Label:13C(6)@L 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 -Label:13C(6)@K 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 -Label:13C(6)@R 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 -HPG@R 132.021129 132.1162 H(4)C(8)O(2) 0.0 Chemical derivative 186 0.0 -2HPG@R 282.052824 282.2476 H(10)C(16)O(5) 0.0 Chemical derivative 187 0.0 -QAT:2H(3)@C 174.168569 174.2784 H(16)2H(3)C(9)N(2)O(1) 0.0 Isotopic label 196 0.0 -Label:18O(2)@Any_C-term 4.008491 3.9995 O(-2)18O(2) 0.0 Isotopic label 193 0.0 -AccQTag@Any_N-term 170.048013 170.1674 H(6)C(10)N(2)O(1) 0.0 Chemical derivative 194 0.0 -AccQTag@K 170.048013 170.1674 H(6)C(10)N(2)O(1) 0.0 Chemical derivative 194 0.0 -Dimethyl:2H(4)@Protein_N-term 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 -Dimethyl:2H(4)@Any_N-term 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 -Dimethyl:2H(4)@K 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 -Dimethyl:2H(4)@R 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 -EQAT@C 184.157563 184.2786 H(20)C(10)N(2)O(1) 0.0 Chemical derivative 197 0.0 -EQAT:2H(5)@C 189.188947 189.3094 H(15)2H(5)C(10)N(2)O(1) 0.0 Isotopic label 198 0.0 -Ethanedithiol@T 75.980527 76.1838 H(4)C(2)O(-1)S(2) 0.0 Chemical derivative 200 0.0 -Ethanedithiol@S 75.980527 76.1838 H(4)C(2)O(-1)S(2) 0.0 Chemical derivative 200 0.0 -NEIAA:2H(5)@Y 90.084148 90.1353 H(2)2H(5)C(4)N(1)O(1) 0.0 Isotopic label 212 0.0 -NEIAA:2H(5)@C 90.084148 90.1353 H(2)2H(5)C(4)N(1)O(1) 0.0 Isotopic label 212 0.0 -Delta:H(6)C(6)O(1)@K 94.041865 94.1112 H(6)C(6)O(1) 0.0 Other 205 0.0 -Delta:H(4)C(3)O(1)@K 56.026215 56.0633 H(4)C(3)O(1) 0.0 Other 206 0.0 -Delta:H(4)C(3)O(1)@H 56.026215 56.0633 H(4)C(3)O(1) 0.0 Other 206 0.0 -Delta:H(4)C(3)O(1)@C 56.026215 56.0633 H(4)C(3)O(1) 0.0 Other 206 0.0 -Delta:H(4)C(3)O(1)@R 56.026215 56.0633 H(4)C(3)O(1) 0.0 Artefact 206 0.0 -Delta:H(2)C(3)@K 38.01565 38.048 H(2)C(3) 0.0 Other 207 0.0 -Delta:H(4)C(6)@K 76.0313 76.096 H(4)C(6) 0.0 Other 208 0.0 -Delta:H(8)C(6)O(2)@K 112.05243 112.1265 H(8)C(6)O(2) 0.0 Other 209 0.0 -ADP-Ribosyl@D 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 -ADP-Ribosyl@K 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 -ADP-Ribosyl@E 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 -ADP-Ribosyl@T 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 541.06111 H(21)C(15)N(5)O(13)P(2) O-linked glycosylation 213 0.5 -ADP-Ribosyl@S 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 541.06111 H(21)C(15)N(5)O(13)P(2) O-linked glycosylation 213 0.5 -ADP-Ribosyl@C 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 -ADP-Ribosyl@N 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 541.06111 H(21)C(15)N(5)O(13)P(2) N-linked glycosylation 213 0.5 -ADP-Ribosyl@R 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 -NEIAA@Y 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Isotopic label 211 0.0 -NEIAA@C 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Isotopic label 211 0.0 -iTRAQ4plex@C 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@T 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@Protein_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@S 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@H 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@Y 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@Any_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -iTRAQ4plex@K 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 -Crotonaldehyde@K 70.041865 70.0898 H(6)C(4)O(1) 0.0 Other 253 0.0 -Crotonaldehyde@H 70.041865 70.0898 H(6)C(4)O(1) 0.0 Other 253 0.0 -Crotonaldehyde@C 70.041865 70.0898 H(6)C(4)O(1) 0.0 Other 253 0.0 -Bromo@F 77.910511 78.8961 H(-1)Br(1) 0.0 Post-translational 340 0.0 -Bromo@H 77.910511 78.8961 H(-1)Br(1) 0.0 Post-translational 340 0.0 -Bromo@W 77.910511 78.8961 H(-1)Br(1) 0.0 Post-translational 340 0.0 -Bromo@Y 77.910511 78.8961 H(-1)Br(1) 0.0 Artefact 340 0.0 -Amino@Y 15.010899 15.0146 H(1)N(1) 0.0 Chemical derivative 342 0.0 -Argbiotinhydrazide@R 199.066699 199.27 H(13)C(9)N(1)O(2)S(1) 0.0 Chemical derivative 343 0.0 -Label:18O(1)@Y 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 -Label:18O(1)@T 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 -Label:18O(1)@S 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 -Label:18O(1)@Any_C-term 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 -Label:13C(6)15N(2)@K 8.014199 7.9427 C(-6)13C(6)N(-2)15N(2) 0.0 Isotopic label 259 0.0 -Thiophospho@Y 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 -Thiophospho@T 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 -Thiophospho@S 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 -SPITC@K 214.971084 215.2495 H(5)C(7)N(1)O(3)S(2) 0.0 Chemical derivative 261 0.0 -SPITC@Any_N-term 214.971084 215.2495 H(5)C(7)N(1)O(3)S(2) 0.0 Chemical derivative 261 0.0 -IGBP@C 296.016039 297.1478 H(13)C(12)N(2)O(2)Br(1) 0.0 Isotopic label 243 0.0 -Cytopiloyne@Y 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 -Cytopiloyne@S 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 -Cytopiloyne@R 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 -Cytopiloyne@P 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 -Cytopiloyne@Any_N-term 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 -Cytopiloyne@K 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 -Cytopiloyne@C 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 -Cytopiloyne+water@Y 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 -Cytopiloyne+water@T 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 -Cytopiloyne+water@S 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 -Cytopiloyne+water@R 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 -Cytopiloyne+water@Any_N-term 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 -Cytopiloyne+water@K 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 -Cytopiloyne+water@C 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 -Label:13C(6)15N(4)@R 10.008269 9.9296 C(-6)13C(6)N(-4)15N(4) 0.0 Isotopic label 267 0.0 -Label:13C(9)15N(1)@F 10.027228 9.9273 C(-9)13C(9)N(-1)15N(1) 0.0 Isotopic label 269 0.0 -Label:2H(3)@L 3.01883 3.0185 H(-3)2H(3) 0.0 Isotopic label 262 0.0 -Label:2H(3)@M 3.01883 3.0185 H(-3)2H(3) 0.0 Isotopic label 262 0.0 -Label:13C(5)15N(1)@M 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 -Label:13C(5)15N(1)@P 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 -Label:13C(5)15N(1)@V 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 -Label:13C(5)15N(1)@E 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 -PET@T 121.035005 121.2028 H(7)C(7)N(1)O(-1)S(1) 0.0 Chemical derivative 264 0.0 -PET@S 121.035005 121.2028 H(7)C(7)N(1)O(-1)S(1) 0.0 Chemical derivative 264 0.0 -CAF@Any_N-term 135.983029 136.1265 H(4)C(3)O(4)S(1) 0.0 Chemical derivative 272 0.0 -Xlink:BS2G[96]@Protein_N-term 96.021129 96.0841 H(4)C(5)O(2) 0.0 Chemical derivative 1905 0.0 -Xlink:BS2G[96]@K 96.021129 96.0841 H(4)C(5)O(2) 0.0 Chemical derivative 1905 0.0 -Nitrosyl@C 28.990164 28.9982 H(-1)N(1)O(1) 0.0 Post-translational 275 0.0 -Nitrosyl@Y 28.990164 28.9982 H(-1)N(1)O(1) 0.0 Chemical derivative 275 0.0 -Kdo@T 220.058303 220.1767 H(12)C(8)O(7) 220.058303 H(12)C(8)O(7) O-linked glycosylation 2022 0.5 -Kdo@S 220.058303 220.1767 H(12)C(8)O(7) 220.058303 H(12)C(8)O(7) O-linked glycosylation 2022 0.5 -AEBS@Y 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 -AEBS@S 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 -AEBS@Protein_N-term 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 -AEBS@K 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 -AEBS@H 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 -Ethanolyl@K 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 -Ethanolyl@C 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 -Ethanolyl@R 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 -Label:13C(6)15N(2)+Dimethyl@K 36.045499 35.9959 H(4)C(-4)13C(6)N(-2)15N(2) 0.0 Isotopic label 987 0.0 -HMVK@C 86.036779 86.0892 H(6)C(4)O(2) 0.0 Chemical derivative 371 0.0 -Ethyl@Any_C-term 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 -Ethyl@Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 -Ethyl@E 28.0313 28.0532 H(4)C(2) 0.0 Artefact 280 0.0 -Ethyl@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Multiple 280 0.0 -Ethyl@K 28.0313 28.0532 H(4)C(2) 0.0 Multiple 280 0.0 -Ethyl@D 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 -CoenzymeA@C 765.09956 765.5182 H(34)C(21)N(7)O(16)P(3)S(1) 0.0 Post-translational 281 0.0 -Methyl+Deamidated@Q 14.999666 15.0113 H(1)C(1)N(-1)O(1) 0.0 Post-translational 528 0.0 -Methyl+Deamidated@N 14.999666 15.0113 H(1)C(1)N(-1)O(1) 0.0 Chemical derivative 528 0.0 -Delta:H(5)C(2)@P 29.039125 29.0611 H(5)C(2) 0.0 Post-translational 529 0.0 -Methyl:2H(2)@K 16.028204 16.0389 2H(2)C(1) 0.0 Isotopic label 284 0.0 -Methyl:2H(2)@Any_N-term 16.028204 16.0389 2H(2)C(1) 0.0 Isotopic label 284 0.0 -SulfanilicAcid@E 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 -SulfanilicAcid@D 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 -SulfanilicAcid@Any_C-term 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 -SulfanilicAcid:13C(6)@E 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 -SulfanilicAcid:13C(6)@D 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 -SulfanilicAcid:13C(6)@Any_C-term 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 -Biotin-PEO-Amine@D 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 -Biotin-PEO-Amine@Protein_C-term 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 -Biotin-PEO-Amine@E 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 -Trp->Oxolactone@W 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 288 0.0 -Biotin-HPDP@C 428.191582 428.6124 H(32)C(19)N(4)O(3)S(2) 0.0 Chemical derivative 290 0.0 -Delta:Hg(1)@C 201.970617 200.59 Hg(1) 0.0 Chemical derivative 291 0.0 -IodoU-AMP@Y 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 -IodoU-AMP@W 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 -IodoU-AMP@F 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 -CAMthiopropanoyl@Protein_N-term 145.019749 145.1796 H(7)C(5)N(1)O(2)S(1) 0.0 Chemical derivative 293 0.0 -CAMthiopropanoyl@K 145.019749 145.1796 H(7)C(5)N(1)O(2)S(1) 0.0 Chemical derivative 293 0.0 -IED-Biotin@C 326.141261 326.4145 H(22)C(14)N(4)O(3)S(1) 0.0 Chemical derivative 294 0.0 -dHex@N 146.057909 146.1412 H(10)C(6)O(4) 146.057909 H(10)C(6)O(4) N-linked glycosylation 295 0.5 -dHex@T 146.057909 146.1412 H(10)C(6)O(4) 146.057909 H(10)C(6)O(4) O-linked glycosylation 295 0.5 -dHex@S 146.057909 146.1412 H(10)C(6)O(4) 146.057909 H(10)C(6)O(4) O-linked glycosylation 295 0.5 -Methyl:2H(3)@Anywhere 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 -Methyl:2H(3)@D 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 -Methyl:2H(3)@E 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 -Methyl:2H(3)@K 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 -Methyl:2H(3)@R 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 -Carboxy@E 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 -Carboxy@D 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 -Carboxy@K 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 -Carboxy@W 43.989829 44.0095 C(1)O(2) 0.0 Chemical derivative 299 0.0 -Carboxy@M^Protein_N-term 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 -Bromobimane@C 190.074228 190.1986 H(10)C(10)N(2)O(2) 0.0 Chemical derivative 301 0.0 -Menadione@K 170.036779 170.1641 H(6)C(11)O(2) 0.0 Chemical derivative 302 0.0 -Menadione@C 170.036779 170.1641 H(6)C(11)O(2) 0.0 Chemical derivative 302 0.0 -DeStreak@C 75.998285 76.1176 H(4)C(2)O(1)S(1) 0.0 Chemical derivative 303 0.0 -dHex(1)Hex(3)HexNAc(4)@T 1444.53387 1445.3331 H(92)C(56)N(4)O(39) 1444.53387 H(92)C(56)N(4)O(39) O-linked glycosylation 305 0.5 -dHex(1)Hex(3)HexNAc(4)@S 1444.53387 1445.3331 H(92)C(56)N(4)O(39) 1444.53387 H(92)C(56)N(4)O(39) O-linked glycosylation 305 0.5 -dHex(1)Hex(3)HexNAc(4)@N 1444.53387 1445.3331 H(92)C(56)N(4)O(39) 1444.53387 H(92)C(56)N(4)O(39) N-linked glycosylation 305 0.5 -dHex(1)Hex(4)HexNAc(4)@T 1606.586693 1607.4737 H(102)C(62)N(4)O(44) 1606.586693 H(102)C(62)N(4)O(44) O-linked glycosylation 307 0.5 -dHex(1)Hex(4)HexNAc(4)@S 1606.586693 1607.4737 H(102)C(62)N(4)O(44) 1606.586693 H(102)C(62)N(4)O(44) O-linked glycosylation 307 0.5 -dHex(1)Hex(4)HexNAc(4)@N 1606.586693 1607.4737 H(102)C(62)N(4)O(44) 1606.586693 H(102)C(62)N(4)O(44) N-linked glycosylation 307 0.5 -dHex(1)Hex(5)HexNAc(4)@N 1768.639517 1769.6143 H(112)C(68)N(4)O(49) 1768.639517 H(112)C(68)N(4)O(49) N-linked glycosylation 308 0.5 -Hex(3)HexNAc(4)@T 1298.475961 1299.1919 H(82)C(50)N(4)O(35) 1298.475961 H(82)C(50)N(4)O(35) O-linked glycosylation 309 0.5 -Hex(3)HexNAc(4)@S 1298.475961 1299.1919 H(82)C(50)N(4)O(35) 1298.475961 H(82)C(50)N(4)O(35) O-linked glycosylation 309 0.5 -Hex(3)HexNAc(4)@N 1298.475961 1299.1919 H(82)C(50)N(4)O(35) 1298.475961 H(82)C(50)N(4)O(35) N-linked glycosylation 309 0.5 -Hex(4)HexNAc(4)@T 1460.528784 1461.3325 H(92)C(56)N(4)O(40) 1460.528784 H(92)C(56)N(4)O(40) O-linked glycosylation 310 0.5 -Hex(4)HexNAc(4)@S 1460.528784 1461.3325 H(92)C(56)N(4)O(40) 1460.528784 H(92)C(56)N(4)O(40) O-linked glycosylation 310 0.5 -Hex(4)HexNAc(4)@N 1460.528784 1461.3325 H(92)C(56)N(4)O(40) 1460.528784 H(92)C(56)N(4)O(40) N-linked glycosylation 310 0.5 -Hex(5)HexNAc(4)@S 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C(62)N(4)O(45) O-linked glycosylation 311 0.5 -Hex(5)HexNAc(4)@N 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C(62)N(4)O(45) N-linked glycosylation 311 0.5 -Hex(5)HexNAc(4)@T 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C(62)N(4)O(45) O-linked glycosylation 311 0.5 -Cysteinyl@C 119.004099 119.1423 H(5)C(3)N(1)O(2)S(1) 0.0 Multiple 312 0.0 -Lys-loss@K^Protein_C-term -128.094963 -128.1723 H(-12)C(-6)N(-2)O(-1) 0.0 Post-translational 313 0.0 -Nmethylmaleimide@K 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 314 0.0 -Nmethylmaleimide@C 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 314 0.0 -CyDye-Cy3@C 672.298156 672.8335 H(44)C(37)N(4)O(6)S(1) 0.0 Chemical derivative 494 0.0 -DimethylpyrroleAdduct@K 78.04695 78.1118 H(6)C(6) 0.0 Chemical derivative 316 0.0 -Delta:H(2)C(5)@K 62.01565 62.0694 H(2)C(5) 0.0 Chemical derivative 318 0.0 -Delta:H(2)C(3)O(1)@K 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 319 0.0 -Delta:H(2)C(3)O(1)@R 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 319 0.0 -Nethylmaleimide+water@K 143.058243 143.1406 H(9)C(6)N(1)O(3) 0.0 Chemical derivative 320 0.0 -Nethylmaleimide+water@C 143.058243 143.1406 H(9)C(6)N(1)O(3) 0.0 Chemical derivative 320 0.0 -Methyl+Acetyl:2H(3)@K 59.045045 59.0817 H(1)2H(3)C(3)O(1) 0.0 Isotopic label 768 0.0 -Xlink:B10621@C 713.093079 713.5626 H(30)C(31)N(4)O(6)S(1)I(1) 0.0 Chemical derivative 323 0.0 -Xlink:DTBP[87]@Protein_N-term 87.01427 87.1435 H(5)C(3)N(1)S(1) 0.0 Chemical derivative 324 0.0 -Xlink:DTBP[87]@K 87.01427 87.1435 H(5)C(3)N(1)S(1) 0.0 Chemical derivative 324 0.0 -FP-Biotin@K 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 -FP-Biotin@T 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 -FP-Biotin@Y 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 -FP-Biotin@S 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 -Thiophos-S-S-biotin@Y 525.142894 525.6658 H(34)C(19)N(4)O(5)P(1)S(3) 525.142894 H(34)C(19)N(4)O(5)P(1)S(3) Chemical derivative 332 0.5 -Thiophos-S-S-biotin@T 525.142894 525.6658 H(34)C(19)N(4)O(5)P(1)S(3) 525.142894 H(34)C(19)N(4)O(5)P(1)S(3) Chemical derivative 332 0.5 -Thiophos-S-S-biotin@S 525.142894 525.6658 H(34)C(19)N(4)O(5)P(1)S(3) 525.142894 H(34)C(19)N(4)O(5)P(1)S(3) Chemical derivative 332 0.5 -Can-FP-biotin@T 447.195679 447.5291 H(34)C(19)N(3)O(5)P(1)S(1) 0.0 Chemical derivative 333 0.0 -Can-FP-biotin@Y 447.195679 447.5291 H(34)C(19)N(3)O(5)P(1)S(1) 0.0 Chemical derivative 333 0.0 -Can-FP-biotin@S 447.195679 447.5291 H(34)C(19)N(3)O(5)P(1)S(1) 0.0 Chemical derivative 333 0.0 -HNE+Delta:H(2)@K 158.13068 158.238 H(18)C(9)O(2) 0.0 Chemical derivative 335 0.0 -HNE+Delta:H(2)@H 158.13068 158.238 H(18)C(9)O(2) 0.0 Chemical derivative 335 0.0 -HNE+Delta:H(2)@C 158.13068 158.238 H(18)C(9)O(2) 0.0 Chemical derivative 335 0.0 -Thrbiotinhydrazide@T 240.104482 240.3252 H(16)C(10)N(4)O(1)S(1) 0.0 Chemical derivative 361 0.0 -Methylamine@T 13.031634 13.0418 H(3)C(1)N(1)O(-1) 0.0 Artefact 337 0.0 -Methylamine@S 13.031634 13.0418 H(3)C(1)N(1)O(-1) 0.0 Artefact 337 0.0 -Diisopropylphosphate@K 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 -Diisopropylphosphate@Y 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 -Diisopropylphosphate@T 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 -Diisopropylphosphate@S 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 -Diisopropylphosphate@Any_N-term 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 -Isopropylphospho@Y 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 -Isopropylphospho@T 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 -Isopropylphospho@S 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 -ICPL:13C(6)@Any_N-term 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 -ICPL:13C(6)@Protein_N-term 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 -ICPL:13C(6)@K 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 -CarbamidomethylDTT@C 209.018035 209.2864 H(11)C(6)N(1)O(3)S(2) 0.0 Artefact 893 0.0 -ICPL@Protein_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 -ICPL@K 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 -ICPL@Any_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 -Deamidated:18O(1)@Q 2.988261 2.9845 H(-1)N(-1)18O(1) 0.0 Isotopic label 366 0.0 -Deamidated:18O(1)@N 2.988261 2.9845 H(-1)N(-1)18O(1) 0.0 Isotopic label 366 0.0 -Arg->Orn@R -42.021798 -42.04 H(-2)C(-1)N(-2) 0.0 Artefact 372 0.0 -Cation:Cu[I]@Any_C-term 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 -Cation:Cu[I]@E 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 -Cation:Cu[I]@D 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 -Cation:Cu[I]@H 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 -Dehydro@C -1.007825 -1.0079 H(-1) 0.0 Multiple 374 0.0 -Diphthamide@H 142.110613 142.1989 H(14)C(7)N(2)O(1) 0.0 Post-translational 375 0.0 -Hydroxyfarnesyl@C 220.182715 220.3505 H(24)C(15)O(1) 0.0 Post-translational 376 0.0 -Diacylglycerol@C 576.511761 576.9334 H(68)C(37)O(4) 0.0 Post-translational 377 0.0 -Carboxyethyl@K 72.021129 72.0627 H(4)C(3)O(2) 0.0 Post-translational 378 0.0 -Carboxyethyl@H 72.021129 72.0627 H(4)C(3)O(2) 0.0 Chemical derivative 378 0.0 -Hypusine@K 87.068414 87.1204 H(9)C(4)N(1)O(1) 0.0 Post-translational 379 0.0 -Retinylidene@K 266.203451 266.4204 H(26)C(20) 0.0 Post-translational 380 0.0 -Lys->AminoadipicAcid@K 14.96328 14.9683 H(-3)N(-1)O(2) 0.0 Post-translational 381 0.0 -Cys->PyruvicAcid@C^Protein_N-term -33.003705 -33.0961 H(-3)N(-1)O(1)S(-1) 0.0 Post-translational 382 0.0 -Ammonia-loss@C^Any_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Artefact 385 0.0 -Ammonia-loss@S^Protein_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Post-translational 385 0.0 -Ammonia-loss@T^Protein_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Post-translational 385 0.0 -Ammonia-loss@N -17.026549 -17.0305 H(-3)N(-1) 0.0 Chemical derivative 385 0.0 -Phycocyanobilin@C 586.279135 586.678 H(38)C(33)N(4)O(6) 0.0 Post-translational 387 0.0 -Phycoerythrobilin@C 588.294785 588.6939 H(40)C(33)N(4)O(6) 0.0 Post-translational 388 0.0 -Phytochromobilin@C 584.263485 584.6621 H(36)C(33)N(4)O(6) 0.0 Post-translational 389 0.0 -Heme@H 616.177295 616.4873 H(32)C(34)N(4)O(4)Fe(1) 0.0 Post-translational 390 0.0 -Heme@C 616.177295 616.4873 H(32)C(34)N(4)O(4)Fe(1) 0.0 Post-translational 390 0.0 -Molybdopterin@C 521.884073 520.2668 H(11)C(10)N(5)O(8)P(1)S(2)Mo(1) 0.0 Post-translational 391 0.0 -Quinone@W 29.974179 29.9829 H(-2)O(2) 0.0 Post-translational 392 0.0 -Quinone@Y 29.974179 29.9829 H(-2)O(2) 0.0 Post-translational 392 0.0 -Glucosylgalactosyl@K 340.100562 340.2806 H(20)C(12)O(11) 340.100562 H(20)C(12)O(11) Other glycosylation 393 0.5 -GPIanchor@Protein_C-term 123.00853 123.0477 H(6)C(2)N(1)O(3)P(1) 0.0 Post-translational 394 0.0 -PhosphoribosyldephosphoCoA@S 881.146904 881.6335 H(42)C(26)N(7)O(19)P(3)S(1) 0.0 Post-translational 395 0.0 -GlycerylPE@E 197.04531 197.1262 H(12)C(5)N(1)O(5)P(1) 0.0 Post-translational 396 0.0 -Triiodothyronine@Y 469.716159 469.785 H(1)C(6)O(1)I(3) 0.0 Post-translational 397 0.0 -Thyroxine@Y 595.612807 595.6815 C(6)O(1)I(4) 0.0 Post-translational 398 0.0 -Tyr->Dha@Y -94.041865 -94.1112 H(-6)C(-6)O(-1) 0.0 Post-translational 400 0.0 -Didehydro@S -2.01565 -2.0159 H(-2) 0.0 Post-translational 401 0.0 -Didehydro@Y -2.01565 -2.0159 H(-2) 0.0 Post-translational 401 0.0 -Didehydro@T -2.01565 -2.0159 H(-2) 0.0 Chemical derivative 401 0.0 -Didehydro@K^Any_C-term -2.01565 -2.0159 H(-2) 0.0 Artefact 401 0.0 -Cys->Oxoalanine@C -17.992806 -18.0815 H(-2)O(1)S(-1) 0.0 Post-translational 402 0.0 -Ser->LacticAcid@S^Protein_N-term -15.010899 -15.0146 H(-1)N(-1) 0.0 Post-translational 403 0.0 -GluGlu@E 258.085186 258.228 H(14)C(10)N(2)O(6) 0.0 Post-translational 451 0.0 -GluGlu@Protein_C-term 258.085186 258.228 H(14)C(10)N(2)O(6) 0.0 Post-translational 451 0.0 -Phosphoadenosine@H 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 -Phosphoadenosine@T 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 -Phosphoadenosine@K 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 -Phosphoadenosine@Y 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 -Phosphoadenosine@S 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 -Glu@E 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Post-translational 450 0.0 -Glu@Protein_C-term 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Chemical derivative 450 0.0 -Hydroxycinnamyl@C 146.036779 146.1427 H(6)C(9)O(2) 0.0 Post-translational 407 0.0 -Glycosyl@P 148.037173 148.114 H(8)C(5)O(5) 0.0 Other glycosylation 408 0.0 -FMNH@H 454.088965 454.3279 H(19)C(17)N(4)O(9)P(1) 0.0 Post-translational 409 0.0 -FMNH@C 454.088965 454.3279 H(19)C(17)N(4)O(9)P(1) 0.0 Post-translational 409 0.0 -Archaeol@C 634.662782 635.1417 H(86)C(43)O(2) 0.0 Post-translational 410 0.0 -Phenylisocyanate@Any_N-term 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 411 0.0 -Phenylisocyanate:2H(5)@Any_N-term 124.068498 124.1515 2H(5)C(7)N(1)O(1) 0.0 Chemical derivative 412 0.0 -Phosphoguanosine@H 345.047435 345.2053 H(12)C(10)N(5)O(7)P(1) 0.0 Post-translational 413 0.0 -Phosphoguanosine@K 345.047435 345.2053 H(12)C(10)N(5)O(7)P(1) 0.0 Post-translational 413 0.0 -Hydroxymethyl@N 30.010565 30.026 H(2)C(1)O(1) 0.0 Post-translational 414 0.0 -MolybdopterinGD+Delta:S(-1)Se(1)@C 1620.930224 1618.9096 H(47)C(40)N(20)O(26)P(4)S(3)Se(1)Mo(1) 0.0 Post-translational 415 0.0 -Dipyrrolylmethanemethyl@C 418.137616 418.3973 H(22)C(20)N(2)O(8) 0.0 Post-translational 416 0.0 -PhosphoUridine@H 306.025302 306.166 H(11)C(9)N(2)O(8)P(1) 0.0 Post-translational 417 0.0 -PhosphoUridine@Y 306.025302 306.166 H(11)C(9)N(2)O(8)P(1) 0.0 Post-translational 417 0.0 -Glycerophospho@S 154.00311 154.0584 H(7)C(3)O(5)P(1) 0.0 Post-translational 419 0.0 -Carboxy->Thiocarboxy@G^Protein_C-term 15.977156 16.0656 O(-1)S(1) 0.0 Post-translational 420 0.0 -Sulfide@D 31.972071 32.065 S(1) 0.0 Post-translational 421 0.0 -Sulfide@C 31.972071 32.065 S(1) 0.0 Post-translational 421 0.0 -Sulfide@W 31.972071 32.065 S(1) 0.0 Chemical derivative 421 0.0 -PyruvicAcidIminyl@K 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 -PyruvicAcidIminyl@V^Protein_N-term 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 -PyruvicAcidIminyl@C^Protein_N-term 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 -Delta:Se(1)@C 79.91652 78.96 Se(1) 0.0 Post-translational 423 0.0 -MolybdopterinGD@D 1572.985775 1572.0146 H(47)C(40)N(20)O(26)P(4)S(4)Mo(1) 0.0 Post-translational 424 0.0 -MolybdopterinGD@C 1572.985775 1572.0146 H(47)C(40)N(20)O(26)P(4)S(4)Mo(1) 0.0 Post-translational 424 0.0 -MolybdopterinGD@U 1572.985775 1572.0146 H(47)C(40)N(20)O(26)P(4)S(4)Mo(1) 0.0 Post-translational 424 0.0 -Dioxidation@U 31.989829 31.9988 O(2) 0.0 Multiple 425 0.0 -Dioxidation@C 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 -Dioxidation@W 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 -Dioxidation@Y 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 -Dioxidation@F 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 -Dioxidation@M 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 -Dioxidation@K 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 -Dioxidation@R 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 -Dioxidation@P 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 -Dioxidation@E 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 -Dioxidation@I 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 -Dioxidation@L 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 -Dioxidation@V 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 -Octanoyl@T 126.104465 126.1962 H(14)C(8)O(1) 0.0 Post-translational 426 0.0 -Octanoyl@S 126.104465 126.1962 H(14)C(8)O(1) 0.0 Post-translational 426 0.0 -Octanoyl@C 126.104465 126.1962 H(14)C(8)O(1) 0.0 Post-translational 426 0.0 -PhosphoHexNAc@T 283.045704 283.1724 H(14)C(8)N(1)O(8)P(1) 283.045704 H(14)C(8)N(1)O(8)P(1) O-linked glycosylation 428 0.5 -PhosphoHexNAc@S 283.045704 283.1724 H(14)C(8)N(1)O(8)P(1) 283.045704 H(14)C(8)N(1)O(8)P(1) O-linked glycosylation 428 0.5 -PhosphoHex@T 242.019154 242.1205 H(11)C(6)O(8)P(1) 242.019154 H(11)C(6)O(8)P(1) O-linked glycosylation 429 0.5 -PhosphoHex@S 242.019154 242.1205 H(11)C(6)O(8)P(1) 242.019154 H(11)C(6)O(8)P(1) O-linked glycosylation 429 0.5 -Palmitoleyl@C 236.214016 236.3929 H(28)C(16)O(1) 0.0 Post-translational 431 0.0 -Palmitoleyl@S 236.214016 236.3929 H(28)C(16)O(1) 0.0 Post-translational 431 0.0 -Palmitoleyl@T 236.214016 236.3929 H(28)C(16)O(1) 0.0 Pre-translational 431 0.0 -Cholesterol@Protein_C-term 368.344302 368.6383 H(44)C(27) 0.0 Post-translational 432 0.0 -Didehydroretinylidene@K 264.187801 264.4046 H(24)C(20) 0.0 Post-translational 433 0.0 -CHDH@D 294.183109 294.3859 H(26)C(17)O(4) 0.0 Post-translational 434 0.0 -Methylpyrroline@K 109.052764 109.1259 H(7)C(6)N(1)O(1) 0.0 Post-translational 435 0.0 -Hydroxyheme@E 614.161645 614.4714 H(30)C(34)N(4)O(4)Fe(1) 0.0 Post-translational 436 0.0 -MicrocinC7@Protein_C-term 386.110369 386.3003 H(19)C(13)N(6)O(6)P(1) 0.0 Post-translational 437 0.0 -Cyano@C 24.995249 25.0095 H(-1)C(1)N(1) 0.0 Post-translational 438 0.0 -Diironsubcluster@C 342.786916 342.876 H(-1)C(5)N(2)O(5)S(2)Fe(2) 0.0 Post-translational 439 0.0 -Amidino@C 42.021798 42.04 H(2)C(1)N(2) 0.0 Post-translational 440 0.0 -FMN@S 438.094051 438.3285 H(19)C(17)N(4)O(8)P(1) 0.0 Post-translational 442 0.0 -FMN@T 438.094051 438.3285 H(19)C(17)N(4)O(8)P(1) 0.0 Post-translational 442 0.0 -FMNC@C 456.104615 456.3438 H(21)C(17)N(4)O(9)P(1) 0.0 Post-translational 443 0.0 -CuSMo@C 922.834855 922.067 H(24)C(19)N(8)O(15)P(2)S(3)Mo(1)Cu(1) 0.0 Post-translational 444 0.0 -Hydroxytrimethyl@K 59.04969 59.0871 H(7)C(3)O(1) 0.0 Post-translational 445 0.0 -Deoxy@T -15.994915 -15.9994 O(-1) 0.0 Chemical derivative 447 0.0 -Deoxy@D -15.994915 -15.9994 O(-1) 0.0 Post-translational 447 0.0 -Deoxy@S -15.994915 -15.9994 O(-1) 0.0 Chemical derivative 447 0.0 -Microcin@Protein_C-term 831.197041 831.6871 H(37)C(36)N(3)O(20) 0.0 Post-translational 448 0.0 -Decanoyl@T 154.135765 154.2493 H(18)C(10)O(1) 0.0 Post-translational 449 0.0 -Decanoyl@S 154.135765 154.2493 H(18)C(10)O(1) 0.0 Post-translational 449 0.0 -GluGluGlu@Protein_C-term 387.127779 387.3419 H(21)C(15)N(3)O(9) 0.0 Post-translational 452 0.0 -GluGluGlu@E 387.127779 387.3419 H(21)C(15)N(3)O(9) 0.0 Post-translational 452 0.0 -GluGluGluGlu@Protein_C-term 516.170373 516.4559 H(28)C(20)N(4)O(12) 0.0 Post-translational 453 0.0 -GluGluGluGlu@E 516.170373 516.4559 H(28)C(20)N(4)O(12) 0.0 Post-translational 453 0.0 -HexN@W 161.068808 161.1558 H(11)C(6)N(1)O(4) 0.0 Other glycosylation 454 0.0 -HexN@T 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) O-linked glycosylation 454 0.5 -HexN@S 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) O-linked glycosylation 454 0.5 -HexN@N 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) N-linked glycosylation 454 0.5 -HexN@K 161.068808 161.1558 H(11)C(6)N(1)O(4) 0.0 Synth. pep. protect. gp. 454 0.0 -Xlink:DMP[154]@Protein_N-term 154.110613 154.2096 H(14)C(8)N(2)O(1) 0.0 Chemical derivative 455 0.0 -Xlink:DMP[154]@K 154.110613 154.2096 H(14)C(8)N(2)O(1) 0.0 Chemical derivative 455 0.0 -NDA@Any_N-term 175.042199 175.1855 H(5)C(13)N(1) 0.0 Chemical derivative 457 0.0 -NDA@K 175.042199 175.1855 H(5)C(13)N(1) 0.0 Chemical derivative 457 0.0 -SPITC:13C(6)@Any_N-term 220.991213 221.2054 H(5)C(1)13C(6)N(1)O(3)S(2) 0.0 Chemical derivative 464 0.0 -SPITC:13C(6)@K 220.991213 221.2054 H(5)C(1)13C(6)N(1)O(3)S(2) 0.0 Chemical derivative 464 0.0 -TMAB:2H(9)@Any_N-term 137.16403 137.2476 H(5)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 477 0.5 -TMAB:2H(9)@K 137.16403 137.2476 H(5)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 477 0.5 -TMAB@Any_N-term 128.107539 128.1922 H(14)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 476 0.5 -TMAB@K 128.107539 128.1922 H(14)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 476 0.5 -FTC@S 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 -FTC@R 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 -FTC@P 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 -FTC@K 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 -FTC@C 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 -AEC-MAEC@T 59.019355 59.1334 H(5)C(2)N(1)O(-1)S(1) 0.0 Chemical derivative 472 0.0 -AEC-MAEC@S 59.019355 59.1334 H(5)C(2)N(1)O(-1)S(1) 0.0 Chemical derivative 472 0.0 -BADGE@C 340.167459 340.4129 H(24)C(21)O(4) 0.0 Non-standard residue 493 0.0 -Label:2H(4)@A 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 -Label:2H(4)@Y 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 -Label:2H(4)@F 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 -Label:2H(4)@K 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 -Label:2H(4)@U 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 -Hep@T 192.063388 192.1666 H(12)C(7)O(6) 192.063388 H(12)C(7)O(6) O-linked glycosylation 490 0.5 -Hep@S 192.063388 192.1666 H(12)C(7)O(6) 192.063388 H(12)C(7)O(6) O-linked glycosylation 490 0.5 -Hep@R 192.063388 192.1666 H(12)C(7)O(6) 0.0 N-linked glycosylation 490 0.0 -Hep@Q 192.063388 192.1666 H(12)C(7)O(6) 0.0 Other glycosylation 490 0.0 -Hep@N 192.063388 192.1666 H(12)C(7)O(6) 192.063388 H(12)C(7)O(6) N-linked glycosylation 490 0.5 -Hep@K 192.063388 192.1666 H(12)C(7)O(6) 0.0 Other glycosylation 490 0.0 -CyDye-Cy5@C 684.298156 684.8442 H(44)C(38)N(4)O(6)S(1) 0.0 Chemical derivative 495 0.0 -DHP@C 118.065674 118.1558 H(8)C(8)N(1) 0.0 Chemical derivative 488 0.0 -BHTOH@H 234.16198 234.334 H(22)C(15)O(2) 0.0 Other 498 0.0 -BHTOH@C 234.16198 234.334 H(22)C(15)O(2) 0.0 Other 498 0.0 -BHTOH@K 234.16198 234.334 H(22)C(15)O(2) 0.0 Other 498 0.0 -IGBP:13C(2)@C 298.022748 299.1331 H(13)C(10)13C(2)N(2)O(2)Br(1) 0.0 Isotopic label 499 0.0 -Nmethylmaleimide+water@C 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Chemical derivative 500 0.0 -PyMIC@Any_N-term 134.048013 134.1353 H(6)C(7)N(2)O(1) 0.0 Chemical derivative 501 0.0 -LG-lactam-K@Protein_N-term 332.19876 332.4339 H(28)C(20)O(4) 0.0 Post-translational 503 0.0 -LG-lactam-K@K 332.19876 332.4339 H(28)C(20)O(4) 0.0 Post-translational 503 0.0 -BisANS@K 594.091928 594.6569 H(22)C(32)N(2)O(6)S(2) 0.0 Chemical derivative 519 0.0 -Piperidine@Any_N-term 68.0626 68.117 H(8)C(5) 0.0 Chemical derivative 520 0.0 -Piperidine@K 68.0626 68.117 H(8)C(5) 0.0 Chemical derivative 520 0.0 -Diethyl@Any_N-term 56.0626 56.1063 H(8)C(4) 0.0 Chemical derivative 518 0.0 -Diethyl@K 56.0626 56.1063 H(8)C(4) 0.0 Chemical derivative 518 0.0 -LG-Hlactam-K@Protein_N-term 348.193674 348.4333 H(28)C(20)O(5) 0.0 Post-translational 504 0.0 -LG-Hlactam-K@K 348.193674 348.4333 H(28)C(20)O(5) 0.0 Post-translational 504 0.0 -Dimethyl:2H(4)13C(2)@Protein_N-term 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 -Dimethyl:2H(4)13C(2)@R 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 -Dimethyl:2H(4)13C(2)@K 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 -Dimethyl:2H(4)13C(2)@Any_N-term 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 -C8-QAT@Any_N-term 227.224915 227.3862 H(29)C(14)N(1)O(1) 0.0 Chemical derivative 513 0.0 -C8-QAT@K 227.224915 227.3862 H(29)C(14)N(1)O(1) 0.0 Chemical derivative 513 0.0 -Hex(2)@R 324.105647 324.2812 H(20)C(12)O(10) 0.0 Other glycosylation 512 0.0 -Hex(2)@K 324.105647 324.2812 H(20)C(12)O(10) 0.0 Other glycosylation 512 0.0 -Hex(2)@S 324.105647 324.2812 H(20)C(12)O(10) 324.105647 H(20)C(12)O(10) O-linked glycosylation 512 0.5 -Hex(2)@T 324.105647 324.2812 H(20)C(12)O(10) 324.105647 H(20)C(12)O(10) O-linked glycosylation 512 0.5 -LG-lactam-R@R 290.176961 290.3939 H(26)C(19)N(-2)O(4) 0.0 Post-translational 505 0.0 -Withaferin@C 470.266839 470.5977 H(38)C(28)O(6) 0.0 Chemical derivative 1036 0.0 -Biotin:Thermo-88317@S 443.291294 443.5603 H(42)C(22)N(3)O(4)P(1) 0.0 Chemical derivative 1037 0.0 -Biotin:Thermo-88317@Y 443.291294 443.5603 H(42)C(22)N(3)O(4)P(1) 0.0 Chemical derivative 1037 0.0 -CLIP_TRAQ_2@Any_N-term 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 -CLIP_TRAQ_2@K 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 -CLIP_TRAQ_2@Y 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 -LG-Hlactam-R@R 306.171876 306.3933 H(26)C(19)N(-2)O(5) 0.0 Post-translational 506 0.0 -Maleimide-PEO2-Biotin@C 525.225719 525.6183 H(35)C(23)N(5)O(7)S(1) 0.0 Chemical derivative 522 0.0 -Sulfo-NHS-LC-LC-Biotin@Any_N-term 452.245726 452.6106 H(36)C(22)N(4)O(4)S(1) 0.0 Chemical derivative 523 0.0 -Sulfo-NHS-LC-LC-Biotin@K 452.245726 452.6106 H(36)C(22)N(4)O(4)S(1) 0.0 Chemical derivative 523 0.0 -FNEM@C 427.069202 427.3625 H(13)C(24)N(1)O(7) 0.0 Chemical derivative 515 0.0 -PropylNAGthiazoline@C 232.064354 232.2768 H(14)C(9)N(1)O(4)S(1) 0.0 Chemical derivative 514 0.0 -Dethiomethyl@M -48.003371 -48.1075 H(-4)C(-1)S(-1) 0.0 Artefact 526 0.0 -iTRAQ4plex114@Y 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 -iTRAQ4plex114@Any_N-term 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 -iTRAQ4plex114@K 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 -iTRAQ4plex114@C 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 -iTRAQ4plex115@Y 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 -iTRAQ4plex115@Any_N-term 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 -iTRAQ4plex115@K 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 -iTRAQ4plex115@C 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 -Dibromo@Y 155.821022 157.7921 H(-2)Br(2) 0.0 Chemical derivative 534 0.0 -LRGG@K 383.228103 383.446 H(29)C(16)N(7)O(4) 0.0 Chemical derivative 535 0.0 -CLIP_TRAQ_3@Y 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 -CLIP_TRAQ_3@Any_N-term 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 -CLIP_TRAQ_3@K 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 -CLIP_TRAQ_4@Any_N-term 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 -CLIP_TRAQ_4@K 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 -CLIP_TRAQ_4@Y 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 -Biotin:Cayman-10141@C 626.386577 626.8927 H(54)C(35)N(4)O(4)S(1) 0.0 Other 538 0.0 -Biotin:Cayman-10013@C 660.428442 660.9504 H(60)C(36)N(4)O(5)S(1) 0.0 Other 539 0.0 -Ala->Ser@A 15.994915 15.9994 H(0)C(0)N(0)O(1)S(0) 0.0 AA substitution 540 0.0 -Ala->Thr@A 30.010565 30.026 H(2)C(1)N(0)O(1)S(0) 0.0 AA substitution 541 0.0 -Ala->Asp@A 43.989829 44.0095 H(0)C(1)N(0)O(2)S(0) 0.0 AA substitution 542 0.0 -Ala->Pro@A 26.01565 26.0373 H(2)C(2)N(0)O(0)S(0) 0.0 AA substitution 543 0.0 -Ala->Gly@A -14.01565 -14.0266 H(-2)C(-1)N(0)O(0)S(0) 0.0 AA substitution 544 0.0 -Ala->Glu@A 58.005479 58.0361 H(2)C(2)N(0)O(2)S(0) 0.0 AA substitution 545 0.0 -Ala->Val@A 28.0313 28.0532 H(4)C(2)N(0)O(0)S(0) 0.0 AA substitution 546 0.0 -Cys->Phe@C 44.059229 44.031 H(4)C(6)N(0)O(0)S(-1) 0.0 AA substitution 547 0.0 -Cys->Ser@C -15.977156 -16.0656 H(0)C(0)N(0)O(1)S(-1) 0.0 AA substitution 548 0.0 -Cys->Trp@C 83.070128 83.067 H(5)C(8)N(1)O(0)S(-1) 0.0 AA substitution 549 0.0 -Cys->Tyr@C 60.054144 60.0304 H(4)C(6)N(0)O(1)S(-1) 0.0 AA substitution 550 0.0 -Cys->Arg@C 53.091927 53.0428 H(7)C(3)N(3)O(0)S(-1) 0.0 AA substitution 551 0.0 -Cys->Gly@C -45.987721 -46.0916 H(-2)C(-1)N(0)O(0)S(-1) 0.0 AA substitution 552 0.0 -Asp->Ala@D -43.989829 -44.0095 H(0)C(-1)N(0)O(-2)S(0) 0.0 AA substitution 553 0.0 -Asp->His@D 22.031969 22.0519 H(2)C(2)N(2)O(-2)S(0) 0.0 AA substitution 554 0.0 -Asp->Asn@D -0.984016 -0.9848 H(1)C(0)N(1)O(-1)S(0) 0.0 AA substitution 555 0.0 -Asp->Gly@D -58.005479 -58.0361 H(-2)C(-2)N(0)O(-2)S(0) 0.0 AA substitution 556 0.0 -Asp->Tyr@D 48.036386 48.0859 H(4)C(5)N(0)O(-1)S(0) 0.0 AA substitution 557 0.0 -Asp->Glu@D 14.01565 14.0266 H(2)C(1)N(0)O(0)S(0) 0.0 AA substitution 558 0.0 -Asp->Val@D -15.958529 -15.9563 H(4)C(1)N(0)O(-2)S(0) 0.0 AA substitution 559 0.0 -Glu->Ala@E -58.005479 -58.0361 H(-2)C(-2)N(0)O(-2)S(0) 0.0 AA substitution 560 0.0 -Glu->Gln@E -0.984016 -0.9848 H(1)C(0)N(1)O(-1)S(0) 0.0 AA substitution 561 0.0 -Glu->Asp@E -14.01565 -14.0266 H(-2)C(-1)N(0)O(0)S(0) 0.0 AA substitution 562 0.0 -Glu->Lys@E -0.94763 -0.9417 H(5)C(1)N(1)O(-2)S(0) 0.0 AA substitution 563 0.0 -Glu->Gly@E -72.021129 -72.0627 H(-4)C(-3)N(0)O(-2)S(0) 0.0 AA substitution 564 0.0 -Glu->Val@E -29.974179 -29.9829 H(2)C(0)N(0)O(-2)S(0) 0.0 AA substitution 565 0.0 -Phe->Ser@F -60.036386 -60.0966 H(-4)C(-6)N(0)O(1)S(0) 0.0 AA substitution 566 0.0 -Phe->Cys@F -44.059229 -44.031 H(-4)C(-6)N(0)O(0)S(1) 0.0 AA substitution 567 0.0 -Phe->Xle@F -33.98435 -34.0162 H(2)C(-3) 0.0 AA substitution 568 0.0 -Phe->Tyr@F 15.994915 15.9994 H(0)C(0)N(0)O(1)S(0) 0.0 AA substitution 569 0.0 -Phe->Val@F -48.0 -48.0428 H(0)C(-4)N(0)O(0)S(0) 0.0 AA substitution 570 0.0 -Gly->Ala@G 14.01565 14.0266 H(2)C(1)N(0)O(0)S(0) 0.0 AA substitution 571 0.0 -Gly->Ser@G 30.010565 30.026 H(2)C(1)N(0)O(1)S(0) 0.0 AA substitution 572 0.0 -Gly->Trp@G 129.057849 129.1586 H(7)C(9)N(1)O(0)S(0) 0.0 AA substitution 573 0.0 -Gly->Glu@G 72.021129 72.0627 H(4)C(3)N(0)O(2)S(0) 0.0 AA substitution 574 0.0 -Gly->Val@G 42.04695 42.0797 H(6)C(3)N(0)O(0)S(0) 0.0 AA substitution 575 0.0 -Gly->Asp@G 58.005479 58.0361 H(2)C(2)N(0)O(2)S(0) 0.0 AA substitution 576 0.0 -Gly->Cys@G 45.987721 46.0916 H(2)C(1)N(0)O(0)S(1) 0.0 AA substitution 577 0.0 -Gly->Arg@G 99.079647 99.1344 H(9)C(4)N(3)O(0)S(0) 0.0 AA substitution 578 0.0 -dNIC@Any_N-term 109.048119 109.1205 H(1)2H(3)C(6)N(1)O(1) 0.0 Isotopic label 698 0.0 -dNIC@K 109.048119 109.1205 H(1)2H(3)C(6)N(1)O(1) 0.0 Isotopic label 698 0.0 -His->Pro@H -40.006148 -40.0241 H(0)C(-1)N(-2)O(0)S(0) 0.0 AA substitution 580 0.0 -His->Tyr@H 26.004417 26.034 H(2)C(3)N(-2)O(1)S(0) 0.0 AA substitution 581 0.0 -His->Gln@H -9.000334 -9.0101 H(1)C(-1)N(-1)O(1)S(0) 0.0 AA substitution 582 0.0 -NIC@Any_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 697 0.0 -NIC@K 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 697 0.0 -His->Arg@H 19.042199 19.0464 H(5)C(0)N(1)O(0)S(0) 0.0 AA substitution 584 0.0 -His->Xle@H -23.974848 -23.9816 H(4)N(-2) 0.0 AA substitution 585 0.0 -Xle->Ala@L -42.04695 -42.0797 H(-6)C(-3)N(0)O(0)S(0) 0.0 AA substitution 1125 0.0 -Xle->Ala@I -42.04695 -42.0797 H(-6)C(-3)N(0)O(0)S(0) 0.0 AA substitution 1125 0.0 -Xle->Thr@L -12.036386 -12.0538 H(-4)C(-2)O(1) 0.0 AA substitution 588 0.0 -Xle->Thr@I -12.036386 -12.0538 H(-4)C(-2)O(1) 0.0 AA substitution 588 0.0 -Xle->Asn@L 0.958863 0.945 H(-5)C(-2)N(1)O(1) 0.0 AA substitution 589 0.0 -Xle->Asn@I 0.958863 0.945 H(-5)C(-2)N(1)O(1) 0.0 AA substitution 589 0.0 -Xle->Lys@L 15.010899 15.0146 H(1)N(1) 0.0 AA substitution 590 0.0 -Xle->Lys@I 15.010899 15.0146 H(1)N(1) 0.0 AA substitution 590 0.0 -Lys->Thr@K -27.047285 -27.0684 H(-5)C(-2)N(-1)O(1)S(0) 0.0 AA substitution 594 0.0 -Lys->Asn@K -14.052036 -14.0696 H(-6)C(-2)N(0)O(1)S(0) 0.0 AA substitution 595 0.0 -Lys->Glu@K 0.94763 0.9417 H(-5)C(-1)N(-1)O(2)S(0) 0.0 AA substitution 596 0.0 -Lys->Gln@K -0.036386 -0.0431 H(-4)C(-1)N(0)O(1)S(0) 0.0 AA substitution 597 0.0 -Lys->Met@K 2.945522 3.0238 H(-3)C(-1)N(-1)O(0)S(1) 0.0 AA substitution 598 0.0 -Lys->Arg@K 28.006148 28.0134 H(0)C(0)N(2)O(0)S(0) 0.0 AA substitution 599 0.0 -Lys->Xle@K -15.010899 -15.0146 H(-1)N(-1) 0.0 AA substitution 600 0.0 -Xle->Ser@I -26.052036 -26.0803 H(-6)C(-3)O(1) 0.0 AA substitution 601 0.0 -Xle->Ser@L -26.052036 -26.0803 H(-6)C(-3)O(1) 0.0 AA substitution 601 0.0 -Xle->Phe@I 33.98435 34.0162 H(-2)C(3) 0.0 AA substitution 602 0.0 -Xle->Phe@L 33.98435 34.0162 H(-2)C(3) 0.0 AA substitution 602 0.0 -Xle->Trp@I 72.995249 73.0523 H(-1)C(5)N(1) 0.0 AA substitution 603 0.0 -Xle->Trp@L 72.995249 73.0523 H(-1)C(5)N(1) 0.0 AA substitution 603 0.0 -Xle->Pro@I -16.0313 -16.0425 H(-4)C(-1) 0.0 AA substitution 604 0.0 -Xle->Pro@L -16.0313 -16.0425 H(-4)C(-1) 0.0 AA substitution 604 0.0 -Xle->Val@I -14.01565 -14.0266 H(-2)C(-1) 0.0 AA substitution 605 0.0 -Xle->Val@L -14.01565 -14.0266 H(-2)C(-1) 0.0 AA substitution 605 0.0 -Xle->His@I 23.974848 23.9816 H(-4)N(2) 0.0 AA substitution 606 0.0 -Xle->His@L 23.974848 23.9816 H(-4)N(2) 0.0 AA substitution 606 0.0 -Xle->Gln@I 14.974514 14.9716 H(-3)C(-1)N(1)O(1) 0.0 AA substitution 607 0.0 -Xle->Gln@L 14.974514 14.9716 H(-3)C(-1)N(1)O(1) 0.0 AA substitution 607 0.0 -Xle->Met@I 17.956421 18.0384 H(-2)C(-1)S(1) 0.0 AA substitution 608 0.0 -Xle->Met@L 17.956421 18.0384 H(-2)C(-1)S(1) 0.0 AA substitution 608 0.0 -Xle->Arg@I 43.017047 43.028 H(1)N(3) 0.0 AA substitution 609 0.0 -Xle->Arg@L 43.017047 43.028 H(1)N(3) 0.0 AA substitution 609 0.0 -Met->Thr@M -29.992806 -30.0922 H(-2)C(-1)N(0)O(1)S(-1) 0.0 AA substitution 610 0.0 -Met->Arg@M 25.060626 24.9896 H(3)C(1)N(3)O(0)S(-1) 0.0 AA substitution 611 0.0 -Met->Lys@M -2.945522 -3.0238 H(3)C(1)N(1)O(0)S(-1) 0.0 AA substitution 613 0.0 -Met->Xle@M -17.956421 -18.0384 H(2)C(1)S(-1) 0.0 AA substitution 614 0.0 -Met->Val@M -31.972071 -32.065 H(0)C(0)N(0)O(0)S(-1) 0.0 AA substitution 615 0.0 -Asn->Ser@N -27.010899 -27.0253 H(-1)C(-1)N(-1)O(0)S(0) 0.0 AA substitution 616 0.0 -Asn->Thr@N -12.995249 -12.9988 H(1)C(0)N(-1)O(0)S(0) 0.0 AA substitution 617 0.0 -Asn->Lys@N 14.052036 14.0696 H(6)C(2)N(0)O(-1)S(0) 0.0 AA substitution 618 0.0 -Asn->Tyr@N 49.020401 49.0706 H(3)C(5)N(-1)O(0)S(0) 0.0 AA substitution 619 0.0 -Asn->His@N 23.015984 23.0366 H(1)C(2)N(1)O(-1)S(0) 0.0 AA substitution 620 0.0 -Asn->Asp@N 0.984016 0.9848 H(-1)C(0)N(-1)O(1)S(0) 0.0 AA substitution 621 0.0 -Asn->Xle@N -0.958863 -0.945 H(5)C(2)N(-1)O(-1) 0.0 AA substitution 622 0.0 -Pro->Ser@P -10.020735 -10.0379 H(-2)C(-2)N(0)O(1)S(0) 0.0 AA substitution 623 0.0 -Pro->Ala@P -26.01565 -26.0373 H(-2)C(-2)N(0)O(0)S(0) 0.0 AA substitution 624 0.0 -Pro->His@P 40.006148 40.0241 H(0)C(1)N(2)O(0)S(0) 0.0 AA substitution 625 0.0 -Pro->Gln@P 31.005814 31.014 H(1)C(0)N(1)O(1)S(0) 0.0 AA substitution 626 0.0 -Pro->Thr@P 3.994915 3.9887 H(0)C(-1)N(0)O(1)S(0) 0.0 AA substitution 627 0.0 -Pro->Arg@P 59.048347 59.0705 H(5)C(1)N(3)O(0)S(0) 0.0 AA substitution 628 0.0 -Pro->Xle@P 16.0313 16.0425 H(4)C(1) 0.0 AA substitution 629 0.0 -Gln->Pro@Q -31.005814 -31.014 H(-1)C(0)N(-1)O(-1)S(0) 0.0 AA substitution 630 0.0 -Gln->Lys@Q 0.036386 0.0431 H(4)C(1)N(0)O(-1)S(0) 0.0 AA substitution 631 0.0 -Gln->Glu@Q 0.984016 0.9848 H(-1)C(0)N(-1)O(1)S(0) 0.0 AA substitution 632 0.0 -Gln->His@Q 9.000334 9.0101 H(-1)C(1)N(1)O(-1)S(0) 0.0 AA substitution 633 0.0 -Gln->Arg@Q 28.042534 28.0565 H(4)C(1)N(2)O(-1)S(0) 0.0 AA substitution 634 0.0 -Gln->Xle@Q -14.974514 -14.9716 H(3)C(1)N(-1)O(-1) 0.0 AA substitution 635 0.0 -Arg->Ser@R -69.069083 -69.1084 H(-7)C(-3)N(-3)O(1)S(0) 0.0 AA substitution 636 0.0 -Arg->Trp@R 29.978202 30.0242 H(-2)C(5)N(-2)O(0)S(0) 0.0 AA substitution 637 0.0 -Arg->Thr@R -55.053433 -55.0818 H(-5)C(-2)N(-3)O(1)S(0) 0.0 AA substitution 638 0.0 -Arg->Pro@R -59.048347 -59.0705 H(-5)C(-1)N(-3)O(0)S(0) 0.0 AA substitution 639 0.0 -Arg->Lys@R -28.006148 -28.0134 H(0)C(0)N(-2)O(0)S(0) 0.0 AA substitution 640 0.0 -Arg->His@R -19.042199 -19.0464 H(-5)C(0)N(-1)O(0)S(0) 0.0 AA substitution 641 0.0 -Arg->Gln@R -28.042534 -28.0565 H(-4)C(-1)N(-2)O(1)S(0) 0.0 AA substitution 642 0.0 -Arg->Met@R -25.060626 -24.9896 H(-3)C(-1)N(-3)O(0)S(1) 0.0 AA substitution 643 0.0 -Arg->Cys@R -53.091927 -53.0428 H(-7)C(-3)N(-3)O(0)S(1) 0.0 AA substitution 644 0.0 -Arg->Xle@R -43.017047 -43.028 H(-1)N(-3) 0.0 AA substitution 645 0.0 -Arg->Gly@R -99.079647 -99.1344 H(-9)C(-4)N(-3)O(0)S(0) 0.0 AA substitution 646 0.0 -Ser->Phe@S 60.036386 60.0966 H(4)C(6)N(0)O(-1)S(0) 0.0 AA substitution 647 0.0 -Ser->Ala@S -15.994915 -15.9994 H(0)C(0)N(0)O(-1)S(0) 0.0 AA substitution 648 0.0 -Ser->Trp@S 99.047285 99.1326 H(5)C(8)N(1)O(-1)S(0) 0.0 AA substitution 649 0.0 -Ser->Thr@S 14.01565 14.0266 H(2)C(1)N(0)O(0)S(0) 0.0 AA substitution 650 0.0 -Ser->Asn@S 27.010899 27.0253 H(1)C(1)N(1)O(0)S(0) 0.0 AA substitution 651 0.0 -Ser->Pro@S 10.020735 10.0379 H(2)C(2)N(0)O(-1)S(0) 0.0 AA substitution 652 0.0 -Ser->Tyr@S 76.0313 76.096 H(4)C(6)N(0)O(0)S(0) 0.0 AA substitution 653 0.0 -Ser->Cys@S 15.977156 16.0656 H(0)C(0)N(0)O(-1)S(1) 0.0 AA substitution 654 0.0 -Ser->Arg@S 69.069083 69.1084 H(7)C(3)N(3)O(-1)S(0) 0.0 AA substitution 655 0.0 -Ser->Xle@S 26.052036 26.0803 H(6)C(3)O(-1) 0.0 AA substitution 656 0.0 -Ser->Gly@S -30.010565 -30.026 H(-2)C(-1)N(0)O(-1)S(0) 0.0 AA substitution 657 0.0 -Thr->Ser@T -14.01565 -14.0266 H(-2)C(-1)N(0)O(0)S(0) 0.0 AA substitution 658 0.0 -Thr->Ala@T -30.010565 -30.026 H(-2)C(-1)N(0)O(-1)S(0) 0.0 AA substitution 659 0.0 -Thr->Asn@T 12.995249 12.9988 H(-1)C(0)N(1)O(0)S(0) 0.0 AA substitution 660 0.0 -Thr->Lys@T 27.047285 27.0684 H(5)C(2)N(1)O(-1)S(0) 0.0 AA substitution 661 0.0 -Thr->Pro@T -3.994915 -3.9887 H(0)C(1)N(0)O(-1)S(0) 0.0 AA substitution 662 0.0 -Thr->Met@T 29.992806 30.0922 H(2)C(1)N(0)O(-1)S(1) 0.0 AA substitution 663 0.0 -Thr->Xle@T 12.036386 12.0538 H(4)C(2)O(-1) 0.0 AA substitution 664 0.0 -Thr->Arg@T 55.053433 55.0818 H(5)C(2)N(3)O(-1)S(0) 0.0 AA substitution 665 0.0 -Val->Phe@V 48.0 48.0428 H(0)C(4)N(0)O(0)S(0) 0.0 AA substitution 666 0.0 -Val->Ala@V -28.0313 -28.0532 H(-4)C(-2)N(0)O(0)S(0) 0.0 AA substitution 667 0.0 -Val->Glu@V 29.974179 29.9829 H(-2)C(0)N(0)O(2)S(0) 0.0 AA substitution 668 0.0 -Val->Met@V 31.972071 32.065 H(0)C(0)N(0)O(0)S(1) 0.0 AA substitution 669 0.0 -Val->Asp@V 15.958529 15.9563 H(-4)C(-1)N(0)O(2)S(0) 0.0 AA substitution 670 0.0 -Val->Xle@V 14.01565 14.0266 H(2)C(1) 0.0 AA substitution 671 0.0 -Val->Gly@V -42.04695 -42.0797 H(-6)C(-3)N(0)O(0)S(0) 0.0 AA substitution 672 0.0 -Trp->Ser@W -99.047285 -99.1326 H(-5)C(-8)N(-1)O(1)S(0) 0.0 AA substitution 673 0.0 -Trp->Cys@W -83.070128 -83.067 H(-5)C(-8)N(-1)O(0)S(1) 0.0 AA substitution 674 0.0 -Trp->Arg@W -29.978202 -30.0242 H(2)C(-5)N(2)O(0)S(0) 0.0 AA substitution 675 0.0 -Trp->Gly@W -129.057849 -129.1586 H(-7)C(-9)N(-1)O(0)S(0) 0.0 AA substitution 676 0.0 -Trp->Xle@W -72.995249 -73.0523 H(1)C(-5)N(-1) 0.0 AA substitution 677 0.0 -Tyr->Phe@Y -15.994915 -15.9994 H(0)C(0)N(0)O(-1)S(0) 0.0 AA substitution 678 0.0 -Tyr->Ser@Y -76.0313 -76.096 H(-4)C(-6)N(0)O(0)S(0) 0.0 AA substitution 679 0.0 -Tyr->Asn@Y -49.020401 -49.0706 H(-3)C(-5)N(1)O(0)S(0) 0.0 AA substitution 680 0.0 -Tyr->His@Y -26.004417 -26.034 H(-2)C(-3)N(2)O(-1)S(0) 0.0 AA substitution 681 0.0 -Tyr->Asp@Y -48.036386 -48.0859 H(-4)C(-5)N(0)O(1)S(0) 0.0 AA substitution 682 0.0 -Tyr->Cys@Y -60.054144 -60.0304 H(-4)C(-6)N(0)O(-1)S(1) 0.0 AA substitution 683 0.0 -BDMAPP@W 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 -BDMAPP@Y 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 -BDMAPP@Protein_N-term 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Chemical derivative 684 0.0 -BDMAPP@K 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Chemical derivative 684 0.0 -BDMAPP@H 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 -NA-LNO2@C 325.225309 325.443 H(31)C(18)N(1)O(4) 0.0 Post-translational 685 0.0 -NA-LNO2@H 325.225309 325.443 H(31)C(18)N(1)O(4) 0.0 Post-translational 685 0.0 -NA-OA-NO2@C 327.240959 327.4589 H(33)C(18)N(1)O(4) 0.0 Post-translational 686 0.0 -NA-OA-NO2@H 327.240959 327.4589 H(33)C(18)N(1)O(4) 0.0 Post-translational 686 0.0 -ICPL:2H(4)@Any_N-term 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 -ICPL:2H(4)@Protein_N-term 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 -ICPL:2H(4)@K 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 -CarboxymethylDTT@C 210.00205 210.2712 H(10)C(6)O(4)S(2) 0.0 Artefact 894 0.0 -iTRAQ8plex@Protein_N-term 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -iTRAQ8plex@T 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -iTRAQ8plex@S 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -iTRAQ8plex@H 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -iTRAQ8plex@Y 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -iTRAQ8plex@Any_N-term 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -iTRAQ8plex@K 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -iTRAQ8plex@C 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 -Label:13C(6)15N(1)@I 7.017164 6.9493 C(-6)13C(6)N(-1)15N(1) 0.0 Isotopic label 695 0.0 -Label:13C(6)15N(1)@L 7.017164 6.9493 C(-6)13C(6)N(-1)15N(1) 0.0 Isotopic label 695 0.0 -Label:2H(9)13C(6)15N(2)@K 17.07069 16.9982 H(-9)2H(9)C(-6)13C(6)N(-2)15N(2) 0.0 Isotopic label 696 0.0 -HNE-Delta:H(2)O@K 138.104465 138.2069 H(14)C(9)O(1) 0.0 Chemical derivative 720 0.0 -HNE-Delta:H(2)O@H 138.104465 138.2069 H(14)C(9)O(1) 0.0 Chemical derivative 720 0.0 -HNE-Delta:H(2)O@C 138.104465 138.2069 H(14)C(9)O(1) 0.0 Chemical derivative 720 0.0 -4-ONE@K 154.09938 154.2063 H(14)C(9)O(2) 0.0 Chemical derivative 721 0.0 -4-ONE@H 154.09938 154.2063 H(14)C(9)O(2) 0.0 Chemical derivative 721 0.0 -4-ONE@C 154.09938 154.2063 H(14)C(9)O(2) 0.0 Chemical derivative 721 0.0 -O-Dimethylphosphate@Y 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 723 0.0 -O-Dimethylphosphate@T 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 723 0.0 -O-Dimethylphosphate@S 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 723 0.0 -O-Methylphosphate@Y 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 -O-Methylphosphate@T 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 -O-Methylphosphate@S 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 -Diethylphosphate@Any_N-term 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 -Diethylphosphate@H 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 -Diethylphosphate@C 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 -Diethylphosphate@K 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 -Diethylphosphate@Y 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 -Diethylphosphate@T 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 -Diethylphosphate@S 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 -Ethylphosphate@Any_N-term 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 -Ethylphosphate@K 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 -Ethylphosphate@Y 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 -Ethylphosphate@T 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 -Ethylphosphate@S 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 -O-pinacolylmethylphosphonate@T 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 -O-pinacolylmethylphosphonate@S 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 -O-pinacolylmethylphosphonate@K 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 -O-pinacolylmethylphosphonate@Y 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 -O-pinacolylmethylphosphonate@H 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 -Methylphosphonate@Y 77.987066 78.0071 H(3)C(1)O(2)P(1) 0.0 Chemical derivative 728 0.0 -Methylphosphonate@T 77.987066 78.0071 H(3)C(1)O(2)P(1) 0.0 Chemical derivative 728 0.0 -Methylphosphonate@S 77.987066 78.0071 H(3)C(1)O(2)P(1) 0.0 Chemical derivative 728 0.0 -O-Isopropylmethylphosphonate@Y 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemical derivative 729 0.0 -O-Isopropylmethylphosphonate@T 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemical derivative 729 0.0 -O-Isopropylmethylphosphonate@S 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemical derivative 729 0.0 -iTRAQ8plex:13C(6)15N(2)@Y 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 -iTRAQ8plex:13C(6)15N(2)@Any_N-term 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 -iTRAQ8plex:13C(6)15N(2)@K 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 -iTRAQ8plex:13C(6)15N(2)@C 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 -BEMAD_ST@T 136.001656 136.2357 H(8)C(4)O(1)S(2) 0.0 Chemical derivative 735 0.0 -BEMAD_ST@S 136.001656 136.2357 H(8)C(4)O(1)S(2) 0.0 Chemical derivative 735 0.0 -Ethanolamine@D 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 -Ethanolamine@Any_C-term 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 -Ethanolamine@E 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 -Ethanolamine@C 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 -TMT6plex@T 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 -TMT6plex@S 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 -TMT6plex@H 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 -TMT6plex@Protein_N-term 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 -TMT6plex@Any_N-term 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 -TMT6plex@K 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 -BEMAD_C@C 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 736 0.0 -TMT2plex@H 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT2plex@S 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT2plex@T 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT2plex@Protein_N-term 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT2plex@Any_N-term 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT2plex@K 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 -TMT@Protein_N-term 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 -TMT@Any_N-term 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 -TMT@K 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 -TMT@H 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Isotopic label 739 0.0 -TMT@S 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Isotopic label 739 0.0 -TMT@T 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Isotopic label 739 0.0 -ExacTagThiol@C 972.365219 972.7268 H(50)C(23)13C(12)N(8)15N(6)O(18) 0.0 Isotopic label 740 0.0 -ExacTagAmine@K 1046.347854 1046.8285 H(52)C(25)13C(12)N(8)15N(6)O(19)S(1) 0.0 Isotopic label 741 0.0 -NO_SMX_SEMD@C 252.044287 252.2697 H(10)C(10)N(3)O(3)S(1) 0.0 Chemical derivative 744 0.0 -4-ONE+Delta:H(-2)O(-1)@K 136.088815 136.191 H(12)C(9)O(1) 0.0 Chemical derivative 743 0.0 -4-ONE+Delta:H(-2)O(-1)@H 136.088815 136.191 H(12)C(9)O(1) 0.0 Chemical derivative 743 0.0 -4-ONE+Delta:H(-2)O(-1)@C 136.088815 136.191 H(12)C(9)O(1) 0.0 Chemical derivative 743 0.0 -NO_SMX_SMCT@C 268.039202 268.2691 H(10)C(10)N(3)O(4)S(1) 0.0 Chemical derivative 745 0.0 -NO_SMX_SIMD@C 267.031377 267.2612 H(9)C(10)N(3)O(4)S(1) 0.0 Chemical derivative 746 0.0 -Malonyl@C 86.000394 86.0462 H(2)C(3)O(3) 0.0 Chemical derivative 747 0.0 -Malonyl@S 86.000394 86.0462 H(2)C(3)O(3) 0.0 Chemical derivative 747 0.0 -Malonyl@K 86.000394 86.0462 H(2)C(3)O(3) 0.0 Post-translational 747 0.0 -3sulfo@Any_N-term 183.983029 184.1693 H(4)C(7)O(4)S(1) 0.0 Chemical derivative 748 0.0 -trifluoro@L 53.971735 53.9714 H(-3)F(3) 0.0 Non-standard residue 750 0.0 -TNBS@Any_N-term 210.986535 211.0886 H(1)C(6)N(3)O(6) 0.0 Chemical derivative 751 0.0 -TNBS@K 210.986535 211.0886 H(1)C(6)N(3)O(6) 0.0 Chemical derivative 751 0.0 -Biotin-phenacyl@C 626.263502 626.727 H(38)C(29)N(8)O(6)S(1) 0.0 Chemical derivative 774 0.0 -Biotin-phenacyl@H 626.263502 626.727 H(38)C(29)N(8)O(6)S(1) 0.0 Chemical derivative 774 0.0 -Biotin-phenacyl@S 626.263502 626.727 H(38)C(29)N(8)O(6)S(1) 0.0 Chemical derivative 774 0.0 -BEMAD_C:2H(6)@C 126.062161 126.2071 H(2)2H(6)C(4)O(2)S(1) 0.0 Isotopic label 764 0.0 -lapachenole@C 240.11503 240.297 H(16)C(16)O(2) 0.0 Chemical derivative 771 0.0 -Label:13C(5)@P 5.016774 4.9633 C(-5)13C(5) 0.0 Isotopic label 772 0.0 -maleimide@K 97.016378 97.0721 H(3)C(4)N(1)O(2) 0.0 Chemical derivative 773 0.0 -maleimide@C 97.016378 97.0721 H(3)C(4)N(1)O(2) 0.0 Chemical derivative 773 0.0 -IDEnT@C 214.990469 216.064 H(7)C(9)N(1)O(1)Cl(2) 0.0 Isotopic label 762 0.0 -BEMAD_ST:2H(6)@T 142.039317 142.2727 H(2)2H(6)C(4)O(1)S(2) 0.0 Isotopic label 763 0.0 -BEMAD_ST:2H(6)@S 142.039317 142.2727 H(2)2H(6)C(4)O(1)S(2) 0.0 Isotopic label 763 0.0 -Met-loss@M^Protein_N-term -131.040485 -131.1961 H(-9)C(-5)N(-1)O(-1)S(-1) 0.0 Co-translational 765 0.0 -Met-loss+Acetyl@M^Protein_N-term -89.02992 -89.1594 H(-7)C(-3)N(-1)S(-1) 0.0 Co-translational 766 0.0 -Menadione-HQ@K 172.05243 172.18 H(8)C(11)O(2) 0.0 Chemical derivative 767 0.0 -Menadione-HQ@C 172.05243 172.18 H(8)C(11)O(2) 0.0 Chemical derivative 767 0.0 -Carboxymethyl:13C(2)@C 60.012189 60.0214 H(2)13C(2)O(2) 0.0 Chemical derivative 775 0.0 -NEM:2H(5)@C 130.079062 130.1561 H(2)2H(5)C(6)N(1)O(2) 0.0 Chemical derivative 776 0.0 -Gly-loss+Amide@G^Any_C-term -58.005479 -58.0361 H(-2)C(-2)O(-2) 0.0 Post-translational 822 0.0 -TMPP-Ac@Any_N-term 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Chemical derivative 827 0.0 -TMPP-Ac@K 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Artefact 827 0.0 -TMPP-Ac@Y 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Artefact 827 0.0 -Label:13C(6)+GG@K 120.063056 120.0586 H(6)C(-2)13C(6)N(2)O(2) 0.0 Isotopic label 799 0.0 -Arg->Npo@R 80.985078 81.0297 H(-1)C(3)N(1)O(2) 0.0 Chemical derivative 837 0.0 -Label:2H(4)+Acetyl@K 46.035672 46.0613 H(-2)2H(4)C(2)O(1) 0.0 Isotopic label 834 0.0 -Pentylamine@Q 85.089149 85.1475 H(11)C(5)N(1) 0.0 Chemical derivative 801 0.0 -Biotin:Thermo-21345@Q 311.166748 311.4429 H(25)C(15)N(3)O(2)S(1) 0.0 Chemical derivative 800 0.0 -Dihydroxyimidazolidine@R 72.021129 72.0627 H(4)C(3)O(2) 0.0 Multiple 830 0.0 -Xlink:DFDNB@N 163.985807 164.0752 C(6)N(2)O(4) 0.0 Chemical derivative 825 0.0 -Xlink:DFDNB@Q 163.985807 164.0752 C(6)N(2)O(4) 0.0 Chemical derivative 825 0.0 -Xlink:DFDNB@R 163.985807 164.0752 C(6)N(2)O(4) 0.0 Chemical derivative 825 0.0 -Xlink:DFDNB@K 163.985807 164.0752 C(6)N(2)O(4) 0.0 Chemical derivative 825 0.0 -Cy3b-maleimide@C 682.24612 682.7852 H(38)C(37)N(4)O(7)S(1) 0.0 Chemical derivative 821 0.0 -Hex(1)HexNAc(1)@N 365.132196 365.3331 H(23)C(14)N(1)O(10) 365.132196 H(23)C(14)N(1)O(10) N-linked glycosylation 793 0.5 -Hex(1)HexNAc(1)@T 365.132196 365.3331 H(23)C(14)N(1)O(10) 365.132196 H(23)C(14)N(1)O(10) O-linked glycosylation 793 0.5 -Hex(1)HexNAc(1)@S 365.132196 365.3331 H(23)C(14)N(1)O(10) 365.132196 H(23)C(14)N(1)O(10) O-linked glycosylation 793 0.5 -AEC-MAEC:2H(4)@S 63.044462 63.158 H(1)2H(4)C(2)N(1)O(-1)S(1) 0.0 Isotopic label 792 0.0 -AEC-MAEC:2H(4)@T 63.044462 63.158 H(1)2H(4)C(2)N(1)O(-1)S(1) 0.0 Isotopic label 792 0.0 -Xlink:BMOE@C 220.048407 220.1815 H(8)C(10)N(2)O(4) 0.0 Chemical derivative 824 0.0 -Biotin:Thermo-21360@Anywhere 487.246455 487.6134 H(37)C(21)N(5)O(6)S(1) 0.0 Chemical derivative 811 0.0 -Label:13C(6)+Acetyl@K 48.030694 47.9926 H(2)C(-4)13C(6)O(1) 0.0 Isotopic label 835 0.0 -Label:13C(6)15N(2)+Acetyl@K 50.024764 49.9794 H(2)C(-4)13C(6)N(-2)15N(2)O(1) 0.0 Isotopic label 836 0.0 -EQIGG@K 484.228162 484.5035 H(32)C(20)N(6)O(8) 0.0 Other 846 0.0 -cGMP@S 343.031785 343.1895 H(10)C(10)N(5)O(7)P(1) 0.0 Post-translational 849 0.0 -cGMP@C 343.031785 343.1895 H(10)C(10)N(5)O(7)P(1) 0.0 Post-translational 849 0.0 -cGMP+RMP-loss@C 150.041585 150.1182 H(4)C(5)N(5)O(1) 0.0 Post-translational 851 0.0 -cGMP+RMP-loss@S 150.041585 150.1182 H(4)C(5)N(5)O(1) 0.0 Post-translational 851 0.0 -mTRAQ@Y 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 -mTRAQ@Any_N-term 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 -mTRAQ@K 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 -mTRAQ@H 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 -mTRAQ@S 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 -mTRAQ@T 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 -Arg2PG@R 266.057909 266.2482 H(10)C(16)O(4) 0.0 Chemical derivative 848 0.0 -Label:2H(4)+GG@K 118.068034 118.1273 H(2)2H(4)C(4)N(2)O(2) 0.0 Post-translational 853 0.0 -spermine@Q 185.189198 185.3097 H(23)C(10)N(3) 0.0 Chemical derivative 1420 0.0 -Label:13C(1)2H(3)@M 4.022185 4.0111 H(-3)2H(3)C(-1)13C(1) 0.0 Isotopic label 862 0.0 -ZGB@K 758.380841 758.7261 H(53)C(37)N(6)O(6)F(2)S(1)B(1) 0.0 Other 861 0.0 -ZGB@Any_N-term 758.380841 758.7261 H(53)C(37)N(6)O(6)F(2)S(1)B(1) 0.0 Other 861 0.0 -MG-H1@R 54.010565 54.0474 H(2)C(3)O(1) 0.0 Other 859 0.0 -G-H1@R 39.994915 40.0208 C(2)O(1) 0.0 Other 860 0.0 -Label:13C(6)15N(2)+GG@K 122.057126 122.0454 H(6)C(-2)13C(6)15N(2)O(2) 0.0 Isotopic label 864 0.0 -ICPL:13C(6)2H(4)@Any_N-term 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 -ICPL:13C(6)2H(4)@K 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 -ICPL:13C(6)2H(4)@Protein_N-term 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 -DyLight-maleimide@C 940.1999 941.0762 H(48)C(39)N(4)O(15)S(4) 0.0 Chemical derivative 890 0.0 -mTRAQ:13C(3)15N(1)@S 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 -mTRAQ:13C(3)15N(1)@T 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 -mTRAQ:13C(3)15N(1)@H 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 -mTRAQ:13C(3)15N(1)@Y 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 -mTRAQ:13C(3)15N(1)@Any_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 -mTRAQ:13C(3)15N(1)@K 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 -Methyl-PEO12-Maleimide@C 710.383719 710.8073 H(58)C(32)N(2)O(15) 0.0 Chemical derivative 891 0.0 -MDCC@C 383.148121 383.3978 H(21)C(20)N(3)O(5) 0.0 Chemical derivative 887 0.0 -QQQTGG@K 599.266339 599.5942 H(37)C(23)N(9)O(10) 0.0 Other 877 0.0 -QEQTGG@K 600.250354 600.5789 H(36)C(23)N(8)O(11) 0.0 Other 876 0.0 -HydroxymethylOP@K 108.021129 108.0948 H(4)C(6)O(2) 0.0 Other 886 0.0 -Biotin:Thermo-21325@K 695.310118 695.8288 H(45)C(34)N(7)O(7)S(1) 0.0 Chemical derivative 884 0.0 -Label:13C(1)2H(3)+Oxidation@M 20.0171 20.0105 H(-3)2H(3)C(-1)13C(1)O(1) 0.0 Multiple 885 0.0 -Bodipy@C 414.167478 414.2135 H(21)C(20)N(4)O(3)F(2)B(1) 0.0 Chemical derivative 878 0.0 -Biotin-PEG-PRA@M 578.317646 578.6611 H(42)C(26)N(8)O(7) 0.0 Chemical derivative 895 0.0 -Met->Aha@M -4.986324 -5.0794 H(-3)C(-1)N(3)S(-1) 0.0 Non-standard residue 896 0.0 -Label:15N(4)@R 3.98814 3.9736 N(-4)15N(4) 0.0 Isotopic label 897 0.0 -pyrophospho@T 159.932662 159.9598 H(2)O(6)P(2) 176.935402 H(3)O(7)P(2) Post-translational 898 0.5 -pyrophospho@S 159.932662 159.9598 H(2)O(6)P(2) 176.935402 H(3)O(7)P(2) Post-translational 898 0.5 -Met->Hpg@M -21.987721 -22.0702 H(-2)C(1)S(-1) 0.0 Non-standard residue 899 0.0 -4AcAllylGal@C 372.142033 372.3671 H(24)C(17)O(9) 0.0 Chemical derivative 901 0.0 -DimethylArsino@C 103.960719 103.9827 H(5)C(2)As(1) 0.0 Post-translational 902 0.0 -Lys->CamCys@K 31.935685 32.0219 H(-4)C(-1)O(1)S(1) 0.0 Pre-translational 903 0.0 -Phe->CamCys@F 12.962234 13.0204 H(-1)C(-4)N(1)O(1)S(1) 0.0 Pre-translational 904 0.0 -Leu->MetOx@L 33.951335 34.0378 H(-2)C(-1)O(1)S(1) 0.0 Pre-translational 905 0.0 -Lys->MetOx@K 18.940436 19.0232 H(-3)C(-1)N(-1)O(1)S(1) 0.0 Pre-translational 906 0.0 -Galactosyl@Any_N-term 178.047738 178.14 H(10)C(6)O(6) 0.0 Other glycosylation 907 0.0 -Galactosyl@K 178.047738 178.14 H(10)C(6)O(6) 0.0 Other glycosylation 907 0.0 -Xlink:SMCC[321]@C 321.205242 321.4146 H(27)C(17)N(3)O(3) 0.0 Chemical derivative 908 0.0 -Bacillosamine@N 228.111007 228.245 H(16)C(10)N(2)O(4) 228.111007 H(16)C(10)N(2)O(4) N-linked glycosylation 910 0.5 -MTSL@C 184.07961 184.2786 H(14)C(9)N(1)O(1)S(1) 0.0 Chemical derivative 911 0.0 -HNE-BAHAH@H 511.319226 511.7209 H(45)C(25)N(5)O(4)S(1) 0.0 Chemical derivative 912 0.0 -HNE-BAHAH@C 511.319226 511.7209 H(45)C(25)N(5)O(4)S(1) 0.0 Chemical derivative 912 0.0 -HNE-BAHAH@K 511.319226 511.7209 H(45)C(25)N(5)O(4)S(1) 0.0 Chemical derivative 912 0.0 -Ethoxyformyl@H 72.021129 72.0627 H(4)C(3)O(2) 0.0 Chemical derivative 915 0.0 -Methylmalonylation@S 100.016044 100.0728 H(4)C(4)O(3) 0.0 Chemical derivative 914 0.0 -AROD@C 820.336015 820.979 H(52)C(35)N(10)O(9)S(2) 0.0 Chemical derivative 938 0.0 -Cys->methylaminoAla@C -2.945522 -3.0238 H(3)C(1)N(1)S(-1) 0.0 Chemical derivative 939 0.0 -Cys->ethylaminoAla@C 11.070128 11.0028 H(5)C(2)N(1)S(-1) 0.0 Chemical derivative 940 0.0 -Label:13C(4)15N(2)+GG@K 120.050417 120.0601 H(6)13C(4)15N(2)O(2) 0.0 Isotopic label 923 0.0 -ethylamino@S 27.047285 27.0684 H(5)C(2)N(1)O(-1) 0.0 Chemical derivative 926 0.0 -ethylamino@T 27.047285 27.0684 H(5)C(2)N(1)O(-1) 0.0 Chemical derivative 926 0.0 -MercaptoEthanol@S 60.003371 60.1182 H(4)C(2)S(1) 0.0 Chemical derivative 928 0.0 -MercaptoEthanol@T 60.003371 60.1182 H(4)C(2)S(1) 0.0 Chemical derivative 928 0.0 -Atto495Maleimide@C 474.250515 474.5747 H(32)C(27)N(5)O(3) 0.0 Chemical derivative 935 0.0 -AMTzHexNAc2@T 502.202341 502.4757 H(30)C(19)N(6)O(10) 0.0 Chemical derivative 934 0.0 -AMTzHexNAc2@S 502.202341 502.4757 H(30)C(19)N(6)O(10) 0.0 Chemical derivative 934 0.0 -AMTzHexNAc2@N 502.202341 502.4757 H(30)C(19)N(6)O(10) 0.0 Chemical derivative 934 0.0 -Ethyl+Deamidated@Q 29.015316 29.0379 H(3)C(2)N(-1)O(1) 0.0 Chemical derivative 931 0.0 -Ethyl+Deamidated@N 29.015316 29.0379 H(3)C(2)N(-1)O(1) 0.0 Chemical derivative 931 0.0 -VFQQQTGG@K 845.403166 845.8991 H(55)C(37)N(11)O(12) 0.0 Other 932 0.0 -VIEVYQEQTGG@K 1203.577168 1204.2859 H(81)C(53)N(13)O(19) 0.0 Other 933 0.0 -Chlorination@W 33.961028 34.4451 H(-1)Cl(1) 0.0 Artefact 936 0.0 -Chlorination@Y 33.961028 34.4451 H(-1)Cl(1) 0.0 Artefact 936 0.0 -dichlorination@C 67.922055 68.8901 H(-2)Cl(2) 0.0 Chemical derivative 937 0.0 -dichlorination@Y 67.922055 68.8901 H(-2)Cl(2) 0.0 Artefact 937 0.0 -DNPS@C 198.981352 199.164 H(3)C(6)N(2)O(4)S(1) 0.0 Chemical derivative 941 0.0 -DNPS@W 198.981352 199.164 H(3)C(6)N(2)O(4)S(1) 0.0 Chemical derivative 941 0.0 -SulfoGMBS@C 458.162391 458.5306 H(26)C(22)N(4)O(5)S(1) 0.0 Other 942 0.0 -DimethylamineGMBS@C 267.158292 267.3241 H(21)C(13)N(3)O(3) 0.0 Chemical derivative 943 0.0 -Label:15N(2)2H(9)@K 11.050561 11.0423 H(-9)2H(9)N(-2)15N(2) 0.0 Isotopic label 944 0.0 -LG-anhydrolactam@Any_N-term 314.188195 314.4186 H(26)C(20)O(3) 0.0 Post-translational 946 0.0 -LG-anhydrolactam@K 314.188195 314.4186 H(26)C(20)O(3) 0.0 Post-translational 946 0.0 -LG-pyrrole@C 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 -LG-pyrrole@Any_N-term 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 -LG-pyrrole@K 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 -LG-anhyropyrrole@Any_N-term 298.19328 298.4192 H(26)C(20)O(2) 0.0 Post-translational 948 0.0 -LG-anhyropyrrole@K 298.19328 298.4192 H(26)C(20)O(2) 0.0 Post-translational 948 0.0 -3-deoxyglucosone@R 144.042259 144.1253 H(8)C(6)O(4) 0.0 Multiple 949 0.0 -Cation:Li@D 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 -Cation:Li@E 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 -Cation:Li@Any_C-term 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 -Cation:Ca[II]@Any_C-term 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 -Cation:Ca[II]@E 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 -Cation:Ca[II]@D 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 -Cation:Fe[II]@D 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 -Cation:Fe[II]@E 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 -Cation:Fe[II]@Any_C-term 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 -Cation:Ni[II]@D 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 -Cation:Ni[II]@E 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 -Cation:Ni[II]@Any_C-term 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 -Cation:Zn[II]@Any_C-term 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 -Cation:Zn[II]@E 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 -Cation:Zn[II]@D 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 -Cation:Zn[II]@H 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 -Cation:Ag@D 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 -Cation:Ag@E 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 -Cation:Ag@Any_C-term 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 -Cation:Mg[II]@D 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 -Cation:Mg[II]@E 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 -Cation:Mg[II]@Any_C-term 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 -2-succinyl@C 116.010959 116.0722 H(4)C(4)O(4) 0.0 Chemical derivative 957 0.0 -Propargylamine@D 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 -Propargylamine@Any_C-term 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 -Propargylamine@E 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 -Phosphopropargyl@T 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0.0 -Phosphopropargyl@Y 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0.0 -Phosphopropargyl@S 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0.0 -SUMO2135@K 2135.920496 2137.2343 H(137)C(90)N(21)O(37)S(1) 0.0 Other 960 0.0 -SUMO3549@K 3549.536568 3551.6672 H(224)C(150)N(38)O(60)S(1) 0.0 Other 961 0.0 -serotonylation@Q 159.068414 159.1846 H(9)C(10)N(1)O(1) 0.0 Post-translational 1992 0.0 -BITC@Any_N-term 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 -BITC@K 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 -BITC@C 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 -Carbofuran@S 58.029289 58.0593 H(4)C(2)N(1)O(1) 0.0 Chemical derivative 977 0.0 -PEITC@Any_N-term 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 -PEITC@K 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 -PEITC@C 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 -thioacylPA@K 159.035399 159.2062 H(9)C(6)N(1)O(2)S(1) 0.0 Chemical derivative 967 0.0 -maleimide3@K 969.366232 969.8975 H(59)C(37)N(7)O(23) 0.0 Post-translational 971 0.0 -maleimide3@C 969.366232 969.8975 H(59)C(37)N(7)O(23) 0.0 Post-translational 971 0.0 -maleimide5@K 1293.471879 1294.1787 H(79)C(49)N(7)O(33) 0.0 Post-translational 972 0.0 -maleimide5@C 1293.471879 1294.1787 H(79)C(49)N(7)O(33) 0.0 Post-translational 972 0.0 -Puromycin@Any_C-term 453.212452 453.4943 H(27)C(22)N(7)O(4) 0.0 Co-translational 973 0.0 -glucosone@R 160.037173 160.1247 H(8)C(6)O(5) 0.0 Other 981 0.0 -Label:13C(6)+Dimethyl@K 34.051429 34.0091 H(4)C(-4)13C(6) 0.0 Isotopic label 986 0.0 -cysTMT@C 299.166748 299.4322 H(25)C(14)N(3)O(2)S(1) 0.0 Chemical derivative 984 0.0 -cysTMT6plex@C 304.177202 304.3962 H(25)C(10)13C(4)N(2)15N(1)O(2)S(1) 0.0 Isotopic label 985 0.0 -ISD_z+2_ion@Any_N-term -15.010899 -15.0146 H(-1)N(-1) 0.0 Artefact 991 0.0 -Ammonium@E 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 -Ammonium@D 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 -Ammonium@Any_C-term 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 -Biotin:Sigma-B1267@C 449.17329 449.5239 H(27)C(20)N(5)O(5)S(1) 0.0 Chemical derivative 993 0.0 -Label:15N(1)@M 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@E 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@D 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@L 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@I 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@C 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@T 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@V 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@P 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@S 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@A 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@G 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@Y 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(1)@F 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 -Label:15N(2)@W 1.99407 1.9868 N(-2)15N(2) 0.0 Isotopic label 995 0.0 -Label:15N(2)@K 1.99407 1.9868 N(-2)15N(2) 0.0 Isotopic label 995 0.0 -Label:15N(2)@Q 1.99407 1.9868 N(-2)15N(2) 0.0 Isotopic label 995 0.0 -Label:15N(2)@N 1.99407 1.9868 N(-2)15N(2) 0.0 Isotopic label 995 0.0 -Label:15N(3)@H 2.991105 2.9802 N(-3)15N(3) 0.0 Isotopic label 996 0.0 -sulfo+amino@Y 94.967714 95.0778 H(1)N(1)O(3)S(1) 0.0 Chemical derivative 997 0.0 -AHA-Alkyne@M 107.077339 107.0504 H(5)C(4)N(5)O(1)S(-1) 0.0 Chemical derivative 1000 0.0 -AHA-Alkyne-KDDDD@M 695.280074 695.5723 H(37)C(26)N(11)O(14)S(-1) 0.0 Chemical derivative 1001 0.0 -EGCG1@C 456.069261 456.3558 H(16)C(22)O(11) 0.0 Post-translational 1002 0.0 -EGCG2@C 287.055563 287.2442 H(11)C(15)O(6) 0.0 Post-translational 1003 0.0 -Label:13C(6)15N(4)+Methyl@R 24.023919 23.9561 H(2)C(-5)13C(6)N(-4)15N(4) 0.0 Isotopic label 1004 0.0 -Label:13C(6)15N(4)+Dimethyl@R 38.039569 37.9827 H(4)C(-4)13C(6)N(-4)15N(4) 0.0 Isotopic label 1005 0.0 -Label:13C(6)15N(4)+Methyl:2H(3)13C(1)@R 28.046104 27.9673 H(-1)2H(3)C(-6)13C(7)N(-4)15N(4) 0.0 Isotopic label 1006 0.0 -Label:13C(6)15N(4)+Dimethyl:2H(6)13C(2)@R 46.083939 46.005 H(-2)2H(6)C(-6)13C(8)N(-4)15N(4) 0.0 Isotopic label 1007 0.0 -Cys->CamSec@C 104.965913 103.9463 H(3)C(2)N(1)O(1)S(-1)Se(1) 0.0 Non-standard residue 1008 0.0 -Thiazolidine@W 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -Thiazolidine@Y 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -Thiazolidine@H 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -Thiazolidine@R 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -Thiazolidine@K 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -Thiazolidine@Protein_N-term 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -Thiazolidine@C 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -Thiazolidine@F 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 -DEDGFLYMVYASQETFG@K 1970.824411 1972.088 H(122)C(89)N(18)O(31)S(1) 18.010565 H(2)O(1) Post-translational 1010 0.5 -Biotin:Invitrogen-M1602@C 523.210069 523.6024 H(33)C(23)N(5)O(7)S(1) 0.0 Chemical derivative 1012 0.0 -Xlink:DSS[156]@K 156.078644 156.1791 H(12)C(8)O(3) 0.0 Chemical derivative 1020 0.0 -Xlink:DSS[156]@Protein_N-term 156.078644 156.1791 H(12)C(8)O(3) 0.0 Chemical derivative 1020 0.0 -DMPO@H 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 -DMPO@Y 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 -DMPO@C 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 -glycidamide@K 87.032028 87.0773 H(5)C(3)N(1)O(2) 0.0 Chemical derivative 1014 0.0 -glycidamide@Any_N-term 87.032028 87.0773 H(5)C(3)N(1)O(2) 0.0 Chemical derivative 1014 0.0 -Ahx2+Hsl@Any_C-term 309.205242 309.4039 H(27)C(16)N(3)O(3) 0.0 Non-standard residue 1015 0.0 -ICDID@C 138.06808 138.1638 H(10)C(8)O(2) 0.0 Isotopic label 1018 0.0 -ICDID:2H(6)@C 144.10574 144.2008 H(4)2H(6)C(8)O(2) 0.0 Isotopic label 1019 0.0 -Xlink:EGS[244]@Protein_N-term 244.058303 244.1981 H(12)C(10)O(7) 0.0 Chemical derivative 1021 0.0 -Xlink:EGS[244]@K 244.058303 244.1981 H(12)C(10)O(7) 0.0 Chemical derivative 1021 0.0 -Xlink:DST[132]@Protein_N-term 132.005873 132.0716 H(4)C(4)O(5) 0.0 Chemical derivative 1022 0.0 -Xlink:DST[132]@K 132.005873 132.0716 H(4)C(4)O(5) 0.0 Chemical derivative 1022 0.0 -Xlink:DTSSP[192]@Protein_N-term 191.991486 192.2559 H(8)C(6)O(3)S(2) 0.0 Chemical derivative 1023 0.0 -Xlink:DTSSP[192]@K 191.991486 192.2559 H(8)C(6)O(3)S(2) 0.0 Chemical derivative 1023 0.0 -Xlink:SMCC[237]@C 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 -Xlink:SMCC[237]@K 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 -Xlink:SMCC[237]@Protein_N-term 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 -2-nitrobenzyl@Y 135.032028 135.1201 H(5)C(7)N(1)O(2) 0.0 Chemical derivative 1032 0.0 -Xlink:DMP[140]@Protein_N-term 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Chemical derivative 1027 0.0 -Xlink:DMP[140]@K 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Chemical derivative 1027 0.0 -Xlink:EGS[115]@Protein_N-term 115.026943 115.0874 H(5)C(4)N(1)O(3) 0.0 Chemical derivative 1028 0.0 -Xlink:EGS[115]@K 115.026943 115.0874 H(5)C(4)N(1)O(3) 0.0 Chemical derivative 1028 0.0 -Cys->SecNEM@C 172.992127 172.0203 H(7)C(6)N(1)O(2)S(-1)Se(1) 0.0 Non-standard residue 1033 0.0 -Cys->SecNEM:2H(5)@C 178.023511 177.0511 H(2)2H(5)C(6)N(1)O(2)S(-1)Se(1) 0.0 Chemical derivative 1034 0.0 -Thiadiazole@C 174.025169 174.2223 H(6)C(9)N(2)S(1) 0.0 Chemical derivative 1035 0.0 -Biotin:Thermo-88310@K 196.121178 196.2462 H(16)C(10)N(2)O(2) 0.0 Chemical derivative 1031 0.0 -TAMRA-FP@Y 659.312423 659.7514 H(46)C(37)N(3)O(6)P(1) 0.0 Chemical derivative 1038 0.0 -TAMRA-FP@S 659.312423 659.7514 H(46)C(37)N(3)O(6)P(1) 0.0 Chemical derivative 1038 0.0 -Biotin:Thermo-21901+H2O@C 543.236284 543.6336 H(37)C(23)N(5)O(8)S(1) 0.0 Chemical derivative 1039 0.0 -Deoxyhypusine@Q 71.073499 71.121 H(9)C(4)N(1) 0.0 Chemical derivative 1041 0.0 -Deoxyhypusine@K 71.073499 71.121 H(9)C(4)N(1) 0.0 Post-translational 1041 0.0 -Acetyldeoxyhypusine@K 97.089149 97.1582 H(11)C(6)N(1) 0.0 Post-translational 1042 0.0 -Acetylhypusine@K 113.084064 113.1576 H(11)C(6)N(1)O(1) 0.0 Post-translational 1043 0.0 -Ala->Cys@A 31.972071 32.065 H(0)C(0)N(0)O(0)S(1) 0.0 AA substitution 1044 0.0 -Ala->Phe@A 76.0313 76.096 H(4)C(6)N(0)O(0)S(0) 0.0 AA substitution 1045 0.0 -Ala->His@A 66.021798 66.0614 H(2)C(3)N(2)O(0)S(0) 0.0 AA substitution 1046 0.0 -Ala->Xle@A 42.04695 42.0797 H(6)C(3) 0.0 AA substitution 1047 0.0 -Ala->Lys@A 57.057849 57.0944 H(7)C(3)N(1)O(0)S(0) 0.0 AA substitution 1048 0.0 -Ala->Met@A 60.003371 60.1182 H(4)C(2)N(0)O(0)S(1) 0.0 AA substitution 1049 0.0 -Ala->Asn@A 43.005814 43.0247 H(1)C(1)N(1)O(1)S(0) 0.0 AA substitution 1050 0.0 -Ala->Gln@A 57.021464 57.0513 H(3)C(2)N(1)O(1)S(0) 0.0 AA substitution 1051 0.0 -Ala->Arg@A 85.063997 85.1078 H(7)C(3)N(3)O(0)S(0) 0.0 AA substitution 1052 0.0 -Ala->Trp@A 115.042199 115.132 H(5)C(8)N(1)O(0)S(0) 0.0 AA substitution 1053 0.0 -Ala->Tyr@A 92.026215 92.0954 H(4)C(6)N(0)O(1)S(0) 0.0 AA substitution 1054 0.0 -Cys->Ala@C -31.972071 -32.065 H(0)C(0)N(0)O(0)S(-1) 0.0 AA substitution 1055 0.0 -Cys->Asp@C 12.017759 11.9445 H(0)C(1)N(0)O(2)S(-1) 0.0 AA substitution 1056 0.0 -Cys->Glu@C 26.033409 25.9711 H(2)C(2)N(0)O(2)S(-1) 0.0 AA substitution 1057 0.0 -Cys->His@C 34.049727 33.9964 H(2)C(3)N(2)O(0)S(-1) 0.0 AA substitution 1058 0.0 -Cys->Xle@C 10.07488 10.0147 H(6)C(3)S(-1) 0.0 AA substitution 1059 0.0 -Cys->Lys@C 25.085779 25.0294 H(7)C(3)N(1)O(0)S(-1) 0.0 AA substitution 1060 0.0 -Cys->Met@C 28.0313 28.0532 H(4)C(2)N(0)O(0)S(0) 0.0 AA substitution 1061 0.0 -Cys->Asn@C 11.033743 10.9597 H(1)C(1)N(1)O(1)S(-1) 0.0 AA substitution 1062 0.0 -Cys->Pro@C -5.956421 -6.0277 H(2)C(2)N(0)O(0)S(-1) 0.0 AA substitution 1063 0.0 -Cys->Gln@C 25.049393 24.9863 H(3)C(2)N(1)O(1)S(-1) 0.0 AA substitution 1064 0.0 -Cys->Thr@C -1.961506 -2.039 H(2)C(1)N(0)O(1)S(-1) 0.0 AA substitution 1065 0.0 -Cys->Val@C -3.940771 -4.0118 H(4)C(2)N(0)O(0)S(-1) 0.0 AA substitution 1066 0.0 -Asp->Cys@D -12.017759 -11.9445 H(0)C(-1)N(0)O(-2)S(1) 0.0 AA substitution 1067 0.0 -Asp->Phe@D 32.041471 32.0865 H(4)C(5)N(0)O(-2)S(0) 0.0 AA substitution 1068 0.0 -Asp->Xle@D -1.942879 -1.9298 H(6)C(2)O(-2) 0.0 AA substitution 1069 0.0 -Asp->Lys@D 13.06802 13.0849 H(7)C(2)N(1)O(-2)S(0) 0.0 AA substitution 1070 0.0 -Asp->Met@D 16.013542 16.1087 H(4)C(1)N(0)O(-2)S(1) 0.0 AA substitution 1071 0.0 -Asp->Pro@D -17.974179 -17.9722 H(2)C(1)N(0)O(-2)S(0) 0.0 AA substitution 1072 0.0 -Asp->Gln@D 13.031634 13.0418 H(3)C(1)N(1)O(-1)S(0) 0.0 AA substitution 1073 0.0 -Asp->Arg@D 41.074168 41.0983 H(7)C(2)N(3)O(-2)S(0) 0.0 AA substitution 1074 0.0 -Asp->Ser@D -27.994915 -28.0101 H(0)C(-1)N(0)O(-1)S(0) 0.0 AA substitution 1075 0.0 -Asp->Thr@D -13.979265 -13.9835 H(2)C(0)N(0)O(-1)S(0) 0.0 AA substitution 1076 0.0 -Asp->Trp@D 71.05237 71.1225 H(5)C(7)N(1)O(-2)S(0) 0.0 AA substitution 1077 0.0 -Glu->Cys@E -26.033409 -25.9711 H(-2)C(-2)N(0)O(-2)S(1) 0.0 AA substitution 1078 0.0 -Glu->Phe@E 18.025821 18.0599 H(2)C(4)N(0)O(-2)S(0) 0.0 AA substitution 1079 0.0 -Glu->His@E 8.016319 8.0253 H(0)C(1)N(2)O(-2)S(0) 0.0 AA substitution 1080 0.0 -Glu->Xle@E -15.958529 -15.9563 H(4)C(1)O(-2) 0.0 AA substitution 1081 0.0 -Glu->Met@E 1.997892 2.0821 H(2)C(0)N(0)O(-2)S(1) 0.0 AA substitution 1082 0.0 -Glu->Asn@E -14.999666 -15.0113 H(-1)C(-1)N(1)O(-1)S(0) 0.0 AA substitution 1083 0.0 -Glu->Pro@E -31.989829 -31.9988 H(0)C(0)N(0)O(-2)S(0) 0.0 AA substitution 1084 0.0 -Glu->Arg@E 27.058518 27.0717 H(5)C(1)N(3)O(-2)S(0) 0.0 AA substitution 1085 0.0 -Glu->Ser@E -42.010565 -42.0367 H(-2)C(-2)N(0)O(-1)S(0) 0.0 AA substitution 1086 0.0 -Glu->Thr@E -27.994915 -28.0101 H(0)C(-1)N(0)O(-1)S(0) 0.0 AA substitution 1087 0.0 -Glu->Trp@E 57.03672 57.0959 H(3)C(6)N(1)O(-2)S(0) 0.0 AA substitution 1088 0.0 -Glu->Tyr@E 34.020735 34.0593 H(2)C(4)N(0)O(-1)S(0) 0.0 AA substitution 1089 0.0 -Phe->Ala@F -76.0313 -76.096 H(-4)C(-6)N(0)O(0)S(0) 0.0 AA substitution 1090 0.0 -Phe->Asp@F -32.041471 -32.0865 H(-4)C(-5)N(0)O(2)S(0) 0.0 AA substitution 1091 0.0 -Phe->Glu@F -18.025821 -18.0599 H(-2)C(-4)N(0)O(2)S(0) 0.0 AA substitution 1092 0.0 -Phe->Gly@F -90.04695 -90.1225 H(-6)C(-7)N(0)O(0)S(0) 0.0 AA substitution 1093 0.0 -Phe->His@F -10.009502 -10.0346 H(-2)C(-3)N(2)O(0)S(0) 0.0 AA substitution 1094 0.0 -Phe->Lys@F -18.973451 -19.0016 H(3)C(-3)N(1)O(0)S(0) 0.0 AA substitution 1095 0.0 -Phe->Met@F -16.027929 -15.9778 H(0)C(-4)N(0)O(0)S(1) 0.0 AA substitution 1096 0.0 -Phe->Asn@F -33.025486 -33.0712 H(-3)C(-5)N(1)O(1)S(0) 0.0 AA substitution 1097 0.0 -Phe->Pro@F -50.01565 -50.0587 H(-2)C(-4)N(0)O(0)S(0) 0.0 AA substitution 1098 0.0 -Phe->Gln@F -19.009836 -19.0446 H(-1)C(-4)N(1)O(1)S(0) 0.0 AA substitution 1099 0.0 -Phe->Arg@F 9.032697 9.0118 H(3)C(-3)N(3)O(0)S(0) 0.0 AA substitution 1100 0.0 -Phe->Thr@F -46.020735 -46.07 H(-2)C(-5)N(0)O(1)S(0) 0.0 AA substitution 1101 0.0 -Phe->Trp@F 39.010899 39.036 H(1)C(2)N(1)O(0)S(0) 0.0 AA substitution 1102 0.0 -Gly->Phe@G 90.04695 90.1225 H(6)C(7)N(0)O(0)S(0) 0.0 AA substitution 1103 0.0 -Gly->His@G 80.037448 80.088 H(4)C(4)N(2)O(0)S(0) 0.0 AA substitution 1104 0.0 -Gly->Xle@G 56.0626 56.1063 H(8)C(4) 0.0 AA substitution 1105 0.0 -Gly->Lys@G 71.073499 71.121 H(9)C(4)N(1)O(0)S(0) 0.0 AA substitution 1106 0.0 -Gly->Met@G 74.019021 74.1447 H(6)C(3)N(0)O(0)S(1) 0.0 AA substitution 1107 0.0 -Gly->Asn@G 57.021464 57.0513 H(3)C(2)N(1)O(1)S(0) 0.0 AA substitution 1108 0.0 -Gly->Pro@G 40.0313 40.0639 H(4)C(3)N(0)O(0)S(0) 0.0 AA substitution 1109 0.0 -Gly->Gln@G 71.037114 71.0779 H(5)C(3)N(1)O(1)S(0) 0.0 AA substitution 1110 0.0 -Gly->Thr@G 44.026215 44.0526 H(4)C(2)N(0)O(1)S(0) 0.0 AA substitution 1111 0.0 -Gly->Tyr@G 106.041865 106.1219 H(6)C(7)N(0)O(1)S(0) 0.0 AA substitution 1112 0.0 -His->Ala@H -66.021798 -66.0614 H(-2)C(-3)N(-2)O(0)S(0) 0.0 AA substitution 1113 0.0 -His->Cys@H -34.049727 -33.9964 H(-2)C(-3)N(-2)O(0)S(1) 0.0 AA substitution 1114 0.0 -His->Glu@H -8.016319 -8.0253 H(0)C(-1)N(-2)O(2)S(0) 0.0 AA substitution 1115 0.0 -His->Phe@H 10.009502 10.0346 H(2)C(3)N(-2)O(0)S(0) 0.0 AA substitution 1116 0.0 -His->Gly@H -80.037448 -80.088 H(-4)C(-4)N(-2)O(0)S(0) 0.0 AA substitution 1117 0.0 -His->Lys@H -8.963949 -8.967 H(5)C(0)N(-1)O(0)S(0) 0.0 AA substitution 1119 0.0 -His->Met@H -6.018427 -5.9432 H(2)C(-1)N(-2)O(0)S(1) 0.0 AA substitution 1120 0.0 -His->Ser@H -50.026883 -50.062 H(-2)C(-3)N(-2)O(1)S(0) 0.0 AA substitution 1121 0.0 -His->Thr@H -36.011233 -36.0354 H(0)C(-2)N(-2)O(1)S(0) 0.0 AA substitution 1122 0.0 -His->Val@H -37.990498 -38.0082 H(2)C(-1)N(-2)O(0)S(0) 0.0 AA substitution 1123 0.0 -His->Trp@H 49.020401 49.0706 H(3)C(5)N(-1)O(0)S(0) 0.0 AA substitution 1124 0.0 -Xle->Cys@L -10.07488 -10.0147 H(-6)C(-3)N(0)O(0)S(1) 0.0 AA substitution 1126 0.0 -Xle->Cys@I -10.07488 -10.0147 H(-6)C(-3)N(0)O(0)S(1) 0.0 AA substitution 1126 0.0 -Xle->Asp@L 1.942879 1.9298 H(-6)C(-2)N(0)O(2)S(0) 0.0 AA substitution 1127 0.0 -Xle->Asp@I 1.942879 1.9298 H(-6)C(-2)N(0)O(2)S(0) 0.0 AA substitution 1127 0.0 -Xle->Glu@L 15.958529 15.9563 H(-4)C(-1)N(0)O(2)S(0) 0.0 AA substitution 1128 0.0 -Xle->Glu@I 15.958529 15.9563 H(-4)C(-1)N(0)O(2)S(0) 0.0 AA substitution 1128 0.0 -Xle->Gly@L -56.0626 -56.1063 H(-8)C(-4)N(0)O(0)S(0) 0.0 AA substitution 1129 0.0 -Xle->Gly@I -56.0626 -56.1063 H(-8)C(-4)N(0)O(0)S(0) 0.0 AA substitution 1129 0.0 -Xle->Tyr@L 49.979265 50.0156 H(-2)C(3)N(0)O(1)S(0) 0.0 AA substitution 1130 0.0 -Xle->Tyr@I 49.979265 50.0156 H(-2)C(3)N(0)O(1)S(0) 0.0 AA substitution 1130 0.0 -Lys->Ala@K -57.057849 -57.0944 H(-7)C(-3)N(-1)O(0)S(0) 0.0 AA substitution 1131 0.0 -Lys->Cys@K -25.085779 -25.0294 H(-7)C(-3)N(-1)O(0)S(1) 0.0 AA substitution 1132 0.0 -Lys->Asp@K -13.06802 -13.0849 H(-7)C(-2)N(-1)O(2)S(0) 0.0 AA substitution 1133 0.0 -Lys->Phe@K 18.973451 19.0016 H(-3)C(3)N(-1)O(0)S(0) 0.0 AA substitution 1134 0.0 -Lys->Gly@K -71.073499 -71.121 H(-9)C(-4)N(-1)O(0)S(0) 0.0 AA substitution 1135 0.0 -Lys->His@K 8.963949 8.967 H(-5)C(0)N(1)O(0)S(0) 0.0 AA substitution 1136 0.0 -Lys->Pro@K -31.042199 -31.0571 H(-5)C(-1)N(-1)O(0)S(0) 0.0 AA substitution 1137 0.0 -Lys->Ser@K -41.062935 -41.095 H(-7)C(-3)N(-1)O(1)S(0) 0.0 AA substitution 1138 0.0 -Lys->Val@K -29.026549 -29.0412 H(-3)C(-1)N(-1)O(0)S(0) 0.0 AA substitution 1139 0.0 -Lys->Trp@K 57.98435 58.0376 H(-2)C(5)N(0)O(0)S(0) 0.0 AA substitution 1140 0.0 -Lys->Tyr@K 34.968366 35.001 H(-3)C(3)N(-1)O(1)S(0) 0.0 AA substitution 1141 0.0 -Met->Ala@M -60.003371 -60.1182 H(-4)C(-2)N(0)O(0)S(-1) 0.0 AA substitution 1142 0.0 -Met->Cys@M -28.0313 -28.0532 H(-4)C(-2)N(0)O(0)S(0) 0.0 AA substitution 1143 0.0 -Met->Asp@M -16.013542 -16.1087 H(-4)C(-1)N(0)O(2)S(-1) 0.0 AA substitution 1144 0.0 -Met->Glu@M -1.997892 -2.0821 H(-2)C(0)N(0)O(2)S(-1) 0.0 AA substitution 1145 0.0 -Met->Phe@M 16.027929 15.9778 H(0)C(4)N(0)O(0)S(-1) 0.0 AA substitution 1146 0.0 -Met->Gly@M -74.019021 -74.1447 H(-6)C(-3)N(0)O(0)S(-1) 0.0 AA substitution 1147 0.0 -Met->His@M 6.018427 5.9432 H(-2)C(1)N(2)O(0)S(-1) 0.0 AA substitution 1148 0.0 -Met->Asn@M -16.997557 -17.0934 H(-3)C(-1)N(1)O(1)S(-1) 0.0 AA substitution 1149 0.0 -Met->Pro@M -33.987721 -34.0809 H(-2)C(0)N(0)O(0)S(-1) 0.0 AA substitution 1150 0.0 -Met->Gln@M -2.981907 -3.0668 H(-1)C(0)N(1)O(1)S(-1) 0.0 AA substitution 1151 0.0 -Met->Ser@M -44.008456 -44.1188 H(-4)C(-2)N(0)O(1)S(-1) 0.0 AA substitution 1152 0.0 -Met->Trp@M 55.038828 55.0138 H(1)C(6)N(1)O(0)S(-1) 0.0 AA substitution 1153 0.0 -Met->Tyr@M 32.022844 31.9772 H(0)C(4)N(0)O(1)S(-1) 0.0 AA substitution 1154 0.0 -Asn->Ala@N -43.005814 -43.0247 H(-1)C(-1)N(-1)O(-1)S(0) 0.0 AA substitution 1155 0.0 -Asn->Cys@N -11.033743 -10.9597 H(-1)C(-1)N(-1)O(-1)S(1) 0.0 AA substitution 1156 0.0 -Asn->Glu@N 14.999666 15.0113 H(1)C(1)N(-1)O(1)S(0) 0.0 AA substitution 1157 0.0 -Asn->Phe@N 33.025486 33.0712 H(3)C(5)N(-1)O(-1)S(0) 0.0 AA substitution 1158 0.0 -Asn->Gly@N -57.021464 -57.0513 H(-3)C(-2)N(-1)O(-1)S(0) 0.0 AA substitution 1159 0.0 -Asn->Met@N 16.997557 17.0934 H(3)C(1)N(-1)O(-1)S(1) 0.0 AA substitution 1160 0.0 -Asn->Pro@N -16.990164 -16.9875 H(1)C(1)N(-1)O(-1)S(0) 0.0 AA substitution 1161 0.0 -Asn->Gln@N 14.01565 14.0266 H(2)C(1)N(0)O(0)S(0) 0.0 AA substitution 1162 0.0 -Asn->Arg@N 42.058184 42.083 H(6)C(2)N(2)O(-1)S(0) 0.0 AA substitution 1163 0.0 -Asn->Val@N -14.974514 -14.9716 H(3)C(1)N(-1)O(-1)S(0) 0.0 AA substitution 1164 0.0 -Asn->Trp@N 72.036386 72.1073 H(4)C(7)N(0)O(-1)S(0) 0.0 AA substitution 1165 0.0 -Pro->Cys@P 5.956421 6.0277 H(-2)C(-2)N(0)O(0)S(1) 0.0 AA substitution 1166 0.0 -Pro->Asp@P 17.974179 17.9722 H(-2)C(-1)N(0)O(2)S(0) 0.0 AA substitution 1167 0.0 -Pro->Glu@P 31.989829 31.9988 H(0)C(0)N(0)O(2)S(0) 0.0 AA substitution 1168 0.0 -Pro->Phe@P 50.01565 50.0587 H(2)C(4)N(0)O(0)S(0) 0.0 AA substitution 1169 0.0 -Pro->Gly@P -40.0313 -40.0639 H(-4)C(-3)N(0)O(0)S(0) 0.0 AA substitution 1170 0.0 -Pro->Lys@P 31.042199 31.0571 H(5)C(1)N(1)O(0)S(0) 0.0 AA substitution 1171 0.0 -Pro->Met@P 33.987721 34.0809 H(2)C(0)N(0)O(0)S(1) 0.0 AA substitution 1172 0.0 -Pro->Asn@P 16.990164 16.9875 H(-1)C(-1)N(1)O(1)S(0) 0.0 AA substitution 1173 0.0 -Pro->Val@P 2.01565 2.0159 H(2)C(0)N(0)O(0)S(0) 0.0 AA substitution 1174 0.0 -Pro->Trp@P 89.026549 89.0947 H(3)C(6)N(1)O(0)S(0) 0.0 AA substitution 1175 0.0 -Pro->Tyr@P 66.010565 66.0581 H(2)C(4)N(0)O(1)S(0) 0.0 AA substitution 1176 0.0 -Gln->Ala@Q -57.021464 -57.0513 H(-3)C(-2)N(-1)O(-1)S(0) 0.0 AA substitution 1177 0.0 -Gln->Cys@Q -25.049393 -24.9863 H(-3)C(-2)N(-1)O(-1)S(1) 0.0 AA substitution 1178 0.0 -Gln->Asp@Q -13.031634 -13.0418 H(-3)C(-1)N(-1)O(1)S(0) 0.0 AA substitution 1179 0.0 -Gln->Phe@Q 19.009836 19.0446 H(1)C(4)N(-1)O(-1)S(0) 0.0 AA substitution 1180 0.0 -Gln->Gly@Q -71.037114 -71.0779 H(-5)C(-3)N(-1)O(-1)S(0) 0.0 AA substitution 1181 0.0 -Gln->Met@Q 2.981907 3.0668 H(1)C(0)N(-1)O(-1)S(1) 0.0 AA substitution 1182 0.0 -Gln->Asn@Q -14.01565 -14.0266 H(-2)C(-1)N(0)O(0)S(0) 0.0 AA substitution 1183 0.0 -Gln->Ser@Q -41.026549 -41.0519 H(-3)C(-2)N(-1)O(0)S(0) 0.0 AA substitution 1184 0.0 -Gln->Thr@Q -27.010899 -27.0253 H(-1)C(-1)N(-1)O(0)S(0) 0.0 AA substitution 1185 0.0 -Gln->Val@Q -28.990164 -28.9982 H(1)C(0)N(-1)O(-1)S(0) 0.0 AA substitution 1186 0.0 -Gln->Trp@Q 58.020735 58.0807 H(2)C(6)N(0)O(-1)S(0) 0.0 AA substitution 1187 0.0 -Gln->Tyr@Q 35.004751 35.044 H(1)C(4)N(-1)O(0)S(0) 0.0 AA substitution 1188 0.0 -Arg->Ala@R -85.063997 -85.1078 H(-7)C(-3)N(-3)O(0)S(0) 0.0 AA substitution 1189 0.0 -Arg->Asp@R -41.074168 -41.0983 H(-7)C(-2)N(-3)O(2)S(0) 0.0 AA substitution 1190 0.0 -Arg->Glu@R -27.058518 -27.0717 H(-5)C(-1)N(-3)O(2)S(0) 0.0 AA substitution 1191 0.0 -Arg->Asn@R -42.058184 -42.083 H(-6)C(-2)N(-2)O(1)S(0) 0.0 AA substitution 1192 0.0 -Arg->Val@R -57.032697 -57.0546 H(-3)C(-1)N(-3)O(0)S(0) 0.0 AA substitution 1193 0.0 -Arg->Tyr@R 6.962218 6.9876 H(-3)C(3)N(-3)O(1)S(0) 0.0 AA substitution 1194 0.0 -Arg->Phe@R -9.032697 -9.0118 H(-3)C(3)N(-3) 0.0 AA substitution 1195 0.0 -Ser->Asp@S 27.994915 28.0101 H(0)C(1)N(0)O(1)S(0) 0.0 AA substitution 1196 0.0 -Ser->Glu@S 42.010565 42.0367 H(2)C(2)N(0)O(1)S(0) 0.0 AA substitution 1197 0.0 -Ser->His@S 50.026883 50.062 H(2)C(3)N(2)O(-1)S(0) 0.0 AA substitution 1198 0.0 -Ser->Lys@S 41.062935 41.095 H(7)C(3)N(1)O(-1)S(0) 0.0 AA substitution 1199 0.0 -Ser->Met@S 44.008456 44.1188 H(4)C(2)N(0)O(-1)S(1) 0.0 AA substitution 1200 0.0 -Ser->Gln@S 41.026549 41.0519 H(3)C(2)N(1)O(0)S(0) 0.0 AA substitution 1201 0.0 -Ser->Val@S 12.036386 12.0538 H(4)C(2)N(0)O(-1)S(0) 0.0 AA substitution 1202 0.0 -Thr->Cys@T 1.961506 2.039 H(-2)C(-1)N(0)O(-1)S(1) 0.0 AA substitution 1203 0.0 -Thr->Asp@T 13.979265 13.9835 H(-2)C(0)N(0)O(1)S(0) 0.0 AA substitution 1204 0.0 -Thr->Glu@T 27.994915 28.0101 H(0)C(1)N(0)O(1)S(0) 0.0 AA substitution 1205 0.0 -Thr->Phe@T 46.020735 46.07 H(2)C(5)N(0)O(-1)S(0) 0.0 AA substitution 1206 0.0 -Thr->Gly@T -44.026215 -44.0526 H(-4)C(-2)N(0)O(-1)S(0) 0.0 AA substitution 1207 0.0 -Thr->His@T 36.011233 36.0354 H(0)C(2)N(2)O(-1)S(0) 0.0 AA substitution 1208 0.0 -Thr->Gln@T 27.010899 27.0253 H(1)C(1)N(1)O(0)S(0) 0.0 AA substitution 1209 0.0 -Thr->Val@T -1.979265 -1.9728 H(2)C(1)N(0)O(-1)S(0) 0.0 AA substitution 1210 0.0 -Thr->Trp@T 85.031634 85.106 H(3)C(7)N(1)O(-1)S(0) 0.0 AA substitution 1211 0.0 -Thr->Tyr@T 62.01565 62.0694 H(2)C(5)N(0)O(0)S(0) 0.0 AA substitution 1212 0.0 -Val->Cys@V 3.940771 4.0118 H(-4)C(-2)N(0)O(0)S(1) 0.0 AA substitution 1213 0.0 -Val->His@V 37.990498 38.0082 H(-2)C(1)N(2)O(0)S(0) 0.0 AA substitution 1214 0.0 -Val->Lys@V 29.026549 29.0412 H(3)C(1)N(1)O(0)S(0) 0.0 AA substitution 1215 0.0 -Val->Asn@V 14.974514 14.9716 H(-3)C(-1)N(1)O(1)S(0) 0.0 AA substitution 1216 0.0 -Val->Pro@V -2.01565 -2.0159 H(-2)C(0)N(0)O(0)S(0) 0.0 AA substitution 1217 0.0 -Val->Gln@V 28.990164 28.9982 H(-1)C(0)N(1)O(1)S(0) 0.0 AA substitution 1218 0.0 -Val->Arg@V 57.032697 57.0546 H(3)C(1)N(3)O(0)S(0) 0.0 AA substitution 1219 0.0 -Val->Ser@V -12.036386 -12.0538 H(-4)C(-2)N(0)O(1)S(0) 0.0 AA substitution 1220 0.0 -Val->Thr@V 1.979265 1.9728 H(-2)C(-1)N(0)O(1)S(0) 0.0 AA substitution 1221 0.0 -Val->Trp@V 87.010899 87.0788 H(1)C(6)N(1)O(0)S(0) 0.0 AA substitution 1222 0.0 -Val->Tyr@V 63.994915 64.0422 H(0)C(4)N(0)O(1)S(0) 0.0 AA substitution 1223 0.0 -Trp->Ala@W -115.042199 -115.132 H(-5)C(-8)N(-1)O(0)S(0) 0.0 AA substitution 1224 0.0 -Trp->Asp@W -71.05237 -71.1225 H(-5)C(-7)N(-1)O(2)S(0) 0.0 AA substitution 1225 0.0 -Trp->Glu@W -57.03672 -57.0959 H(-3)C(-6)N(-1)O(2)S(0) 0.0 AA substitution 1226 0.0 -Trp->Phe@W -39.010899 -39.036 H(-1)C(-2)N(-1)O(0)S(0) 0.0 AA substitution 1227 0.0 -Trp->His@W -49.020401 -49.0706 H(-3)C(-5)N(1)O(0)S(0) 0.0 AA substitution 1228 0.0 -Trp->Lys@W -57.98435 -58.0376 H(2)C(-5)N(0)O(0)S(0) 0.0 AA substitution 1229 0.0 -Trp->Met@W -55.038828 -55.0138 H(-1)C(-6)N(-1)O(0)S(1) 0.0 AA substitution 1230 0.0 -Trp->Asn@W -72.036386 -72.1073 H(-4)C(-7)N(0)O(1)S(0) 0.0 AA substitution 1231 0.0 -Trp->Pro@W -89.026549 -89.0947 H(-3)C(-6)N(-1)O(0)S(0) 0.0 AA substitution 1232 0.0 -Trp->Gln@W -58.020735 -58.0807 H(-2)C(-6)N(0)O(1)S(0) 0.0 AA substitution 1233 0.0 -Trp->Thr@W -85.031634 -85.106 H(-3)C(-7)N(-1)O(1)S(0) 0.0 AA substitution 1234 0.0 -Trp->Val@W -87.010899 -87.0788 H(-1)C(-6)N(-1)O(0)S(0) 0.0 AA substitution 1235 0.0 -Trp->Tyr@W -23.015984 -23.0366 H(-1)C(-2)N(-1)O(1)S(0) 0.0 AA substitution 1236 0.0 -Tyr->Ala@Y -92.026215 -92.0954 H(-4)C(-6)N(0)O(-1)S(0) 0.0 AA substitution 1237 0.0 -Tyr->Glu@Y -34.020735 -34.0593 H(-2)C(-4)N(0)O(1)S(0) 0.0 AA substitution 1238 0.0 -Tyr->Gly@Y -106.041865 -106.1219 H(-6)C(-7)N(0)O(-1)S(0) 0.0 AA substitution 1239 0.0 -Tyr->Lys@Y -34.968366 -35.001 H(3)C(-3)N(1)O(-1)S(0) 0.0 AA substitution 1240 0.0 -Tyr->Met@Y -32.022844 -31.9772 H(0)C(-4)N(0)O(-1)S(1) 0.0 AA substitution 1241 0.0 -Tyr->Pro@Y -66.010565 -66.0581 H(-2)C(-4)N(0)O(-1)S(0) 0.0 AA substitution 1242 0.0 -Tyr->Gln@Y -35.004751 -35.044 H(-1)C(-4)N(1)O(0)S(0) 0.0 AA substitution 1243 0.0 -Tyr->Arg@Y -6.962218 -6.9876 H(3)C(-3)N(3)O(-1)S(0) 0.0 AA substitution 1244 0.0 -Tyr->Thr@Y -62.01565 -62.0694 H(-2)C(-5)N(0)O(0)S(0) 0.0 AA substitution 1245 0.0 -Tyr->Val@Y -63.994915 -64.0422 H(0)C(-4)N(0)O(-1)S(0) 0.0 AA substitution 1246 0.0 -Tyr->Trp@Y 23.015984 23.0366 H(1)C(2)N(1)O(-1)S(0) 0.0 AA substitution 1247 0.0 -Tyr->Xle@Y -49.979265 -50.0156 H(2)C(-3)O(-1) 0.0 AA substitution 1248 0.0 -AHA-SS@M 195.075625 195.1787 H(9)C(7)N(5)O(2) 0.0 Multiple 1249 0.0 -AHA-SS_CAM@M 252.097088 252.23 H(12)C(9)N(6)O(3) 0.0 Multiple 1250 0.0 -Biotin:Thermo-33033@Anywhere 548.223945 548.7211 H(36)C(25)N(6)O(4)S(2) 0.0 Chemical derivative 1251 0.0 -Biotin:Thermo-33033-H@Anywhere 546.208295 546.7053 H(34)C(25)N(6)O(4)S(2) 0.0 Chemical derivative 1252 0.0 -2-monomethylsuccinyl@C 130.026609 130.0987 H(6)C(5)O(4) 0.0 Chemical derivative 1253 0.0 -Saligenin@H 106.041865 106.1219 H(6)C(7)O(1) 0.0 Chemical derivative 1254 0.0 -Saligenin@K 106.041865 106.1219 H(6)C(7)O(1) 0.0 Chemical derivative 1254 0.0 -Cresylphosphate@R 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 -Cresylphosphate@S 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 -Cresylphosphate@T 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 -Cresylphosphate@Y 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 -Cresylphosphate@K 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 -Cresylphosphate@H 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 -CresylSaligeninPhosphate@R 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 -CresylSaligeninPhosphate@S 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 -CresylSaligeninPhosphate@T 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 -CresylSaligeninPhosphate@Y 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 -CresylSaligeninPhosphate@K 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 -CresylSaligeninPhosphate@H 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 -Ub-Br2@C 100.063663 100.1191 H(8)C(4)N(2)O(1) 0.0 Chemical derivative 1257 0.0 -Ub-VME@C 173.092617 173.1897 H(13)C(7)N(2)O(3) 0.0 Chemical derivative 1258 0.0 -Ub-amide@C 196.108602 196.2264 H(14)C(9)N(3)O(2) 0.0 Chemical derivative 1260 0.0 -Ub-fluorescein@C 597.209772 597.598 H(29)C(31)N(6)O(7) 0.0 Chemical derivative 1261 0.0 -2-dimethylsuccinyl@C 144.042259 144.1253 H(8)C(6)O(4) 0.0 Chemical derivative 1262 0.0 -Gly@T 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Other 1263 0.0 -Gly@S 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Other 1263 0.0 -Gly@K 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Other 1263 0.0 -pupylation@K 243.085521 243.2166 H(13)C(9)N(3)O(5) 0.0 Post-translational 1264 0.0 -Label:13C(4)@M 4.013419 3.9706 C(-4)13C(4) 0.0 Isotopic label 1266 0.0 -HCysteinyl@C 133.019749 133.1689 H(7)C(4)N(1)O(2)S(1) 0.0 Post-translational 1271 0.0 -Label:13C(4)+Oxidation@M 20.008334 19.97 C(-4)13C(4)O(1) 0.0 Isotopic label 1267 0.0 -UgiJoullie@E 1106.48935 1107.1274 H(60)C(47)N(23)O(10) 0.0 Chemical derivative 1276 0.0 -UgiJoullie@D 1106.48935 1107.1274 H(60)C(47)N(23)O(10) 0.0 Chemical derivative 1276 0.0 -HCysThiolactone@K 117.024835 117.1695 H(7)C(4)N(1)O(1)S(1) 0.0 Post-translational 1270 0.0 -UgiJoullieProGly@D 154.074228 154.1665 H(10)C(7)N(2)O(2) 0.0 Chemical derivative 1282 0.0 -UgiJoullieProGly@E 154.074228 154.1665 H(10)C(7)N(2)O(2) 0.0 Chemical derivative 1282 0.0 -Dipyridyl@C 225.090212 225.2459 H(11)C(13)N(3)O(1) 0.0 Chemical derivative 1277 0.0 -Furan@Y 66.010565 66.0581 H(2)C(4)O(1) 0.0 Chemical derivative 1278 0.0 -Difuran@Y 132.021129 132.1162 H(4)C(8)O(2) 0.0 Chemical derivative 1279 0.0 -BMP-piperidinol@C 263.131014 263.3337 H(17)C(18)N(1)O(1) 0.0 Chemical derivative 1281 0.0 -BMP-piperidinol@M 263.131014 263.3337 H(17)C(18)N(1)O(1) 0.0 Chemical derivative 1281 0.0 -UgiJoullieProGlyProGly@D 308.148455 308.333 H(20)C(14)N(4)O(4) 0.0 Chemical derivative 1283 0.0 -UgiJoullieProGlyProGly@E 308.148455 308.333 H(20)C(14)N(4)O(4) 0.0 Chemical derivative 1283 0.0 -Arg-loss@R^Any_C-term -156.101111 -156.1857 H(-12)C(-6)N(-4)O(-1) 0.0 Other 1287 0.0 -Arg@Any_N-term 156.101111 156.1857 H(12)C(6)N(4)O(1) 0.0 Other 1288 0.0 -IMEHex(2)NeuAc(1)@K 688.199683 688.6527 H(40)C(25)N(2)O(18)S(1) 0.0 Other glycosylation 1286 0.0 -Butyryl@K 70.041865 70.0898 H(6)C(4)O(1) 0.0 Post-translational 1289 0.0 -Dicarbamidomethyl@K 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 -Dicarbamidomethyl@H 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 -Dicarbamidomethyl@C 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 -Dicarbamidomethyl@R 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 -Dicarbamidomethyl@Any_N-term 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 -Dimethyl:2H(6)@K 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 -Dimethyl:2H(6)@Any_N-term 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 -Dimethyl:2H(6)@R 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 -GGQ@K 242.101505 242.2319 H(14)C(9)N(4)O(4) 0.0 Other 1292 0.0 -QTGG@K 343.149184 343.3357 H(21)C(13)N(5)O(6) 0.0 Other 1293 0.0 -Label:13C(3)15N(1)@A 4.007099 3.9714 C(-3)13C(3)N(-1)15N(1) 0.0 Isotopic label 1297 0.0 -Label:13C(3)15N(1)@S 4.007099 3.9714 C(-3)13C(3)N(-1)15N(1) 0.0 Isotopic label 1297 0.0 -Label:13C(3)@A 3.010064 2.978 C(-3)13C(3) 0.0 Isotopic label 1296 0.0 -Label:13C(4)15N(1)@D 5.010454 4.964 C(-4)13C(4)N(-1)15N(1) 0.0 Isotopic label 1298 0.0 -Label:2H(10)@L 10.062767 10.0616 H(-10)2H(10) 0.0 Isotopic label 1299 0.0 -Label:2H(4)13C(1)@R 5.028462 5.0173 H(-4)2H(4)C(-1)13C(1) 0.0 Isotopic label 1300 0.0 -Lys@Any_N-term 128.094963 128.1723 H(12)C(6)N(2)O(1) 0.0 Other 1301 0.0 -mTRAQ:13C(6)15N(2)@K 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 -mTRAQ:13C(6)15N(2)@Any_N-term 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 -mTRAQ:13C(6)15N(2)@Y 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 -mTRAQ:13C(6)15N(2)@H 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 -mTRAQ:13C(6)15N(2)@S 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 -mTRAQ:13C(6)15N(2)@T 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 -NeuAc@T 291.095417 291.2546 H(17)C(11)N(1)O(8) 291.095417 H(17)C(11)N(1)O(8) O-linked glycosylation 1303 0.5 -NeuAc@S 291.095417 291.2546 H(17)C(11)N(1)O(8) 291.095417 H(17)C(11)N(1)O(8) O-linked glycosylation 1303 0.5 -NeuAc@N 291.095417 291.2546 H(17)C(11)N(1)O(8) 291.095417 H(17)C(11)N(1)O(8) N-linked glycosylation 1303 0.5 -NeuGc@T 307.090331 307.254 H(17)C(11)N(1)O(9) 307.090331 H(17)C(11)N(1)O(9) O-linked glycosylation 1304 0.5 -NeuGc@S 307.090331 307.254 H(17)C(11)N(1)O(9) 307.090331 H(17)C(11)N(1)O(9) O-linked glycosylation 1304 0.5 -NeuGc@N 307.090331 307.254 H(17)C(11)N(1)O(9) 307.090331 H(17)C(11)N(1)O(9) N-linked glycosylation 1304 0.5 -Propyl@D 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 -Propyl@K 42.04695 42.0797 H(6)C(3) 0.0 Isotopic label 1305 0.0 -Propyl@Any_N-term 42.04695 42.0797 H(6)C(3) 0.0 Isotopic label 1305 0.0 -Propyl@E 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 -Propyl@Any_C-term 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 -Propyl@Protein_C-term 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 -Propyl:2H(6)@Any_N-term 48.084611 48.1167 2H(6)C(3) 0.0 Isotopic label 1306 0.0 -Propyl:2H(6)@K 48.084611 48.1167 2H(6)C(3) 0.0 Isotopic label 1306 0.0 -Propiophenone@C 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 -Propiophenone@W 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 -Propiophenone@T 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 -Propiophenone@S 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 -Propiophenone@R 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 -Propiophenone@K 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 -Propiophenone@H 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 -PS_Hapten@H 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 -PS_Hapten@C 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 -PS_Hapten@K 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 -Cy3-maleimide@C 753.262796 753.9046 H(45)C(37)N(4)O(9)S(2) 0.0 Chemical derivative 1348 0.0 -Delta:H(6)C(3)O(1)@Protein_N-term 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 -Delta:H(6)C(3)O(1)@K 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 -Delta:H(6)C(3)O(1)@H 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 -Delta:H(6)C(3)O(1)@C 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 -Delta:H(8)C(6)O(1)@Protein_N-term 96.057515 96.1271 H(8)C(6)O(1) 0.0 Chemical derivative 1313 0.0 -Delta:H(8)C(6)O(1)@K 96.057515 96.1271 H(8)C(6)O(1) 0.0 Chemical derivative 1313 0.0 -biotinAcrolein298@H 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 -biotinAcrolein298@K 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 -biotinAcrolein298@Protein_N-term 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 -biotinAcrolein298@C 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 -MM-diphenylpentanone@C 265.146664 265.3496 H(19)C(18)N(1)O(1) 0.0 Chemical derivative 1315 0.0 -EHD-diphenylpentanone@M 266.13068 266.3343 H(18)C(18)O(2) 0.0 Chemical derivative 1317 0.0 -EHD-diphenylpentanone@C 266.13068 266.3343 H(18)C(18)O(2) 0.0 Chemical derivative 1317 0.0 -benzylguanidine@K 132.068748 132.1625 H(8)C(8)N(2) 0.0 Chemical derivative 1349 0.0 -CarboxymethylDMAP@Any_N-term 162.079313 162.1885 H(10)C(9)N(2)O(1) 0.0 Chemical derivative 1350 0.0 -Biotin:Thermo-21901+2H2O@C 561.246849 561.6489 H(39)C(23)N(5)O(9)S(1) 0.0 Chemical derivative 1320 0.0 -DiLeu4plex115@K 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 -DiLeu4plex115@Any_N-term 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 -DiLeu4plex115@Y 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 -DiLeu4plex@Any_N-term 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 -DiLeu4plex@K 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 -DiLeu4plex@Y 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 -DiLeu4plex117@K 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 -DiLeu4plex117@Any_N-term 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 -DiLeu4plex117@Y 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 -DiLeu4plex118@K 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 -DiLeu4plex118@Any_N-term 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 -DiLeu4plex118@Y 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 -Xlink:BuUrBu[213]@K 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 -Xlink:BuUrBu[213]@Protein_N-term 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 -bisANS-sulfonates@S 437.201774 437.5543 H(25)C(32)N(2) 0.0 Chemical derivative 1330 0.0 -bisANS-sulfonates@T 437.201774 437.5543 H(25)C(32)N(2) 0.0 Chemical derivative 1330 0.0 -bisANS-sulfonates@K 437.201774 437.5543 H(25)C(32)N(2) 0.0 Chemical derivative 1330 0.0 -DNCB_hapten@Y 166.001457 166.0911 H(2)C(6)N(2)O(4) 0.0 Chemical derivative 1331 0.0 -DNCB_hapten@H 166.001457 166.0911 H(2)C(6)N(2)O(4) 0.0 Chemical derivative 1331 0.0 -DNCB_hapten@K 166.001457 166.0911 H(2)C(6)N(2)O(4) 0.0 Chemical derivative 1331 0.0 -DNCB_hapten@C 166.001457 166.0911 H(2)C(6)N(2)O(4) 0.0 Chemical derivative 1331 0.0 -NEMsulfur@C 157.019749 157.1903 H(7)C(6)N(1)O(2)S(1) 0.0 Chemical derivative 1326 0.0 -SulfurDioxide@C 63.9619 64.0638 O(2)S(1) 0.0 Post-translational 1327 0.0 -NEMsulfurWater@C 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1328 0.0 -HN3_mustard@C 131.094629 131.1729 H(13)C(6)N(1)O(2) 0.0 Post-translational 1389 0.0 -HN3_mustard@H 131.094629 131.1729 H(13)C(6)N(1)O(2) 0.0 Post-translational 1389 0.0 -HN3_mustard@K 131.094629 131.1729 H(13)C(6)N(1)O(2) 0.0 Post-translational 1389 0.0 -3-phosphoglyceryl@K 167.982375 168.042 H(5)C(3)O(6)P(1) 0.0 Post-translational 1387 0.0 -HN2_mustard@H 101.084064 101.1469 H(11)C(5)N(1)O(1) 0.0 Post-translational 1388 0.0 -HN2_mustard@K 101.084064 101.1469 H(11)C(5)N(1)O(1) 0.0 Post-translational 1388 0.0 -HN2_mustard@C 101.084064 101.1469 H(11)C(5)N(1)O(1) 0.0 Post-translational 1388 0.0 -NEM:2H(5)+H2O@C 148.089627 148.1714 H(4)2H(5)C(6)N(1)O(3) 0.0 Chemical derivative 1358 0.0 -Crotonyl@K 68.026215 68.074 H(4)C(4)O(1) 0.0 Post-translational 1363 0.0 -O-Et-N-diMePhospho@S 135.044916 135.1015 H(10)C(4)N(1)O(2)P(1) 0.0 Chemical derivative 1364 0.0 -N-dimethylphosphate@S 107.013615 107.0483 H(6)C(2)N(1)O(2)P(1) 0.0 Chemical derivative 1365 0.0 -phosphoRibosyl@E 212.00859 212.0945 H(9)C(5)O(7)P(1) 0.0 Post-translational 1356 0.0 -phosphoRibosyl@R 212.00859 212.0945 H(9)C(5)O(7)P(1) 0.0 Post-translational 1356 0.0 -phosphoRibosyl@D 212.00859 212.0945 H(9)C(5)O(7)P(1) 0.0 Post-translational 1356 0.0 -azole@C -20.026215 -20.0312 H(-4)O(-1) 0.0 Post-translational 1355 0.0 -azole@S -20.026215 -20.0312 H(-4)O(-1) 0.0 Post-translational 1355 0.0 -Biotin:Thermo-21911@C 921.461652 922.0913 H(71)C(41)N(5)O(16)S(1) 0.0 Chemical derivative 1340 0.0 -iodoTMT@K 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 -iodoTMT@H 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 -iodoTMT@E 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 -iodoTMT@D 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 -iodoTMT@C 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 -iodoTMT6plex@K 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 -iodoTMT6plex@H 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 -iodoTMT6plex@E 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 -iodoTMT6plex@D 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 -iodoTMT6plex@C 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 -Label:13C(2)15N(2)@K 4.00078 3.9721 C(-2)13C(2)N(-2)15N(2) 0.0 Isotopic label 1787 0.0 -Phosphogluconoylation@Any_N-term 258.014069 258.1199 H(11)C(6)O(9)P(1) 0.0 Post-translational 1344 0.0 -Phosphogluconoylation@K 258.014069 258.1199 H(11)C(6)O(9)P(1) 0.0 Post-translational 1344 0.0 -Methyl:2H(3)+Acetyl:2H(3)@K 62.063875 62.1002 H(-2)2H(6)C(3)O(1) 0.0 Isotopic label 1368 0.0 -dHex(1)Hex(1)@T 308.110732 308.2818 H(20)C(12)O(9) 308.110732 H(20)C(12)O(9) O-linked glycosylation 1367 0.5 -dHex(1)Hex(1)@S 308.110732 308.2818 H(20)C(12)O(9) 308.110732 H(20)C(12)O(9) O-linked glycosylation 1367 0.5 -methylsulfonylethyl@K 106.00885 106.1435 H(6)C(3)O(2)S(1) 0.0 Chemical derivative 1380 0.0 -methylsulfonylethyl@H 106.00885 106.1435 H(6)C(3)O(2)S(1) 0.0 Chemical derivative 1380 0.0 -methylsulfonylethyl@C 106.00885 106.1435 H(6)C(3)O(2)S(1) 0.0 Chemical derivative 1380 0.0 -Label:2H(3)+Oxidation@M 19.013745 19.0179 H(-3)2H(3)O(1) 0.0 Isotopic label 1370 0.0 -Trimethyl:2H(9)@R 51.103441 51.1352 H(-3)2H(9)C(3) 0.0 Isotopic label 1371 0.0 -Trimethyl:2H(9)@K 51.103441 51.1352 H(-3)2H(9)C(3) 0.0 Isotopic label 1371 0.0 -Acetyl:13C(2)@K 44.017274 44.022 H(2)13C(2)O(1) 0.0 Isotopic label 1372 0.0 -Acetyl:13C(2)@Protein_N-term 44.017274 44.022 H(2)13C(2)O(1) 0.0 Isotopic label 1372 0.0 -dHex(1)Hex(2)@T 470.163556 470.4224 H(30)C(18)O(14) 470.163556 H(30)C(18)O(14) O-linked glycosylation 1375 0.5 -dHex(1)Hex(2)@S 470.163556 470.4224 H(30)C(18)O(14) 470.163556 H(30)C(18)O(14) O-linked glycosylation 1375 0.5 -dHex(1)Hex(3)@T 632.216379 632.563 H(40)C(24)O(19) 632.216379 H(40)C(24)O(19) O-linked glycosylation 1376 0.5 -dHex(1)Hex(3)@S 632.216379 632.563 H(40)C(24)O(19) 632.216379 H(40)C(24)O(19) O-linked glycosylation 1376 0.5 -dHex(1)Hex(4)@T 794.269203 794.7036 H(50)C(30)O(24) 794.269203 H(50)C(30)O(24) O-linked glycosylation 1377 0.5 -dHex(1)Hex(4)@S 794.269203 794.7036 H(50)C(30)O(24) 794.269203 H(50)C(30)O(24) O-linked glycosylation 1377 0.5 -dHex(1)Hex(5)@T 956.322026 956.8442 H(60)C(36)O(29) 956.322026 H(60)C(36)O(29) O-linked glycosylation 1378 0.5 -dHex(1)Hex(5)@S 956.322026 956.8442 H(60)C(36)O(29) 956.322026 H(60)C(36)O(29) O-linked glycosylation 1378 0.5 -dHex(1)Hex(6)@T 1118.37485 1118.9848 H(70)C(42)O(34) 1118.37485 H(70)C(42)O(34) O-linked glycosylation 1379 0.5 -dHex(1)Hex(6)@S 1118.37485 1118.9848 H(70)C(42)O(34) 1118.37485 H(70)C(42)O(34) O-linked glycosylation 1379 0.5 -ethylsulfonylethyl@H 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 1381 0.0 -ethylsulfonylethyl@C 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 1381 0.0 -ethylsulfonylethyl@K 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 1381 0.0 -phenylsulfonylethyl@C 168.0245 168.2129 H(8)C(8)O(2)S(1) 0.0 Chemical derivative 1382 0.0 -PyridoxalPhosphateH2@K 231.02966 231.1425 H(10)C(8)N(1)O(5)P(1) 0.0 Chemical derivative 1383 0.0 -Homocysteic_acid@M 33.969094 33.9716 H(-2)C(-1)O(3) 0.0 Artefact 1384 0.0 -Hydroxamic_acid@E 15.010899 15.0146 H(1)N(1) 0.0 Artefact 1385 0.0 -Hydroxamic_acid@D 15.010899 15.0146 H(1)N(1) 0.0 Artefact 1385 0.0 -Oxidation+NEM@C 141.042593 141.1247 H(7)C(6)N(1)O(3) 0.0 Chemical derivative 1390 0.0 -NHS-fluorescein@K 471.131802 471.4581 H(21)C(27)N(1)O(7) 0.0 Chemical derivative 1391 0.0 -DiART6plex@Y 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 -DiART6plex@Protein_N-term 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 -DiART6plex@Any_N-term 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 -DiART6plex@K 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 -DiART6plex115@K 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 -DiART6plex115@Any_N-term 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 -DiART6plex115@Protein_N-term 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 -DiART6plex115@Y 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 -DiART6plex116/119@Y 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 -DiART6plex116/119@Protein_N-term 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 -DiART6plex116/119@K 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 -DiART6plex116/119@Any_N-term 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 -DiART6plex117@K 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 -DiART6plex117@Any_N-term 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 -DiART6plex117@Protein_N-term 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 -DiART6plex117@Y 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 -DiART6plex118@K 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 -DiART6plex118@Any_N-term 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 -DiART6plex118@Protein_N-term 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 -DiART6plex118@Y 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 -Iodoacetanilide@K 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Artefact 1397 0.0 -Iodoacetanilide@C 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Chemical derivative 1397 0.0 -Iodoacetanilide@Any_N-term 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Artefact 1397 0.0 -Iodoacetanilide:13C(6)@K 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Artefact 1398 0.0 -Iodoacetanilide:13C(6)@C 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Chemical derivative 1398 0.0 -Iodoacetanilide:13C(6)@Any_N-term 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Artefact 1398 0.0 -Dap-DSP@K 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Chemical derivative 1399 0.0 -Dap-DSP@E 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Non-standard residue 1399 0.0 -Dap-DSP@A 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Non-standard residue 1399 0.0 -MurNAc@A 275.100502 275.2552 H(17)C(11)N(1)O(7) 0.0 Other glycosylation 1400 0.0 -EEEDVIEVYQEQTGG@K 1705.73189 1706.7153 H(107)C(72)N(17)O(31) 0.0 Chemical derivative 1405 0.0 -Label:2H(7)15N(4)@R 11.032077 11.0168 H(-7)2H(7)N(-4)15N(4) 0.0 Isotopic label 1402 0.0 -Label:2H(6)15N(1)@P 7.034695 7.0304 H(-6)2H(6)N(-1)15N(1) 0.0 Isotopic label 1403 0.0 -EDEDTIDVFQQQTGG@K 1662.700924 1663.6508 H(102)C(69)N(18)O(30) 0.0 Chemical derivative 1406 0.0 -Hex(5)HexNAc(4)NeuAc(2)@N 2204.772441 2205.9822 H(136)C(84)N(6)O(61) 2204.772441 H(136)C(84)N(6)O(61) N-linked glycosylation 1408 0.5 -Hex(5)HexNAc(4)NeuAc(1)@N 1913.677025 1914.7277 H(119)C(73)N(5)O(53) 1913.677025 H(119)C(73)N(5)O(53) N-linked glycosylation 1409 0.5 -dHex(1)Hex(5)HexNAc(4)NeuAc(1)@N 2059.734933 2060.8689 H(129)C(79)N(5)O(57) 2059.734933 H(129)C(79)N(5)O(57) N-linked glycosylation 1410 0.5 -dHex(1)Hex(5)HexNAc(4)NeuAc(2)@N 2350.83035 2352.1234 H(146)C(90)N(6)O(65) 2350.83035 H(146)C(90)N(6)O(65) N-linked glycosylation 1411 0.5 -s-GlcNAc@T 283.036187 283.2557 H(13)C(8)N(1)O(8)S(1) 283.036187 H(13)C(8)N(1)O(8)S(1) O-linked glycosylation 1412 0.5 -s-GlcNAc@S 283.036187 283.2557 H(13)C(8)N(1)O(8)S(1) 283.036187 H(13)C(8)N(1)O(8)S(1) O-linked glycosylation 1412 0.5 -PhosphoHex(2)@N 404.071978 404.2611 H(21)C(12)O(13)P(1) 404.071978 H(21)C(12)O(13)P(1) N-linked glycosylation 1413 0.5 -PhosphoHex(2)@T 404.071978 404.2611 H(21)C(12)O(13)P(1) 404.071978 H(21)C(12)O(13)P(1) O-linked glycosylation 1413 0.5 -PhosphoHex(2)@S 404.071978 404.2611 H(21)C(12)O(13)P(1) 404.071978 H(21)C(12)O(13)P(1) O-linked glycosylation 1413 0.5 -Trimethyl:13C(3)2H(9)@K 54.113505 54.1132 H(-3)2H(9)13C(3) 0.0 Isotopic label 1414 0.0 -Trimethyl:13C(3)2H(9)@R 54.113505 54.1132 H(-3)2H(9)13C(3) 0.0 Isotopic label 1414 0.0 -15N-oxobutanoic@S^Protein_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Post-translational 1419 0.0 -15N-oxobutanoic@C^Any_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Artefact 1419 0.0 -15N-oxobutanoic@T^Protein_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Post-translational 1419 0.0 -spermidine@Q 128.131349 128.2153 H(16)C(7)N(2) 0.0 Chemical derivative 1421 0.0 -Biotin:Thermo-21330@Any_N-term 473.219571 473.5835 H(35)C(21)N(3)O(7)S(1) 0.0 Chemical derivative 1423 0.0 -Biotin:Thermo-21330@K 473.219571 473.5835 H(35)C(21)N(3)O(7)S(1) 0.0 Chemical derivative 1423 0.0 -Hex(1)Pent(2)@T 426.137341 426.3698 H(26)C(16)O(13) 426.137341 H(26)C(16)O(13) O-linked glycosylation 1428 0.5 -Hex(1)Pent(2)@S 426.137341 426.3698 H(26)C(16)O(13) 426.137341 H(26)C(16)O(13) O-linked glycosylation 1428 0.5 -Pentose@T 132.042259 132.1146 H(8)C(5)O(4) 132.042259 H(8)C(5)O(4) O-linked glycosylation 1425 0.5 -Pentose@S 132.042259 132.1146 H(8)C(5)O(4) 132.042259 H(8)C(5)O(4) O-linked glycosylation 1425 0.5 -Hex(1)Pent(1)@T 294.095082 294.2552 H(18)C(11)O(9) 294.095082 H(18)C(11)O(9) O-linked glycosylation 1426 0.5 -Hex(1)Pent(1)@S 294.095082 294.2552 H(18)C(11)O(9) 294.095082 H(18)C(11)O(9) O-linked glycosylation 1426 0.5 -Hex(1)HexA(1)@T 338.084912 338.2647 H(18)C(12)O(11) 338.084912 H(18)C(12)O(11) O-linked glycosylation 1427 0.5 -Hex(1)HexA(1)@S 338.084912 338.2647 H(18)C(12)O(11) 338.084912 H(18)C(12)O(11) O-linked glycosylation 1427 0.5 -Hex(1)HexNAc(1)Phos(1)@T 445.098527 445.313 H(24)C(14)N(1)O(13)P(1) 445.098527 H(24)C(14)N(1)O(13)P(1) O-linked glycosylation 1429 0.5 -Hex(1)HexNAc(1)Phos(1)@S 445.098527 445.313 H(24)C(14)N(1)O(13)P(1) 445.098527 H(24)C(14)N(1)O(13)P(1) O-linked glycosylation 1429 0.5 -Hex(1)HexNAc(1)Sulf(1)@T 445.089011 445.3963 H(23)C(14)N(1)O(13)S(1) 445.089011 H(23)C(14)N(1)O(13)S(1) O-linked glycosylation 1430 0.5 -Hex(1)HexNAc(1)Sulf(1)@S 445.089011 445.3963 H(23)C(14)N(1)O(13)S(1) 445.089011 H(23)C(14)N(1)O(13)S(1) O-linked glycosylation 1430 0.5 -Hex(1)NeuAc(1)@T 453.14824 453.3952 H(27)C(17)N(1)O(13) 453.14824 H(27)C(17)N(1)O(13) O-linked glycosylation 1431 0.5 -Hex(1)NeuAc(1)@S 453.14824 453.3952 H(27)C(17)N(1)O(13) 453.14824 H(27)C(17)N(1)O(13) O-linked glycosylation 1431 0.5 -Hex(1)NeuGc(1)@T 469.143155 469.3946 H(27)C(17)N(1)O(14) 469.143155 H(27)C(17)N(1)O(14) O-linked glycosylation 1432 0.5 -Hex(1)NeuGc(1)@S 469.143155 469.3946 H(27)C(17)N(1)O(14) 469.143155 H(27)C(17)N(1)O(14) O-linked glycosylation 1432 0.5 -HexNAc(3)@T 609.238118 609.5776 H(39)C(24)N(3)O(15) 609.238118 H(39)C(24)N(3)O(15) O-linked glycosylation 1433 0.5 -HexNAc(3)@S 609.238118 609.5776 H(39)C(24)N(3)O(15) 609.238118 H(39)C(24)N(3)O(15) O-linked glycosylation 1433 0.5 -HexNAc(1)NeuAc(1)@T 494.174789 494.4471 H(30)C(19)N(2)O(13) 494.174789 H(30)C(19)N(2)O(13) O-linked glycosylation 1434 0.5 -HexNAc(1)NeuAc(1)@S 494.174789 494.4471 H(30)C(19)N(2)O(13) 494.174789 H(30)C(19)N(2)O(13) O-linked glycosylation 1434 0.5 -HexNAc(1)NeuGc(1)@T 510.169704 510.4465 H(30)C(19)N(2)O(14) 510.169704 H(30)C(19)N(2)O(14) O-linked glycosylation 1435 0.5 -HexNAc(1)NeuGc(1)@S 510.169704 510.4465 H(30)C(19)N(2)O(14) 510.169704 H(30)C(19)N(2)O(14) O-linked glycosylation 1435 0.5 -Hex(2)NeuAc(1)@T 615.201064 615.5358 H(37)C(23)N(1)O(18) 615.201064 H(37)C(23)N(1)O(18) O-linked glycosylation 1444 0.5 -Hex(2)NeuAc(1)@S 615.201064 615.5358 H(37)C(23)N(1)O(18) 615.201064 H(37)C(23)N(1)O(18) O-linked glycosylation 1444 0.5 -Hex(1)HexNAc(1)dHex(1)Me(1)@T 525.205755 525.5009 H(35)C(21)N(1)O(14) 525.205755 H(35)C(21)N(1)O(14) O-linked glycosylation 1436 0.5 -Hex(1)HexNAc(1)dHex(1)Me(1)@S 525.205755 525.5009 H(35)C(21)N(1)O(14) 525.205755 H(35)C(21)N(1)O(14) O-linked glycosylation 1436 0.5 -Hex(1)HexNAc(1)dHex(1)Me(2)@T 539.221405 539.5275 H(37)C(22)N(1)O(14) 539.221405 H(37)C(22)N(1)O(14) O-linked glycosylation 1437 0.5 -Hex(1)HexNAc(1)dHex(1)Me(2)@S 539.221405 539.5275 H(37)C(22)N(1)O(14) 539.221405 H(37)C(22)N(1)O(14) O-linked glycosylation 1437 0.5 -Xlink:DSS[155]@Protein_N-term 155.094629 155.1943 H(13)C(8)N(1)O(2) 0.0 Chemical derivative 1789 0.0 -Xlink:DSS[155]@K 155.094629 155.1943 H(13)C(8)N(1)O(2) 0.0 Chemical derivative 1789 0.0 -Hex(2)HexNAc(1)@N 527.18502 527.4737 H(33)C(20)N(1)O(15) 527.18502 H(33)C(20)N(1)O(15) N-linked glycosylation 1438 0.5 -Hex(2)HexNAc(1)@T 527.18502 527.4737 H(33)C(20)N(1)O(15) 527.18502 H(33)C(20)N(1)O(15) O-linked glycosylation 1438 0.5 -Hex(2)HexNAc(1)@S 527.18502 527.4737 H(33)C(20)N(1)O(15) 527.18502 H(33)C(20)N(1)O(15) O-linked glycosylation 1438 0.5 -Hex(1)HexA(1)HexNAc(1)@T 541.164284 541.4572 H(31)C(20)N(1)O(16) 541.164284 H(31)C(20)N(1)O(16) O-linked glycosylation 1439 0.5 -Hex(1)HexA(1)HexNAc(1)@S 541.164284 541.4572 H(31)C(20)N(1)O(16) 541.164284 H(31)C(20)N(1)O(16) O-linked glycosylation 1439 0.5 -Hex(2)HexNAc(1)Me(1)@T 541.20067 541.5003 H(35)C(21)N(1)O(15) 541.20067 H(35)C(21)N(1)O(15) O-linked glycosylation 1440 0.5 -Hex(2)HexNAc(1)Me(1)@S 541.20067 541.5003 H(35)C(21)N(1)O(15) 541.20067 H(35)C(21)N(1)O(15) O-linked glycosylation 1440 0.5 -Hex(1)Pent(3)@T 558.1796 558.4845 H(34)C(21)O(17) 558.1796 H(34)C(21)O(17) O-linked glycosylation 1441 0.5 -Hex(1)Pent(3)@S 558.1796 558.4845 H(34)C(21)O(17) 558.1796 H(34)C(21)O(17) O-linked glycosylation 1441 0.5 -Hex(1)NeuAc(1)Pent(1)@S 585.190499 585.5098 H(35)C(22)N(1)O(17) 585.190499 H(35)C(22)N(1)O(17) O-linked glycosylation 1442 0.5 -Hex(1)NeuAc(1)Pent(1)@T 585.190499 585.5098 H(35)C(22)N(1)O(17) 585.190499 H(35)C(22)N(1)O(17) O-linked glycosylation 1442 0.5 -Hex(2)HexNAc(1)Sulf(1)@T 607.141834 607.5369 H(33)C(20)N(1)O(18)S(1) 607.141834 H(33)C(20)N(1)O(18)S(1) O-linked glycosylation 1443 0.5 -Hex(2)HexNAc(1)Sulf(1)@S 607.141834 607.5369 H(33)C(20)N(1)O(18)S(1) 607.141834 H(33)C(20)N(1)O(18)S(1) O-linked glycosylation 1443 0.5 -dHex(2)Hex(2)@S 616.221465 616.5636 H(40)C(24)O(18) 616.221465 H(40)C(24)O(18) O-linked glycosylation 1445 0.5 -dHex(2)Hex(2)@T 616.221465 616.5636 H(40)C(24)O(18) 616.221465 H(40)C(24)O(18) O-linked glycosylation 1445 0.5 -dHex(1)Hex(2)HexA(1)@S 646.195644 646.5465 H(38)C(24)O(20) 646.195644 H(38)C(24)O(20) O-linked glycosylation 1446 0.5 -dHex(1)Hex(2)HexA(1)@T 646.195644 646.5465 H(38)C(24)O(20) 646.195644 H(38)C(24)O(20) O-linked glycosylation 1446 0.5 -Hex(1)HexNAc(2)Sulf(1)@T 648.168383 648.5888 H(36)C(22)N(2)O(18)S(1) 648.168383 H(36)C(22)N(2)O(18)S(1) O-linked glycosylation 1447 0.5 -Hex(1)HexNAc(2)Sulf(1)@S 648.168383 648.5888 H(36)C(22)N(2)O(18)S(1) 648.168383 H(36)C(22)N(2)O(18)S(1) O-linked glycosylation 1447 0.5 -Hex(4)@S 648.211294 648.5624 H(40)C(24)O(20) 648.211294 H(40)C(24)O(20) O-linked glycosylation 1448 0.5 -Hex(4)@T 648.211294 648.5624 H(40)C(24)O(20) 648.211294 H(40)C(24)O(20) O-linked glycosylation 1448 0.5 -dHex(1)Hex(2)HexNAc(2)Pent(1)@N 1008.36456 1008.9221 H(64)C(39)N(2)O(28) 1008.36456 H(64)C(39)N(2)O(28) N-linked glycosylation 1449 0.5 -Hex(2)HexNAc(2)NeuAc(1)@N 1021.359809 1021.9208 H(63)C(39)N(3)O(28) 1021.359809 H(63)C(39)N(3)O(28) N-linked glycosylation 1450 0.5 -Hex(2)HexNAc(2)NeuAc(1)@S 1021.359809 1021.9208 H(63)C(39)N(3)O(28) 1021.359809 H(63)C(39)N(3)O(28) O-linked glycosylation 1450 0.5 -Hex(2)HexNAc(2)NeuAc(1)@T 1021.359809 1021.9208 H(63)C(39)N(3)O(28) 1021.359809 H(63)C(39)N(3)O(28) O-linked glycosylation 1450 0.5 -Hex(3)HexNAc(2)Pent(1)@N 1024.359475 1024.9215 H(64)C(39)N(2)O(29) 1024.359475 H(64)C(39)N(2)O(29) N-linked glycosylation 1451 0.5 -Hex(4)HexNAc(2)@N 1054.370039 1054.9474 H(66)C(40)N(2)O(30) 1054.370039 H(66)C(40)N(2)O(30) N-linked glycosylation 1452 0.5 -dHex(1)Hex(4)HexNAc(1)Pent(1)@N 1129.390834 1130.0107 H(71)C(43)N(1)O(33) 1129.390834 H(71)C(43)N(1)O(33) N-linked glycosylation 1453 0.5 -dHex(1)Hex(3)HexNAc(2)Pent(1)@N 1170.417383 1171.0627 H(74)C(45)N(2)O(33) 1170.417383 H(74)C(45)N(2)O(33) N-linked glycosylation 1454 0.5 -Hex(3)HexNAc(2)NeuAc(1)@N 1183.412632 1184.0614 H(73)C(45)N(3)O(33) 1183.412632 H(73)C(45)N(3)O(33) N-linked glycosylation 1455 0.5 -Hex(4)HexNAc(2)Pent(1)@N 1186.412298 1187.0621 H(74)C(45)N(2)O(34) 1186.412298 H(74)C(45)N(2)O(34) N-linked glycosylation 1456 0.5 -Hex(3)HexNAc(3)Pent(1)@N 1227.438847 1228.114 H(77)C(47)N(3)O(34) 1227.438847 H(77)C(47)N(3)O(34) N-linked glycosylation 1457 0.5 -Hex(5)HexNAc(2)Phos(1)@N 1296.389194 1297.0679 H(77)C(46)N(2)O(38)P(1) 1296.389194 H(77)C(46)N(2)O(38)P(1) N-linked glycosylation 1458 0.5 -dHex(1)Hex(4)HexNAc(2)Pent(1)@N 1332.470207 1333.2033 H(84)C(51)N(2)O(38) 1332.470207 H(84)C(51)N(2)O(38) N-linked glycosylation 1459 0.5 -Hex(7)HexNAc(1)@N 1337.449137 1338.1767 H(83)C(50)N(1)O(40) 1337.449137 H(83)C(50)N(1)O(40) N-linked glycosylation 1460 0.5 -Hex(4)HexNAc(2)NeuAc(1)@N 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) N-linked glycosylation 1461 0.5 -Hex(4)HexNAc(2)NeuAc(1)@S 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) O-linked glycosylation 1461 0.5 -Hex(4)HexNAc(2)NeuAc(1)@T 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) O-linked glycosylation 1461 0.5 -dHex(1)Hex(5)HexNAc(2)@N 1362.480772 1363.2292 H(86)C(52)N(2)O(39) 1362.480772 H(86)C(52)N(2)O(39) N-linked glycosylation 1462 0.5 -dHex(1)Hex(3)HexNAc(3)Pent(1)@N 1373.496756 1374.2552 H(87)C(53)N(3)O(38) 1373.496756 H(87)C(53)N(3)O(38) N-linked glycosylation 1463 0.5 -Hex(3)HexNAc(4)Sulf(1)@N 1378.432776 1379.2551 H(82)C(50)N(4)O(38)S(1) 1378.432776 H(82)C(50)N(4)O(38)S(1) N-linked glycosylation 1464 0.5 -Hex(6)HexNAc(2)@N 1378.475686 1379.2286 H(86)C(52)N(2)O(40) 1378.475686 H(86)C(52)N(2)O(40) N-linked glycosylation 1465 0.5 -Hex(4)HexNAc(3)Pent(1)@N 1389.491671 1390.2546 H(87)C(53)N(3)O(39) 1389.491671 H(87)C(53)N(3)O(39) N-linked glycosylation 1466 0.5 -dHex(1)Hex(4)HexNAc(3)@N 1403.507321 1404.2812 H(89)C(54)N(3)O(39) 1403.507321 H(89)C(54)N(3)O(39) N-linked glycosylation 1467 0.5 -Hex(5)HexNAc(3)@N 1419.502235 1420.2806 H(89)C(54)N(3)O(40) 1419.502235 H(89)C(54)N(3)O(40) N-linked glycosylation 1468 0.5 -Hex(3)HexNAc(4)Pent(1)@N 1430.51822 1431.3065 H(90)C(55)N(4)O(39) 1430.51822 H(90)C(55)N(4)O(39) N-linked glycosylation 1469 0.5 -Hex(6)HexNAc(2)Phos(1)@N 1458.442017 1459.2085 H(87)C(52)N(2)O(43)P(1) 1458.442017 H(87)C(52)N(2)O(43)P(1) N-linked glycosylation 1470 0.5 -dHex(1)Hex(4)HexNAc(3)Sulf(1)@N 1483.464135 1484.3444 H(89)C(54)N(3)O(42)S(1) 1483.464135 H(89)C(54)N(3)O(42)S(1) N-linked glycosylation 1471 0.5 -dHex(1)Hex(5)HexNAc(2)Pent(1)@N 1494.52303 1495.3439 H(94)C(57)N(2)O(43) 1494.52303 H(94)C(57)N(2)O(43) N-linked glycosylation 1472 0.5 -Hex(8)HexNAc(1)@N 1499.501961 1500.3173 H(93)C(56)N(1)O(45) 1499.501961 H(93)C(56)N(1)O(45) N-linked glycosylation 1473 0.5 -dHex(1)Hex(3)HexNAc(3)Pent(2)@N 1505.539015 1506.3698 H(95)C(58)N(3)O(42) 1505.539015 H(95)C(58)N(3)O(42) N-linked glycosylation 1474 0.5 -dHex(2)Hex(3)HexNAc(3)Pent(1)@N 1519.554665 1520.3964 H(97)C(59)N(3)O(42) 1519.554665 H(97)C(59)N(3)O(42) N-linked glycosylation 1475 0.5 -dHex(1)Hex(3)HexNAc(4)Sulf(1)@N 1524.490684 1525.3963 H(92)C(56)N(4)O(42)S(1) 1524.490684 H(92)C(56)N(4)O(42)S(1) N-linked glycosylation 1476 0.5 -dHex(1)Hex(6)HexNAc(2)@N 1524.533595 1525.3698 H(96)C(58)N(2)O(44) 1524.533595 H(96)C(58)N(2)O(44) N-linked glycosylation 1477 0.5 -dHex(1)Hex(4)HexNAc(3)Pent(1)@N 1535.549579 1536.3958 H(97)C(59)N(3)O(43) 1535.549579 H(97)C(59)N(3)O(43) N-linked glycosylation 1478 0.5 -Hex(4)HexNAc(4)Sulf(1)@N 1540.485599 1541.3957 H(92)C(56)N(4)O(43)S(1) 1540.485599 H(92)C(56)N(4)O(43)S(1) N-linked glycosylation 1479 0.5 -Hex(7)HexNAc(2)@N 1540.52851 1541.3692 H(96)C(58)N(2)O(45) 1540.52851 H(96)C(58)N(2)O(45) N-linked glycosylation 1480 0.5 -dHex(2)Hex(4)HexNAc(3)@N 1549.56523 1550.4224 H(99)C(60)N(3)O(43) 1549.56523 H(99)C(60)N(3)O(43) N-linked glycosylation 1481 0.5 -Hex(5)HexNAc(3)Pent(1)@N 1551.544494 1552.3952 H(97)C(59)N(3)O(44) 1551.544494 H(97)C(59)N(3)O(44) N-linked glycosylation 1482 0.5 -Hex(4)HexNAc(3)NeuGc(1)@N 1564.539743 1565.3939 H(96)C(59)N(4)O(44) 1564.539743 H(96)C(59)N(4)O(44) N-linked glycosylation 1483 0.5 -dHex(1)Hex(5)HexNAc(3)@N 1565.560144 1566.4218 H(99)C(60)N(3)O(44) 1565.560144 H(99)C(60)N(3)O(44) N-linked glycosylation 1484 0.5 -dHex(1)Hex(3)HexNAc(4)Pent(1)@N 1576.576129 1577.4477 H(100)C(61)N(4)O(43) 1576.576129 H(100)C(61)N(4)O(43) N-linked glycosylation 1485 0.5 -Hex(3)HexNAc(5)Sulf(1)@N 1581.512148 1582.4476 H(95)C(58)N(5)O(43)S(1) 1581.512148 H(95)C(58)N(5)O(43)S(1) N-linked glycosylation 1486 0.5 -Hex(6)HexNAc(3)@N 1581.555059 1582.4212 H(99)C(60)N(3)O(45) 1581.555059 H(99)C(60)N(3)O(45) N-linked glycosylation 1487 0.5 -Hex(3)HexNAc(4)NeuAc(1)@N 1589.571378 1590.4465 H(99)C(61)N(5)O(43) 1589.571378 H(99)C(61)N(5)O(43) N-linked glycosylation 1488 0.5 -Hex(4)HexNAc(4)Pent(1)@N 1592.571043 1593.4471 H(100)C(61)N(4)O(44) 1592.571043 H(100)C(61)N(4)O(44) N-linked glycosylation 1489 0.5 -Hex(7)HexNAc(2)Phos(1)@N 1620.494841 1621.3491 H(97)C(58)N(2)O(48)P(1) 1620.494841 H(97)C(58)N(2)O(48)P(1) N-linked glycosylation 1490 0.5 -Hex(4)HexNAc(4)Me(2)Pent(1)@N 1620.602343 1621.5003 H(104)C(63)N(4)O(44) 1620.602343 H(104)C(63)N(4)O(44) N-linked glycosylation 1491 0.5 -dHex(1)Hex(3)HexNAc(3)Pent(3)@N 1637.581274 1638.4844 H(103)C(63)N(3)O(46) 1637.581274 H(103)C(63)N(3)O(46) N-linked glycosylation 1492 0.5 -dHex(1)Hex(5)HexNAc(3)Sulf(1)@N 1645.516959 1646.485 H(99)C(60)N(3)O(47)S(1) 1645.516959 H(99)C(60)N(3)O(47)S(1) N-linked glycosylation 1493 0.5 -dHex(2)Hex(3)HexNAc(3)Pent(2)@N 1651.596924 1652.511 H(105)C(64)N(3)O(46) 1651.596924 H(105)C(64)N(3)O(46) N-linked glycosylation 1494 0.5 -Hex(6)HexNAc(3)Phos(1)@N 1661.52139 1662.4011 H(100)C(60)N(3)O(48)P(1) 1661.52139 H(100)C(60)N(3)O(48)P(1) N-linked glycosylation 1495 0.5 -Hex(4)HexNAc(5)@N 1663.608157 1664.525 H(105)C(64)N(5)O(45) 1663.608157 H(105)C(64)N(5)O(45) N-linked glycosylation 1496 0.5 -dHex(3)Hex(3)HexNAc(3)Pent(1)@N 1665.612574 1666.5376 H(107)C(65)N(3)O(46) 1665.612574 H(107)C(65)N(3)O(46) N-linked glycosylation 1497 0.5 -dHex(2)Hex(4)HexNAc(3)Pent(1)@N 1681.607488 1682.537 H(107)C(65)N(3)O(47) 1681.607488 H(107)C(65)N(3)O(47) N-linked glycosylation 1498 0.5 -dHex(1)Hex(4)HexNAc(4)Sulf(1)@N 1686.543508 1687.5369 H(102)C(62)N(4)O(47)S(1) 1686.543508 H(102)C(62)N(4)O(47)S(1) N-linked glycosylation 1499 0.5 -dHex(1)Hex(7)HexNAc(2)@N 1686.586419 1687.5104 H(106)C(64)N(2)O(49) 1686.586419 H(106)C(64)N(2)O(49) N-linked glycosylation 1500 0.5 -dHex(1)Hex(4)HexNAc(3)NeuAc(1)@N 1694.602737 1695.5357 H(106)C(65)N(4)O(47) 1694.602737 H(106)C(65)N(4)O(47) N-linked glycosylation 1501 0.5 -dHex(1)Hex(4)HexNAc(3)NeuAc(1)@S 1694.602737 1695.5357 H(106)C(65)N(4)O(47) 1694.602737 H(106)C(65)N(4)O(47) O-linked glycosylation 1501 0.5 -dHex(1)Hex(4)HexNAc(3)NeuAc(1)@T 1694.602737 1695.5357 H(106)C(65)N(4)O(47) 1694.602737 H(106)C(65)N(4)O(47) O-linked glycosylation 1501 0.5 -Hex(7)HexNAc(2)Phos(2)@N 1700.461172 1701.329 H(98)C(58)N(2)O(51)P(2) 1700.461172 H(98)C(58)N(2)O(51)P(2) N-linked glycosylation 1502 0.5 -Hex(5)HexNAc(4)Sulf(1)@N 1702.538423 1703.5363 H(102)C(62)N(4)O(48)S(1) 1702.538423 H(102)C(62)N(4)O(48)S(1) N-linked glycosylation 1503 0.5 -Hex(8)HexNAc(2)@N 1702.581333 1703.5098 H(106)C(64)N(2)O(50) 1702.581333 H(106)C(64)N(2)O(50) N-linked glycosylation 1504 0.5 -dHex(1)Hex(3)HexNAc(4)Pent(2)@N 1708.618387 1709.5623 H(108)C(66)N(4)O(47) 1708.618387 H(108)C(66)N(4)O(47) N-linked glycosylation 1505 0.5 -dHex(1)Hex(4)HexNAc(3)NeuGc(1)@N 1710.597652 1711.5351 H(106)C(65)N(4)O(48) 1710.597652 H(106)C(65)N(4)O(48) N-linked glycosylation 1506 0.5 -dHex(2)Hex(3)HexNAc(4)Pent(1)@N 1722.634037 1723.5889 H(110)C(67)N(4)O(47) 1722.634037 H(110)C(67)N(4)O(47) N-linked glycosylation 1507 0.5 -dHex(1)Hex(3)HexNAc(5)Sulf(1)@N 1727.570057 1728.5888 H(105)C(64)N(5)O(47)S(1) 1727.570057 H(105)C(64)N(5)O(47)S(1) N-linked glycosylation 1508 0.5 -dHex(1)Hex(6)HexNAc(3)@N 1727.612968 1728.5624 H(109)C(66)N(3)O(49) 1727.612968 H(109)C(66)N(3)O(49) N-linked glycosylation 1509 0.5 -dHex(1)Hex(3)HexNAc(4)NeuAc(1)@N 1735.629286 1736.5877 H(109)C(67)N(5)O(47) 1735.629286 H(109)C(67)N(5)O(47) N-linked glycosylation 1510 0.5 -dHex(3)Hex(3)HexNAc(4)@N 1736.649688 1737.6155 H(112)C(68)N(4)O(47) 1736.649688 H(112)C(68)N(4)O(47) N-linked glycosylation 1511 0.5 -dHex(1)Hex(4)HexNAc(4)Pent(1)@N 1738.628952 1739.5883 H(110)C(67)N(4)O(48) 1738.628952 H(110)C(67)N(4)O(48) N-linked glycosylation 1512 0.5 -Hex(4)HexNAc(5)Sulf(1)@N 1743.564972 1744.5882 H(105)C(64)N(5)O(48)S(1) 1743.564972 H(105)C(64)N(5)O(48)S(1) N-linked glycosylation 1513 0.5 -Hex(7)HexNAc(3)@N 1743.607882 1744.5618 H(109)C(66)N(3)O(50) 1743.607882 H(109)C(66)N(3)O(50) N-linked glycosylation 1514 0.5 -dHex(1)Hex(4)HexNAc(3)NeuAc(1)Sulf(1)@N 1774.559552 1775.5989 H(106)C(65)N(4)O(50)S(1) 1774.559552 H(106)C(65)N(4)O(50)S(1) N-linked glycosylation 1515 0.5 -Hex(5)HexNAc(4)Me(2)Pent(1)@N 1782.655167 1783.6409 H(114)C(69)N(4)O(49) 1782.655167 H(114)C(69)N(4)O(49) N-linked glycosylation 1516 0.5 -Hex(3)HexNAc(6)Sulf(1)@N 1784.591521 1785.6401 H(108)C(66)N(6)O(48)S(1) 1784.591521 H(108)C(66)N(6)O(48)S(1) N-linked glycosylation 1517 0.5 -dHex(1)Hex(6)HexNAc(3)Sulf(1)@N 1807.569782 1808.6256 H(109)C(66)N(3)O(52)S(1) 1807.569782 H(109)C(66)N(3)O(52)S(1) N-linked glycosylation 1518 0.5 -dHex(1)Hex(4)HexNAc(5)@N 1809.666066 1810.6662 H(115)C(70)N(5)O(49) 1809.666066 H(115)C(70)N(5)O(49) N-linked glycosylation 1519 0.5 -dHex(1)Hex(5)HexA(1)HexNAc(3)Sulf(1)@N 1821.549047 1822.6091 H(107)C(66)N(3)O(53)S(1) 1821.549047 H(107)C(66)N(3)O(53)S(1) N-linked glycosylation 1520 0.5 -Hex(7)HexNAc(3)Phos(1)@N 1823.574213 1824.5417 H(110)C(66)N(3)O(53)P(1) 1823.574213 H(110)C(66)N(3)O(53)P(1) N-linked glycosylation 1521 0.5 -Hex(6)HexNAc(4)Me(3)@N 1826.681382 1827.6934 H(118)C(71)N(4)O(50) 1826.681382 H(118)C(71)N(4)O(50) N-linked glycosylation 1522 0.5 -dHex(2)Hex(4)HexNAc(4)Sulf(1)@N 1832.601417 1833.6781 H(112)C(68)N(4)O(51)S(1) 1832.601417 H(112)C(68)N(4)O(51)S(1) N-linked glycosylation 1523 0.5 -Hex(4)HexNAc(3)NeuAc(2)@N 1839.640245 1840.6491 H(113)C(70)N(5)O(51) 1839.640245 H(113)C(70)N(5)O(51) N-linked glycosylation 1524 0.5 -dHex(1)Hex(3)HexNAc(4)Pent(3)@N 1840.660646 1841.6769 H(116)C(71)N(4)O(51) 1840.660646 H(116)C(71)N(4)O(51) N-linked glycosylation 1525 0.5 -dHex(2)Hex(5)HexNAc(3)Pent(1)@N 1843.660312 1844.6776 H(117)C(71)N(3)O(52) 1843.660312 H(117)C(71)N(3)O(52) N-linked glycosylation 1526 0.5 -dHex(1)Hex(5)HexNAc(4)Sulf(1)@N 1848.596331 1849.6775 H(112)C(68)N(4)O(52)S(1) 1848.596331 H(112)C(68)N(4)O(52)S(1) N-linked glycosylation 1527 0.5 -dHex(2)Hex(3)HexNAc(4)Pent(2)@N 1854.676296 1855.7035 H(118)C(72)N(4)O(51) 1854.676296 H(118)C(72)N(4)O(51) N-linked glycosylation 1528 0.5 -dHex(1)Hex(5)HexNAc(3)NeuAc(1)@N 1856.655561 1857.6763 H(116)C(71)N(4)O(52) 1856.655561 H(116)C(71)N(4)O(52) N-linked glycosylation 1529 0.5 -Hex(3)HexNAc(6)Sulf(2)@N 1864.548335 1865.7033 H(108)C(66)N(6)O(51)S(2) 1864.548335 H(108)C(66)N(6)O(51)S(2) N-linked glycosylation 1530 0.5 -Hex(9)HexNAc(2)@N 1864.634157 1865.6504 H(116)C(70)N(2)O(55) 1864.634157 H(116)C(70)N(2)O(55) N-linked glycosylation 1531 0.5 -Hex(4)HexNAc(6)@N 1866.68753 1867.7175 H(118)C(72)N(6)O(50) 1866.68753 H(118)C(72)N(6)O(50) N-linked glycosylation 1532 0.5 -dHex(3)Hex(3)HexNAc(4)Pent(1)@N 1868.691946 1869.7301 H(120)C(73)N(4)O(51) 1868.691946 H(120)C(73)N(4)O(51) N-linked glycosylation 1533 0.5 -dHex(1)Hex(5)HexNAc(3)NeuGc(1)@N 1872.650475 1873.6757 H(116)C(71)N(4)O(53) 1872.650475 H(116)C(71)N(4)O(53) N-linked glycosylation 1534 0.5 -dHex(2)Hex(4)HexNAc(4)Pent(1)@N 1884.686861 1885.7295 H(120)C(73)N(4)O(52) 1884.686861 H(120)C(73)N(4)O(52) N-linked glycosylation 1535 0.5 -dHex(1)Hex(4)HexNAc(5)Sulf(1)@N 1889.62288 1890.7294 H(115)C(70)N(5)O(52)S(1) 1889.62288 H(115)C(70)N(5)O(52)S(1) N-linked glycosylation 1536 0.5 -dHex(1)Hex(7)HexNAc(3)@N 1889.665791 1890.703 H(119)C(72)N(3)O(54) 1889.665791 H(119)C(72)N(3)O(54) N-linked glycosylation 1537 0.5 -dHex(1)Hex(5)HexNAc(4)Pent(1)@N 1900.681776 1901.7289 H(120)C(73)N(4)O(53) 1900.681776 H(120)C(73)N(4)O(53) N-linked glycosylation 1538 0.5 -dHex(1)Hex(5)HexA(1)HexNAc(3)Sulf(2)@N 1901.505861 1902.6723 H(107)C(66)N(3)O(56)S(2) 1901.505861 H(107)C(66)N(3)O(56)S(2) N-linked glycosylation 1539 0.5 -Hex(3)HexNAc(7)@N 1907.714079 1908.7694 H(121)C(74)N(7)O(50) 1907.714079 H(121)C(74)N(7)O(50) N-linked glycosylation 1540 0.5 -dHex(2)Hex(5)HexNAc(4)@N 1914.697426 1915.7555 H(122)C(74)N(4)O(53) 1914.697426 H(122)C(74)N(4)O(53) N-linked glycosylation 1541 0.5 -dHex(2)Hex(4)HexNAc(3)NeuAc(1)Sulf(1)@N 1920.617461 1921.7401 H(116)C(71)N(4)O(54)S(1) 1920.617461 H(116)C(71)N(4)O(54)S(1) N-linked glycosylation 1542 0.5 -dHex(1)Hex(5)HexNAc(4)Sulf(2)@N 1928.553146 1929.7407 H(112)C(68)N(4)O(55)S(2) 1928.553146 H(112)C(68)N(4)O(55)S(2) N-linked glycosylation 1543 0.5 -dHex(1)Hex(5)HexNAc(4)Me(2)Pent(1)@N 1928.713076 1929.7821 H(124)C(75)N(4)O(53) 1928.713076 H(124)C(75)N(4)O(53) N-linked glycosylation 1544 0.5 -Hex(5)HexNAc(4)NeuGc(1)@N 1929.671939 1930.7271 H(119)C(73)N(5)O(54) 1929.671939 H(119)C(73)N(5)O(54) N-linked glycosylation 1545 0.5 -dHex(1)Hex(3)HexNAc(6)Sulf(1)@N 1930.64943 1931.7813 H(118)C(72)N(6)O(52)S(1) 1930.64943 H(118)C(72)N(6)O(52)S(1) N-linked glycosylation 1546 0.5 -dHex(1)Hex(6)HexNAc(4)@N 1930.69234 1931.7549 H(122)C(74)N(4)O(54) 1930.69234 H(122)C(74)N(4)O(54) N-linked glycosylation 1547 0.5 -dHex(1)Hex(5)HexNAc(3)NeuAc(1)Sulf(1)@N 1936.612375 1937.7395 H(116)C(71)N(4)O(55)S(1) 1936.612375 H(116)C(71)N(4)O(55)S(1) N-linked glycosylation 1548 0.5 -Hex(7)HexNAc(4)@N 1946.687255 1947.7543 H(122)C(74)N(4)O(55) 1946.687255 H(122)C(74)N(4)O(55) N-linked glycosylation 1549 0.5 -dHex(1)Hex(5)HexNAc(3)NeuGc(1)Sulf(1)@N 1952.60729 1953.7389 H(116)C(71)N(4)O(56)S(1) 1952.60729 H(116)C(71)N(4)O(56)S(1) N-linked glycosylation 1550 0.5 -Hex(4)HexNAc(5)NeuAc(1)@N 1954.703574 1955.7796 H(122)C(75)N(6)O(53) 1954.703574 H(122)C(75)N(6)O(53) N-linked glycosylation 1551 0.5 -Hex(6)HexNAc(4)Me(3)Pent(1)@N 1958.72364 1959.808 H(126)C(76)N(4)O(54) 1958.72364 H(126)C(76)N(4)O(54) N-linked glycosylation 1552 0.5 -dHex(1)Hex(7)HexNAc(3)Sulf(1)@N 1969.622606 1970.7662 H(119)C(72)N(3)O(57)S(1) 1969.622606 H(119)C(72)N(3)O(57)S(1) N-linked glycosylation 1553 0.5 -dHex(1)Hex(7)HexNAc(3)Phos(1)@N 1969.632122 1970.6829 H(120)C(72)N(3)O(57)P(1) 1969.632122 H(120)C(72)N(3)O(57)P(1) N-linked glycosylation 1554 0.5 -dHex(1)Hex(5)HexNAc(5)@N 1971.718889 1972.8068 H(125)C(76)N(5)O(54) 1971.718889 H(125)C(76)N(5)O(54) N-linked glycosylation 1555 0.5 -dHex(1)Hex(4)HexNAc(4)NeuAc(1)Sulf(1)@N 1977.638925 1978.7915 H(119)C(73)N(5)O(55)S(1) 1977.638925 H(119)C(73)N(5)O(55)S(1) N-linked glycosylation 1556 0.5 -dHex(3)Hex(4)HexNAc(4)Sulf(1)@N 1978.659326 1979.8193 H(122)C(74)N(4)O(55)S(1) 1978.659326 H(122)C(74)N(4)O(55)S(1) N-linked glycosylation 1557 0.5 -Hex(3)HexNAc(7)Sulf(1)@N 1987.670893 1988.8326 H(121)C(74)N(7)O(53)S(1) 1987.670893 H(121)C(74)N(7)O(53)S(1) N-linked glycosylation 1558 0.5 -Hex(6)HexNAc(5)@N 1987.713804 1988.8062 H(125)C(76)N(5)O(55) 1987.713804 H(125)C(76)N(5)O(55) N-linked glycosylation 1559 0.5 -Hex(5)HexNAc(4)NeuAc(1)Sulf(1)@N 1993.633839 1994.7909 H(119)C(73)N(5)O(56)S(1) 1993.633839 H(119)C(73)N(5)O(56)S(1) N-linked glycosylation 1560 0.5 -Hex(3)HexNAc(6)NeuAc(1)@N 1995.730123 1996.8315 H(125)C(77)N(7)O(53) 1995.730123 H(125)C(77)N(7)O(53) N-linked glycosylation 1561 0.5 -dHex(2)Hex(3)HexNAc(6)@N 1996.750524 1997.8593 H(128)C(78)N(6)O(53) 1996.750524 H(128)C(78)N(6)O(53) N-linked glycosylation 1562 0.5 -Hex(1)HexNAc(1)NeuGc(1)@S 672.222527 672.5871 H(40)C(25)N(2)O(19) 672.222527 H(40)C(25)N(2)O(19) O-linked glycosylation 1563 0.5 -Hex(1)HexNAc(1)NeuGc(1)@T 672.222527 672.5871 H(40)C(25)N(2)O(19) 672.222527 H(40)C(25)N(2)O(19) O-linked glycosylation 1563 0.5 -dHex(1)Hex(2)HexNAc(1)@S 673.242928 673.6149 H(43)C(26)N(1)O(19) 673.242928 H(43)C(26)N(1)O(19) O-linked glycosylation 1564 0.5 -dHex(1)Hex(2)HexNAc(1)@T 673.242928 673.6149 H(43)C(26)N(1)O(19) 673.242928 H(43)C(26)N(1)O(19) O-linked glycosylation 1564 0.5 -HexNAc(3)Sulf(1)@T 689.194932 689.6408 H(39)C(24)N(3)O(18)S(1) 689.194932 H(39)C(24)N(3)O(18)S(1) O-linked glycosylation 1565 0.5 -HexNAc(3)Sulf(1)@S 689.194932 689.6408 H(39)C(24)N(3)O(18)S(1) 689.194932 H(39)C(24)N(3)O(18)S(1) O-linked glycosylation 1565 0.5 -Hex(3)HexNAc(1)@T 689.237843 689.6143 H(43)C(26)N(1)O(20) 689.237843 H(43)C(26)N(1)O(20) O-linked glycosylation 1566 0.5 -Hex(3)HexNAc(1)@S 689.237843 689.6143 H(43)C(26)N(1)O(20) 689.237843 H(43)C(26)N(1)O(20) O-linked glycosylation 1566 0.5 -Hex(3)HexNAc(1)@N 689.237843 689.6143 H(43)C(26)N(1)O(20) 689.237843 H(43)C(26)N(1)O(20) N-linked glycosylation 1566 0.5 -Hex(1)HexNAc(1)Kdn(1)Sulf(1)@T 695.157878 695.599 H(37)C(23)N(1)O(21)S(1) 695.157878 H(37)C(23)N(1)O(21)S(1) O-linked glycosylation 1567 0.5 -Hex(1)HexNAc(1)Kdn(1)Sulf(1)@S 695.157878 695.599 H(37)C(23)N(1)O(21)S(1) 695.157878 H(37)C(23)N(1)O(21)S(1) O-linked glycosylation 1567 0.5 -HexNAc(2)NeuAc(1)@S 697.254162 697.6396 H(43)C(27)N(3)O(18) 697.254162 H(43)C(27)N(3)O(18) O-linked glycosylation 1568 0.5 -HexNAc(2)NeuAc(1)@T 697.254162 697.6396 H(43)C(27)N(3)O(18) 697.254162 H(43)C(27)N(3)O(18) O-linked glycosylation 1568 0.5 -HexNAc(1)Kdn(2)@T 703.217108 703.5978 H(41)C(26)N(1)O(21) 703.217108 H(41)C(26)N(1)O(21) O-linked glycosylation 1570 0.5 -HexNAc(1)Kdn(2)@S 703.217108 703.5978 H(41)C(26)N(1)O(21) 703.217108 H(41)C(26)N(1)O(21) O-linked glycosylation 1570 0.5 -Hex(3)HexNAc(1)Me(1)@S 703.253493 703.6409 H(45)C(27)N(1)O(20) 703.253493 H(45)C(27)N(1)O(20) O-linked glycosylation 1571 0.5 -Hex(3)HexNAc(1)Me(1)@T 703.253493 703.6409 H(45)C(27)N(1)O(20) 703.253493 H(45)C(27)N(1)O(20) O-linked glycosylation 1571 0.5 -Hex(2)HexA(1)Pent(1)Sulf(1)@T 712.136808 712.5831 H(36)C(23)O(23)S(1) 712.136808 H(36)C(23)O(23)S(1) O-linked glycosylation 1572 0.5 -Hex(2)HexA(1)Pent(1)Sulf(1)@S 712.136808 712.5831 H(36)C(23)O(23)S(1) 712.136808 H(36)C(23)O(23)S(1) O-linked glycosylation 1572 0.5 -HexNAc(2)NeuGc(1)@S 713.249076 713.639 H(43)C(27)N(3)O(19) 713.249076 H(43)C(27)N(3)O(19) O-linked glycosylation 1573 0.5 -HexNAc(2)NeuGc(1)@T 713.249076 713.639 H(43)C(27)N(3)O(19) 713.249076 H(43)C(27)N(3)O(19) O-linked glycosylation 1573 0.5 -Hex(4)Phos(1)@T 728.177625 728.5423 H(41)C(24)O(23)P(1) 728.177625 H(41)C(24)O(23)P(1) O-linked glycosylation 1575 0.5 -Hex(4)Phos(1)@S 728.177625 728.5423 H(41)C(24)O(23)P(1) 728.177625 H(41)C(24)O(23)P(1) O-linked glycosylation 1575 0.5 -Hex(1)HexNAc(1)NeuAc(1)Sulf(1)@T 736.184427 736.6509 H(40)C(25)N(2)O(21)S(1) 736.184427 H(40)C(25)N(2)O(21)S(1) O-linked glycosylation 1577 0.5 -Hex(1)HexNAc(1)NeuAc(1)Sulf(1)@S 736.184427 736.6509 H(40)C(25)N(2)O(21)S(1) 736.184427 H(40)C(25)N(2)O(21)S(1) O-linked glycosylation 1577 0.5 -Hex(1)HexA(1)HexNAc(2)@S 744.243657 744.6498 H(44)C(28)N(2)O(21) 744.243657 H(44)C(28)N(2)O(21) O-linked glycosylation 1578 0.5 -Hex(1)HexA(1)HexNAc(2)@T 744.243657 744.6498 H(44)C(28)N(2)O(21) 744.243657 H(44)C(28)N(2)O(21) O-linked glycosylation 1578 0.5 -dHex(1)Hex(2)HexNAc(1)Sulf(1)@T 753.199743 753.6781 H(43)C(26)N(1)O(22)S(1) 753.199743 H(43)C(26)N(1)O(22)S(1) O-linked glycosylation 1579 0.5 -dHex(1)Hex(2)HexNAc(1)Sulf(1)@S 753.199743 753.6781 H(43)C(26)N(1)O(22)S(1) 753.199743 H(43)C(26)N(1)O(22)S(1) O-linked glycosylation 1579 0.5 -dHex(1)HexNAc(3)@S 755.296027 755.7188 H(49)C(30)N(3)O(19) 755.296027 H(49)C(30)N(3)O(19) O-linked glycosylation 1580 0.5 -dHex(1)HexNAc(3)@T 755.296027 755.7188 H(49)C(30)N(3)O(19) 755.296027 H(49)C(30)N(3)O(19) O-linked glycosylation 1580 0.5 -dHex(1)Hex(1)HexNAc(1)Kdn(1)@T 761.258973 761.677 H(47)C(29)N(1)O(22) 761.258973 H(47)C(29)N(1)O(22) O-linked glycosylation 1581 0.5 -dHex(1)Hex(1)HexNAc(1)Kdn(1)@S 761.258973 761.677 H(47)C(29)N(1)O(22) 761.258973 H(47)C(29)N(1)O(22) O-linked glycosylation 1581 0.5 -Hex(1)HexNAc(3)@S 771.290941 771.7182 H(49)C(30)N(3)O(20) 771.290941 H(49)C(30)N(3)O(20) O-linked glycosylation 1582 0.5 -Hex(1)HexNAc(3)@T 771.290941 771.7182 H(49)C(30)N(3)O(20) 771.290941 H(49)C(30)N(3)O(20) O-linked glycosylation 1582 0.5 -HexNAc(2)NeuAc(1)Sulf(1)@T 777.210976 777.7028 H(43)C(27)N(3)O(21)S(1) 777.210976 H(43)C(27)N(3)O(21)S(1) O-linked glycosylation 1583 0.5 -HexNAc(2)NeuAc(1)Sulf(1)@S 777.210976 777.7028 H(43)C(27)N(3)O(21)S(1) 777.210976 H(43)C(27)N(3)O(21)S(1) O-linked glycosylation 1583 0.5 -dHex(2)Hex(3)@S 778.274288 778.7042 H(50)C(30)O(23) 778.274288 H(50)C(30)O(23) O-linked glycosylation 1584 0.5 -dHex(2)Hex(3)@T 778.274288 778.7042 H(50)C(30)O(23) 778.274288 H(50)C(30)O(23) O-linked glycosylation 1584 0.5 -Hex(2)HexA(1)HexNAc(1)Sulf(1)@T 783.173922 783.661 H(41)C(26)N(1)O(24)S(1) 783.173922 H(41)C(26)N(1)O(24)S(1) O-linked glycosylation 1585 0.5 -Hex(2)HexA(1)HexNAc(1)Sulf(1)@S 783.173922 783.661 H(41)C(26)N(1)O(24)S(1) 783.173922 H(41)C(26)N(1)O(24)S(1) O-linked glycosylation 1585 0.5 -dHex(2)Hex(2)HexA(1)@S 792.253553 792.6877 H(48)C(30)O(24) 792.253553 H(48)C(30)O(24) O-linked glycosylation 1586 0.5 -dHex(2)Hex(2)HexA(1)@T 792.253553 792.6877 H(48)C(30)O(24) 792.253553 H(48)C(30)O(24) O-linked glycosylation 1586 0.5 -dHex(1)Hex(1)HexNAc(2)Sulf(1)@T 794.226292 794.73 H(46)C(28)N(2)O(22)S(1) 794.226292 H(46)C(28)N(2)O(22)S(1) O-linked glycosylation 1587 0.5 -dHex(1)Hex(1)HexNAc(2)Sulf(1)@S 794.226292 794.73 H(46)C(28)N(2)O(22)S(1) 794.226292 H(46)C(28)N(2)O(22)S(1) O-linked glycosylation 1587 0.5 -dHex(1)Hex(1)HexNAc(1)NeuAc(1)@S 802.285522 802.7289 H(50)C(31)N(2)O(22) 802.285522 H(50)C(31)N(2)O(22) O-linked glycosylation 1588 0.5 -dHex(1)Hex(1)HexNAc(1)NeuAc(1)@T 802.285522 802.7289 H(50)C(31)N(2)O(22) 802.285522 H(50)C(31)N(2)O(22) O-linked glycosylation 1588 0.5 -Hex(2)HexNAc(2)Sulf(1)@T 810.221207 810.7294 H(46)C(28)N(2)O(23)S(1) 810.221207 H(46)C(28)N(2)O(23)S(1) O-linked glycosylation 1589 0.5 -Hex(2)HexNAc(2)Sulf(1)@S 810.221207 810.7294 H(46)C(28)N(2)O(23)S(1) 810.221207 H(46)C(28)N(2)O(23)S(1) O-linked glycosylation 1589 0.5 -Hex(5)@S 810.264117 810.703 H(50)C(30)O(25) 810.264117 H(50)C(30)O(25) O-linked glycosylation 1590 0.5 -Hex(5)@T 810.264117 810.703 H(50)C(30)O(25) 810.264117 H(50)C(30)O(25) O-linked glycosylation 1590 0.5 -HexNAc(4)@S 812.31749 812.7701 H(52)C(32)N(4)O(20) 812.31749 H(52)C(32)N(4)O(20) O-linked glycosylation 1591 0.5 -HexNAc(4)@T 812.31749 812.7701 H(52)C(32)N(4)O(20) 812.31749 H(52)C(32)N(4)O(20) O-linked glycosylation 1591 0.5 -HexNAc(1)NeuGc(2)@S 817.260035 817.7005 H(47)C(30)N(3)O(23) 817.260035 H(47)C(30)N(3)O(23) O-linked glycosylation 1592 0.5 -HexNAc(1)NeuGc(2)@T 817.260035 817.7005 H(47)C(30)N(3)O(23) 817.260035 H(47)C(30)N(3)O(23) O-linked glycosylation 1592 0.5 -dHex(1)Hex(1)HexNAc(1)NeuGc(1)@T 818.280436 818.7283 H(50)C(31)N(2)O(23) 818.280436 H(50)C(31)N(2)O(23) O-linked glycosylation 1593 0.5 -dHex(1)Hex(1)HexNAc(1)NeuGc(1)@S 818.280436 818.7283 H(50)C(31)N(2)O(23) 818.280436 H(50)C(31)N(2)O(23) O-linked glycosylation 1593 0.5 -dHex(2)Hex(2)HexNAc(1)@S 819.300837 819.7561 H(53)C(32)N(1)O(23) 819.300837 H(53)C(32)N(1)O(23) O-linked glycosylation 1594 0.5 -dHex(2)Hex(2)HexNAc(1)@T 819.300837 819.7561 H(53)C(32)N(1)O(23) 819.300837 H(53)C(32)N(1)O(23) O-linked glycosylation 1594 0.5 -Hex(2)HexNAc(1)NeuGc(1)@S 834.275351 834.7277 H(50)C(31)N(2)O(24) 834.275351 H(50)C(31)N(2)O(24) O-linked glycosylation 1595 0.5 -Hex(2)HexNAc(1)NeuGc(1)@T 834.275351 834.7277 H(50)C(31)N(2)O(24) 834.275351 H(50)C(31)N(2)O(24) O-linked glycosylation 1595 0.5 -dHex(1)Hex(3)HexNAc(1)@S 835.295752 835.7555 H(53)C(32)N(1)O(24) 835.295752 H(53)C(32)N(1)O(24) O-linked glycosylation 1596 0.5 -dHex(1)Hex(3)HexNAc(1)@T 835.295752 835.7555 H(53)C(32)N(1)O(24) 835.295752 H(53)C(32)N(1)O(24) O-linked glycosylation 1596 0.5 -dHex(1)Hex(2)HexA(1)HexNAc(1)@S 849.275017 849.739 H(51)C(32)N(1)O(25) 849.275017 H(51)C(32)N(1)O(25) O-linked glycosylation 1597 0.5 -dHex(1)Hex(2)HexA(1)HexNAc(1)@T 849.275017 849.739 H(51)C(32)N(1)O(25) 849.275017 H(51)C(32)N(1)O(25) O-linked glycosylation 1597 0.5 -Hex(1)HexNAc(3)Sulf(1)@T 851.247756 851.7814 H(49)C(30)N(3)O(23)S(1) 851.247756 H(49)C(30)N(3)O(23)S(1) O-linked glycosylation 1598 0.5 -Hex(1)HexNAc(3)Sulf(1)@S 851.247756 851.7814 H(49)C(30)N(3)O(23)S(1) 851.247756 H(49)C(30)N(3)O(23)S(1) O-linked glycosylation 1598 0.5 -Hex(4)HexNAc(1)@T 851.290667 851.7549 H(53)C(32)N(1)O(25) 851.290667 H(53)C(32)N(1)O(25) O-linked glycosylation 1599 0.5 -Hex(4)HexNAc(1)@S 851.290667 851.7549 H(53)C(32)N(1)O(25) 851.290667 H(53)C(32)N(1)O(25) O-linked glycosylation 1599 0.5 -Hex(4)HexNAc(1)@N 851.290667 851.7549 H(53)C(32)N(1)O(25) 851.290667 H(53)C(32)N(1)O(25) N-linked glycosylation 1599 0.5 -Hex(1)HexNAc(2)NeuAc(1)@S 859.306985 859.7802 H(53)C(33)N(3)O(23) 859.306985 H(53)C(33)N(3)O(23) O-linked glycosylation 1600 0.5 -Hex(1)HexNAc(2)NeuAc(1)@T 859.306985 859.7802 H(53)C(33)N(3)O(23) 859.306985 H(53)C(33)N(3)O(23) O-linked glycosylation 1600 0.5 -Hex(1)HexNAc(2)NeuGc(1)@S 875.3019 875.7796 H(53)C(33)N(3)O(24) 875.3019 H(53)C(33)N(3)O(24) O-linked glycosylation 1602 0.5 -Hex(1)HexNAc(2)NeuGc(1)@T 875.3019 875.7796 H(53)C(33)N(3)O(24) 875.3019 H(53)C(33)N(3)O(24) O-linked glycosylation 1602 0.5 -Hex(5)Phos(1)@T 890.230448 890.6829 H(51)C(30)O(28)P(1) 890.230448 H(51)C(30)O(28)P(1) O-linked glycosylation 1604 0.5 -Hex(5)Phos(1)@S 890.230448 890.6829 H(51)C(30)O(28)P(1) 890.230448 H(51)C(30)O(28)P(1) O-linked glycosylation 1604 0.5 -dHex(2)Hex(1)HexNAc(1)Kdn(1)@T 907.316881 907.8182 H(57)C(35)N(1)O(26) 907.316881 H(57)C(35)N(1)O(26) O-linked glycosylation 1606 0.5 -dHex(2)Hex(1)HexNAc(1)Kdn(1)@S 907.316881 907.8182 H(57)C(35)N(1)O(26) 907.316881 H(57)C(35)N(1)O(26) O-linked glycosylation 1606 0.5 -dHex(1)Hex(3)HexNAc(1)Sulf(1)@T 915.252567 915.8187 H(53)C(32)N(1)O(27)S(1) 915.252567 H(53)C(32)N(1)O(27)S(1) O-linked glycosylation 1607 0.5 -dHex(1)Hex(3)HexNAc(1)Sulf(1)@S 915.252567 915.8187 H(53)C(32)N(1)O(27)S(1) 915.252567 H(53)C(32)N(1)O(27)S(1) O-linked glycosylation 1607 0.5 -dHex(1)Hex(1)HexNAc(3)@S 917.34885 917.8594 H(59)C(36)N(3)O(24) 917.34885 H(59)C(36)N(3)O(24) O-linked glycosylation 1608 0.5 -dHex(1)Hex(1)HexNAc(3)@T 917.34885 917.8594 H(59)C(36)N(3)O(24) 917.34885 H(59)C(36)N(3)O(24) O-linked glycosylation 1608 0.5 -dHex(1)Hex(2)HexA(1)HexNAc(1)Sulf(1)@T 929.231831 929.8022 H(51)C(32)N(1)O(28)S(1) 929.231831 H(51)C(32)N(1)O(28)S(1) O-linked glycosylation 1609 0.5 -dHex(1)Hex(2)HexA(1)HexNAc(1)Sulf(1)@S 929.231831 929.8022 H(51)C(32)N(1)O(28)S(1) 929.231831 H(51)C(32)N(1)O(28)S(1) O-linked glycosylation 1609 0.5 -Hex(2)HexNAc(3)@S 933.343765 933.8588 H(59)C(36)N(3)O(25) 933.343765 H(59)C(36)N(3)O(25) O-linked glycosylation 1610 0.5 -Hex(2)HexNAc(3)@N 933.343765 933.8588 H(59)C(36)N(3)O(25) 933.343765 H(59)C(36)N(3)O(25) N-linked glycosylation 1610 0.5 -Hex(2)HexNAc(3)@T 933.343765 933.8588 H(59)C(36)N(3)O(25) 933.343765 H(59)C(36)N(3)O(25) O-linked glycosylation 1610 0.5 -Hex(1)HexNAc(2)NeuAc(1)Sulf(1)@T 939.2638 939.8434 H(53)C(33)N(3)O(26)S(1) 939.2638 H(53)C(33)N(3)O(26)S(1) O-linked glycosylation 1611 0.5 -Hex(1)HexNAc(2)NeuAc(1)Sulf(1)@S 939.2638 939.8434 H(53)C(33)N(3)O(26)S(1) 939.2638 H(53)C(33)N(3)O(26)S(1) O-linked glycosylation 1611 0.5 -dHex(2)Hex(4)@S 940.327112 940.8448 H(60)C(36)O(28) 940.327112 H(60)C(36)O(28) O-linked glycosylation 1612 0.5 -dHex(2)Hex(4)@T 940.327112 940.8448 H(60)C(36)O(28) 940.327112 H(60)C(36)O(28) O-linked glycosylation 1612 0.5 -Hex(1)HexNAc(1)NeuAc(1)Ac(1)@T 698.238177 698.6244 H(42)C(27)N(2)O(19) 698.238177 H(42)C(27)N(2)O(19) O-linked glycosylation 1786 0.5 -Hex(1)HexNAc(1)NeuAc(1)Ac(1)@S 698.238177 698.6244 H(42)C(27)N(2)O(19) 698.238177 H(42)C(27)N(2)O(19) O-linked glycosylation 1786 0.5 -dHex(2)HexNAc(2)Kdn(1)@T 948.34343 948.8701 H(60)C(37)N(2)O(26) 948.34343 H(60)C(37)N(2)O(26) O-linked glycosylation 1614 0.5 -dHex(2)HexNAc(2)Kdn(1)@S 948.34343 948.8701 H(60)C(37)N(2)O(26) 948.34343 H(60)C(37)N(2)O(26) O-linked glycosylation 1614 0.5 -dHex(1)Hex(2)HexNAc(2)Sulf(1)@T 956.279116 956.8706 H(56)C(34)N(2)O(27)S(1) 956.279116 H(56)C(34)N(2)O(27)S(1) O-linked glycosylation 1615 0.5 -dHex(1)Hex(2)HexNAc(2)Sulf(1)@S 956.279116 956.8706 H(56)C(34)N(2)O(27)S(1) 956.279116 H(56)C(34)N(2)O(27)S(1) O-linked glycosylation 1615 0.5 -dHex(1)HexNAc(4)@S 958.375399 958.9113 H(62)C(38)N(4)O(24) 958.375399 H(62)C(38)N(4)O(24) O-linked glycosylation 1616 0.5 -dHex(1)HexNAc(4)@T 958.375399 958.9113 H(62)C(38)N(4)O(24) 958.375399 H(62)C(38)N(4)O(24) O-linked glycosylation 1616 0.5 -Hex(1)HexNAc(1)NeuAc(1)NeuGc(1)@S 963.317944 963.8417 H(57)C(36)N(3)O(27) 963.317944 H(57)C(36)N(3)O(27) O-linked glycosylation 1617 0.5 -Hex(1)HexNAc(1)NeuAc(1)NeuGc(1)@T 963.317944 963.8417 H(57)C(36)N(3)O(27) 963.317944 H(57)C(36)N(3)O(27) O-linked glycosylation 1617 0.5 -dHex(1)Hex(1)HexNAc(2)Kdn(1)@T 964.338345 964.8695 H(60)C(37)N(2)O(27) 964.338345 H(60)C(37)N(2)O(27) O-linked glycosylation 1618 0.5 -dHex(1)Hex(1)HexNAc(2)Kdn(1)@S 964.338345 964.8695 H(60)C(37)N(2)O(27) 964.338345 H(60)C(37)N(2)O(27) O-linked glycosylation 1618 0.5 -Hex(1)HexNAc(1)NeuGc(2)@S 979.312859 979.8411 H(57)C(36)N(3)O(28) 979.312859 H(57)C(36)N(3)O(28) O-linked glycosylation 1619 0.5 -Hex(1)HexNAc(1)NeuGc(2)@T 979.312859 979.8411 H(57)C(36)N(3)O(28) 979.312859 H(57)C(36)N(3)O(28) O-linked glycosylation 1619 0.5 -Hex(1)HexNAc(1)NeuAc(2)Ac(1)@T 989.333594 989.879 H(59)C(38)N(3)O(27) 989.333594 H(59)C(38)N(3)O(27) O-linked glycosylation 1620 0.5 -Hex(1)HexNAc(1)NeuAc(2)Ac(1)@S 989.333594 989.879 H(59)C(38)N(3)O(27) 989.333594 H(59)C(38)N(3)O(27) O-linked glycosylation 1620 0.5 -dHex(2)Hex(2)HexA(1)HexNAc(1)@S 995.332925 995.8802 H(61)C(38)N(1)O(29) 995.332925 H(61)C(38)N(1)O(29) O-linked glycosylation 1621 0.5 -dHex(2)Hex(2)HexA(1)HexNAc(1)@T 995.332925 995.8802 H(61)C(38)N(1)O(29) 995.332925 H(61)C(38)N(1)O(29) O-linked glycosylation 1621 0.5 -dHex(1)Hex(1)HexNAc(3)Sulf(1)@T 997.305665 997.9226 H(59)C(36)N(3)O(27)S(1) 997.305665 H(59)C(36)N(3)O(27)S(1) O-linked glycosylation 1622 0.5 -dHex(1)Hex(1)HexNAc(3)Sulf(1)@S 997.305665 997.9226 H(59)C(36)N(3)O(27)S(1) 997.305665 H(59)C(36)N(3)O(27)S(1) O-linked glycosylation 1622 0.5 -Hex(2)HexA(1)NeuAc(1)Pent(1)Sulf(1)@T 1003.232225 1003.8377 H(53)C(34)N(1)O(31)S(1) 1003.232225 H(53)C(34)N(1)O(31)S(1) O-linked glycosylation 1623 0.5 -Hex(2)HexA(1)NeuAc(1)Pent(1)Sulf(1)@S 1003.232225 1003.8377 H(53)C(34)N(1)O(31)S(1) 1003.232225 H(53)C(34)N(1)O(31)S(1) O-linked glycosylation 1623 0.5 -dHex(1)Hex(1)HexNAc(2)NeuAc(1)@S 1005.364894 1005.9214 H(63)C(39)N(3)O(27) 1005.364894 H(63)C(39)N(3)O(27) O-linked glycosylation 1624 0.5 -dHex(1)Hex(1)HexNAc(2)NeuAc(1)@T 1005.364894 1005.9214 H(63)C(39)N(3)O(27) 1005.364894 H(63)C(39)N(3)O(27) O-linked glycosylation 1624 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(1)@S 1011.32784 1011.8796 H(61)C(38)N(1)O(30) 1011.32784 H(61)C(38)N(1)O(30) O-linked glycosylation 1625 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(1)@T 1011.32784 1011.8796 H(61)C(38)N(1)O(30) 1011.32784 H(61)C(38)N(1)O(30) O-linked glycosylation 1625 0.5 -Hex(2)HexNAc(3)Sulf(1)@T 1013.300579 1013.922 H(59)C(36)N(3)O(28)S(1) 1013.300579 H(59)C(36)N(3)O(28)S(1) O-linked glycosylation 1626 0.5 -Hex(2)HexNAc(3)Sulf(1)@S 1013.300579 1013.922 H(59)C(36)N(3)O(28)S(1) 1013.300579 H(59)C(36)N(3)O(28)S(1) O-linked glycosylation 1626 0.5 -Hex(5)HexNAc(1)@T 1013.34349 1013.8955 H(63)C(38)N(1)O(30) 1013.34349 H(63)C(38)N(1)O(30) O-linked glycosylation 1627 0.5 -Hex(5)HexNAc(1)@S 1013.34349 1013.8955 H(63)C(38)N(1)O(30) 1013.34349 H(63)C(38)N(1)O(30) O-linked glycosylation 1627 0.5 -Hex(5)HexNAc(1)@N 1013.34349 1013.8955 H(63)C(38)N(1)O(30) 1013.34349 H(63)C(38)N(1)O(30) N-linked glycosylation 1627 0.5 -HexNAc(5)@S 1015.396863 1015.9626 H(65)C(40)N(5)O(25) 1015.396863 H(65)C(40)N(5)O(25) O-linked glycosylation 1628 0.5 -HexNAc(5)@T 1015.396863 1015.9626 H(65)C(40)N(5)O(25) 1015.396863 H(65)C(40)N(5)O(25) O-linked glycosylation 1628 0.5 -Hex(1)HexNAc(1)NeuAc(2)Ac(2)@T 1031.344159 1031.9156 H(61)C(40)N(3)O(28) 1031.344159 H(61)C(40)N(3)O(28) O-linked glycosylation 1630 0.5 -Hex(1)HexNAc(1)NeuAc(2)Ac(2)@S 1031.344159 1031.9156 H(61)C(40)N(3)O(28) 1031.344159 H(61)C(40)N(3)O(28) O-linked glycosylation 1630 0.5 -Hex(2)HexNAc(2)NeuGc(1)@S 1037.354723 1037.9202 H(63)C(39)N(3)O(29) 1037.354723 H(63)C(39)N(3)O(29) O-linked glycosylation 1631 0.5 -Hex(2)HexNAc(2)NeuGc(1)@T 1037.354723 1037.9202 H(63)C(39)N(3)O(29) 1037.354723 H(63)C(39)N(3)O(29) O-linked glycosylation 1631 0.5 -Hex(5)Phos(3)@T 1050.16311 1050.6427 H(53)C(30)O(34)P(3) 1050.16311 H(53)C(30)O(34)P(3) O-linked glycosylation 1632 0.5 -Hex(5)Phos(3)@S 1050.16311 1050.6427 H(53)C(30)O(34)P(3) 1050.16311 H(53)C(30)O(34)P(3) O-linked glycosylation 1632 0.5 -Hex(6)Phos(1)@T 1052.283272 1052.8235 H(61)C(36)O(33)P(1) 1052.283272 H(61)C(36)O(33)P(1) O-linked glycosylation 1633 0.5 -Hex(6)Phos(1)@S 1052.283272 1052.8235 H(61)C(36)O(33)P(1) 1052.283272 H(61)C(36)O(33)P(1) O-linked glycosylation 1633 0.5 -dHex(1)Hex(2)HexA(1)HexNAc(2)@S 1052.354389 1052.9316 H(64)C(40)N(2)O(30) 1052.354389 H(64)C(40)N(2)O(30) O-linked glycosylation 1634 0.5 -dHex(1)Hex(2)HexA(1)HexNAc(2)@T 1052.354389 1052.9316 H(64)C(40)N(2)O(30) 1052.354389 H(64)C(40)N(2)O(30) O-linked glycosylation 1634 0.5 -dHex(2)Hex(3)HexNAc(1)Sulf(1)@T 1061.310475 1061.9599 H(63)C(38)N(1)O(31)S(1) 1061.310475 H(63)C(38)N(1)O(31)S(1) O-linked glycosylation 1635 0.5 -dHex(2)Hex(3)HexNAc(1)Sulf(1)@S 1061.310475 1061.9599 H(63)C(38)N(1)O(31)S(1) 1061.310475 H(63)C(38)N(1)O(31)S(1) O-linked glycosylation 1635 0.5 -Hex(1)HexNAc(3)NeuAc(1)@S 1062.386358 1062.9727 H(66)C(41)N(4)O(28) 1062.386358 H(66)C(41)N(4)O(28) O-linked glycosylation 1636 0.5 -Hex(1)HexNAc(3)NeuAc(1)@T 1062.386358 1062.9727 H(66)C(41)N(4)O(28) 1062.386358 H(66)C(41)N(4)O(28) O-linked glycosylation 1636 0.5 -dHex(2)Hex(1)HexNAc(3)@S 1063.406759 1064.0006 H(69)C(42)N(3)O(28) 1063.406759 H(69)C(42)N(3)O(28) O-linked glycosylation 1637 0.5 -dHex(2)Hex(1)HexNAc(3)@T 1063.406759 1064.0006 H(69)C(42)N(3)O(28) 1063.406759 H(69)C(42)N(3)O(28) O-linked glycosylation 1637 0.5 -Hex(1)HexNAc(3)NeuGc(1)@S 1078.381273 1078.9721 H(66)C(41)N(4)O(29) 1078.381273 H(66)C(41)N(4)O(29) O-linked glycosylation 1638 0.5 -Hex(1)HexNAc(3)NeuGc(1)@T 1078.381273 1078.9721 H(66)C(41)N(4)O(29) 1078.381273 H(66)C(41)N(4)O(29) O-linked glycosylation 1638 0.5 -dHex(1)Hex(1)HexNAc(2)NeuAc(1)Sulf(1)@T 1085.321709 1085.9846 H(63)C(39)N(3)O(30)S(1) 1085.321709 H(63)C(39)N(3)O(30)S(1) O-linked glycosylation 1639 0.5 -dHex(1)Hex(1)HexNAc(2)NeuAc(1)Sulf(1)@S 1085.321709 1085.9846 H(63)C(39)N(3)O(30)S(1) 1085.321709 H(63)C(39)N(3)O(30)S(1) O-linked glycosylation 1639 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(1)Sulf(1)@T 1091.284655 1091.9428 H(61)C(38)N(1)O(33)S(1) 1091.284655 H(61)C(38)N(1)O(33)S(1) O-linked glycosylation 1640 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(1)Sulf(1)@S 1091.284655 1091.9428 H(61)C(38)N(1)O(33)S(1) 1091.284655 H(61)C(38)N(1)O(33)S(1) O-linked glycosylation 1640 0.5 -dHex(1)Hex(1)HexA(1)HexNAc(3)@S 1093.380938 1093.9835 H(67)C(42)N(3)O(30) 1093.380938 H(67)C(42)N(3)O(30) O-linked glycosylation 1641 0.5 -dHex(1)Hex(1)HexA(1)HexNAc(3)@T 1093.380938 1093.9835 H(67)C(42)N(3)O(30) 1093.380938 H(67)C(42)N(3)O(30) O-linked glycosylation 1641 0.5 -Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@T 1101.316623 1101.984 H(63)C(39)N(3)O(31)S(1) 1101.316623 H(63)C(39)N(3)O(31)S(1) O-linked glycosylation 1642 0.5 -Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@S 1101.316623 1101.984 H(63)C(39)N(3)O(31)S(1) 1101.316623 H(63)C(39)N(3)O(31)S(1) O-linked glycosylation 1642 0.5 -dHex(2)Hex(2)HexNAc(2)Sulf(1)@T 1102.337025 1103.0118 H(66)C(40)N(2)O(31)S(1) 1102.337025 H(66)C(40)N(2)O(31)S(1) O-linked glycosylation 1643 0.5 -dHex(2)Hex(2)HexNAc(2)Sulf(1)@S 1102.337025 1103.0118 H(66)C(40)N(2)O(31)S(1) 1102.337025 H(66)C(40)N(2)O(31)S(1) O-linked glycosylation 1643 0.5 -dHex(2)Hex(1)HexNAc(2)Kdn(1)@T 1110.396254 1111.0107 H(70)C(43)N(2)O(31) 1110.396254 H(70)C(43)N(2)O(31) O-linked glycosylation 1644 0.5 -dHex(2)Hex(1)HexNAc(2)Kdn(1)@S 1110.396254 1111.0107 H(70)C(43)N(2)O(31) 1110.396254 H(70)C(43)N(2)O(31) O-linked glycosylation 1644 0.5 -dHex(1)Hex(1)HexNAc(4)@S 1120.428223 1121.0519 H(72)C(44)N(4)O(29) 1120.428223 H(72)C(44)N(4)O(29) O-linked glycosylation 1645 0.5 -dHex(1)Hex(1)HexNAc(4)@T 1120.428223 1121.0519 H(72)C(44)N(4)O(29) 1120.428223 H(72)C(44)N(4)O(29) O-linked glycosylation 1645 0.5 -Hex(2)HexNAc(4)@T 1136.423137 1137.0513 H(72)C(44)N(4)O(30) 1136.423137 H(72)C(44)N(4)O(30) O-linked glycosylation 1646 0.5 -Hex(2)HexNAc(4)@S 1136.423137 1137.0513 H(72)C(44)N(4)O(30) 1136.423137 H(72)C(44)N(4)O(30) O-linked glycosylation 1646 0.5 -Hex(2)HexNAc(4)@N 1136.423137 1137.0513 H(72)C(44)N(4)O(30) 1136.423137 H(72)C(44)N(4)O(30) N-linked glycosylation 1646 0.5 -Hex(2)HexNAc(1)NeuGc(2)@S 1141.365682 1141.9817 H(67)C(42)N(3)O(33) 1141.365682 H(67)C(42)N(3)O(33) O-linked glycosylation 1647 0.5 -Hex(2)HexNAc(1)NeuGc(2)@T 1141.365682 1141.9817 H(67)C(42)N(3)O(33) 1141.365682 H(67)C(42)N(3)O(33) O-linked glycosylation 1647 0.5 -dHex(2)Hex(4)HexNAc(1)@S 1143.406484 1144.0373 H(73)C(44)N(1)O(33) 1143.406484 H(73)C(44)N(1)O(33) O-linked glycosylation 1648 0.5 -dHex(2)Hex(4)HexNAc(1)@T 1143.406484 1144.0373 H(73)C(44)N(1)O(33) 1143.406484 H(73)C(44)N(1)O(33) O-linked glycosylation 1648 0.5 -Hex(1)HexNAc(2)NeuAc(2)@S 1150.402402 1151.0348 H(70)C(44)N(4)O(31) 1150.402402 H(70)C(44)N(4)O(31) O-linked glycosylation 1649 0.5 -Hex(1)HexNAc(2)NeuAc(2)@T 1150.402402 1151.0348 H(70)C(44)N(4)O(31) 1150.402402 H(70)C(44)N(4)O(31) O-linked glycosylation 1649 0.5 -dHex(2)Hex(1)HexNAc(2)NeuAc(1)@S 1151.422803 1152.0626 H(73)C(45)N(3)O(31) 1151.422803 H(73)C(45)N(3)O(31) O-linked glycosylation 1650 0.5 -dHex(2)Hex(1)HexNAc(2)NeuAc(1)@T 1151.422803 1152.0626 H(73)C(45)N(3)O(31) 1151.422803 H(73)C(45)N(3)O(31) O-linked glycosylation 1650 0.5 -dHex(1)Hex(2)HexNAc(3)Sulf(1)@T 1159.358488 1160.0632 H(69)C(42)N(3)O(32)S(1) 1159.358488 H(69)C(42)N(3)O(32)S(1) O-linked glycosylation 1651 0.5 -dHex(1)Hex(2)HexNAc(3)Sulf(1)@S 1159.358488 1160.0632 H(69)C(42)N(3)O(32)S(1) 1159.358488 H(69)C(42)N(3)O(32)S(1) O-linked glycosylation 1651 0.5 -dHex(1)HexNAc(5)@S 1161.454772 1162.1038 H(75)C(46)N(5)O(29) 1161.454772 H(75)C(46)N(5)O(29) O-linked glycosylation 1652 0.5 -dHex(1)HexNAc(5)@T 1161.454772 1162.1038 H(75)C(46)N(5)O(29) 1161.454772 H(75)C(46)N(5)O(29) O-linked glycosylation 1652 0.5 -dHex(2)Hex(1)HexNAc(2)NeuGc(1)@T 1167.417718 1168.062 H(73)C(45)N(3)O(32) 1167.417718 H(73)C(45)N(3)O(32) O-linked glycosylation 1653 0.5 -dHex(2)Hex(1)HexNAc(2)NeuGc(1)@S 1167.417718 1168.062 H(73)C(45)N(3)O(32) 1167.417718 H(73)C(45)N(3)O(32) O-linked glycosylation 1653 0.5 -dHex(3)Hex(2)HexNAc(2)@S 1168.438119 1169.0898 H(76)C(46)N(2)O(32) 1168.438119 H(76)C(46)N(2)O(32) O-linked glycosylation 1654 0.5 -dHex(3)Hex(2)HexNAc(2)@T 1168.438119 1169.0898 H(76)C(46)N(2)O(32) 1168.438119 H(76)C(46)N(2)O(32) O-linked glycosylation 1654 0.5 -Hex(3)HexNAc(3)Sulf(1)@T 1175.353403 1176.0626 H(69)C(42)N(3)O(33)S(1) 1175.353403 H(69)C(42)N(3)O(33)S(1) O-linked glycosylation 1655 0.5 -Hex(3)HexNAc(3)Sulf(1)@S 1175.353403 1176.0626 H(69)C(42)N(3)O(33)S(1) 1175.353403 H(69)C(42)N(3)O(33)S(1) O-linked glycosylation 1655 0.5 -Hex(3)HexNAc(3)Sulf(1)@N 1175.353403 1176.0626 H(69)C(42)N(3)O(33)S(1) 1175.353403 H(69)C(42)N(3)O(33)S(1) N-linked glycosylation 1655 0.5 -dHex(2)Hex(2)HexNAc(2)Sulf(2)@T 1182.293839 1183.075 H(66)C(40)N(2)O(34)S(2) 1182.293839 H(66)C(40)N(2)O(34)S(2) O-linked glycosylation 1656 0.5 -dHex(2)Hex(2)HexNAc(2)Sulf(2)@S 1182.293839 1183.075 H(66)C(40)N(2)O(34)S(2) 1182.293839 H(66)C(40)N(2)O(34)S(2) O-linked glycosylation 1656 0.5 -dHex(1)Hex(2)HexNAc(2)NeuGc(1)@N 1183.412632 1184.0614 H(73)C(45)N(3)O(33) 1183.412632 H(73)C(45)N(3)O(33) N-linked glycosylation 1657 0.5 -dHex(1)Hex(2)HexNAc(2)NeuGc(1)@T 1183.412632 1184.0614 H(73)C(45)N(3)O(33) 1183.412632 H(73)C(45)N(3)O(33) O-linked glycosylation 1657 0.5 -dHex(1)Hex(2)HexNAc(2)NeuGc(1)@S 1183.412632 1184.0614 H(73)C(45)N(3)O(33) 1183.412632 H(73)C(45)N(3)O(33) O-linked glycosylation 1657 0.5 -dHex(1)Hex(1)HexNAc(3)NeuAc(1)@T 1208.444267 1209.1139 H(76)C(47)N(4)O(32) 1208.444267 H(76)C(47)N(4)O(32) O-linked glycosylation 1658 0.5 -dHex(1)Hex(1)HexNAc(3)NeuAc(1)@S 1208.444267 1209.1139 H(76)C(47)N(4)O(32) 1208.444267 H(76)C(47)N(4)O(32) O-linked glycosylation 1658 0.5 -Hex(6)Phos(3)@T 1212.215934 1212.7833 H(63)C(36)O(39)P(3) 1212.215934 H(63)C(36)O(39)P(3) O-linked glycosylation 1659 0.5 -Hex(6)Phos(3)@S 1212.215934 1212.7833 H(63)C(36)O(39)P(3) 1212.215934 H(63)C(36)O(39)P(3) O-linked glycosylation 1659 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(2)@S 1214.407213 1215.0722 H(74)C(46)N(2)O(35) 1214.407213 H(74)C(46)N(2)O(35) O-linked glycosylation 1660 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(2)@T 1214.407213 1215.0722 H(74)C(46)N(2)O(35) 1214.407213 H(74)C(46)N(2)O(35) O-linked glycosylation 1660 0.5 -dHex(1)Hex(1)HexNAc(3)NeuGc(1)@T 1224.439181 1225.1133 H(76)C(47)N(4)O(33) 1224.439181 H(76)C(47)N(4)O(33) O-linked glycosylation 1661 0.5 -dHex(1)Hex(1)HexNAc(3)NeuGc(1)@S 1224.439181 1225.1133 H(76)C(47)N(4)O(33) 1224.439181 H(76)C(47)N(4)O(33) O-linked glycosylation 1661 0.5 -Hex(1)HexNAc(2)NeuAc(2)Sulf(1)@T 1230.359217 1231.098 H(70)C(44)N(4)O(34)S(1) 1230.359217 H(70)C(44)N(4)O(34)S(1) O-linked glycosylation 1662 0.5 -Hex(1)HexNAc(2)NeuAc(2)Sulf(1)@S 1230.359217 1231.098 H(70)C(44)N(4)O(34)S(1) 1230.359217 H(70)C(44)N(4)O(34)S(1) O-linked glycosylation 1662 0.5 -dHex(2)Hex(3)HexA(1)HexNAc(1)Sulf(1)@T 1237.342563 1238.084 H(71)C(44)N(1)O(37)S(1) 1237.342563 H(71)C(44)N(1)O(37)S(1) O-linked glycosylation 1663 0.5 -dHex(2)Hex(3)HexA(1)HexNAc(1)Sulf(1)@S 1237.342563 1238.084 H(71)C(44)N(1)O(37)S(1) 1237.342563 H(71)C(44)N(1)O(37)S(1) O-linked glycosylation 1663 0.5 -Hex(1)HexNAc(1)NeuAc(3)@S 1238.418446 1239.0969 H(74)C(47)N(4)O(34) 1238.418446 H(74)C(47)N(4)O(34) O-linked glycosylation 1664 0.5 -Hex(1)HexNAc(1)NeuAc(3)@T 1238.418446 1239.0969 H(74)C(47)N(4)O(34) 1238.418446 H(74)C(47)N(4)O(34) O-linked glycosylation 1664 0.5 -Hex(2)HexNAc(3)NeuGc(1)@S 1240.434096 1241.1127 H(76)C(47)N(4)O(34) 1240.434096 H(76)C(47)N(4)O(34) O-linked glycosylation 1665 0.5 -Hex(2)HexNAc(3)NeuGc(1)@T 1240.434096 1241.1127 H(76)C(47)N(4)O(34) 1240.434096 H(76)C(47)N(4)O(34) O-linked glycosylation 1665 0.5 -dHex(1)Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@T 1247.374532 1248.1252 H(73)C(45)N(3)O(35)S(1) 1247.374532 H(73)C(45)N(3)O(35)S(1) O-linked glycosylation 1666 0.5 -dHex(1)Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@S 1247.374532 1248.1252 H(73)C(45)N(3)O(35)S(1) 1247.374532 H(73)C(45)N(3)O(35)S(1) O-linked glycosylation 1666 0.5 -dHex(3)Hex(1)HexNAc(2)Kdn(1)@T 1256.454163 1257.1519 H(80)C(49)N(2)O(35) 1256.454163 H(80)C(49)N(2)O(35) O-linked glycosylation 1667 0.5 -dHex(3)Hex(1)HexNAc(2)Kdn(1)@S 1256.454163 1257.1519 H(80)C(49)N(2)O(35) 1256.454163 H(80)C(49)N(2)O(35) O-linked glycosylation 1667 0.5 -dHex(2)Hex(3)HexNAc(2)Sulf(1)@T 1264.389848 1265.1524 H(76)C(46)N(2)O(36)S(1) 1264.389848 H(76)C(46)N(2)O(36)S(1) O-linked glycosylation 1668 0.5 -dHex(2)Hex(3)HexNAc(2)Sulf(1)@S 1264.389848 1265.1524 H(76)C(46)N(2)O(36)S(1) 1264.389848 H(76)C(46)N(2)O(36)S(1) O-linked glycosylation 1668 0.5 -dHex(2)Hex(2)HexNAc(2)Kdn(1)@T 1272.449077 1273.1513 H(80)C(49)N(2)O(36) 1272.449077 H(80)C(49)N(2)O(36) O-linked glycosylation 1669 0.5 -dHex(2)Hex(2)HexNAc(2)Kdn(1)@S 1272.449077 1273.1513 H(80)C(49)N(2)O(36) 1272.449077 H(80)C(49)N(2)O(36) O-linked glycosylation 1669 0.5 -dHex(2)Hex(2)HexA(1)HexNAc(2)Sulf(1)@T 1278.369113 1279.136 H(74)C(46)N(2)O(37)S(1) 1278.369113 H(74)C(46)N(2)O(37)S(1) O-linked glycosylation 1670 0.5 -dHex(2)Hex(2)HexA(1)HexNAc(2)Sulf(1)@S 1278.369113 1279.136 H(74)C(46)N(2)O(37)S(1) 1278.369113 H(74)C(46)N(2)O(37)S(1) O-linked glycosylation 1670 0.5 -dHex(1)Hex(2)HexNAc(4)@T 1282.481046 1283.1925 H(82)C(50)N(4)O(34) 1282.481046 H(82)C(50)N(4)O(34) O-linked glycosylation 1671 0.5 -dHex(1)Hex(2)HexNAc(4)@S 1282.481046 1283.1925 H(82)C(50)N(4)O(34) 1282.481046 H(82)C(50)N(4)O(34) O-linked glycosylation 1671 0.5 -dHex(1)Hex(2)HexNAc(4)@N 1282.481046 1283.1925 H(82)C(50)N(4)O(34) 1282.481046 H(82)C(50)N(4)O(34) N-linked glycosylation 1671 0.5 -Hex(1)HexNAc(1)NeuGc(3)@S 1286.40319 1287.0951 H(74)C(47)N(4)O(37) 1286.40319 H(74)C(47)N(4)O(37) O-linked glycosylation 1672 0.5 -Hex(1)HexNAc(1)NeuGc(3)@T 1286.40319 1287.0951 H(74)C(47)N(4)O(37) 1286.40319 H(74)C(47)N(4)O(37) O-linked glycosylation 1672 0.5 -dHex(1)Hex(1)HexNAc(3)NeuAc(1)Sulf(1)@T 1288.401081 1289.1771 H(76)C(47)N(4)O(35)S(1) 1288.401081 H(76)C(47)N(4)O(35)S(1) O-linked glycosylation 1673 0.5 -dHex(1)Hex(1)HexNAc(3)NeuAc(1)Sulf(1)@S 1288.401081 1289.1771 H(76)C(47)N(4)O(35)S(1) 1288.401081 H(76)C(47)N(4)O(35)S(1) O-linked glycosylation 1673 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(2)Sulf(1)@T 1294.364027 1295.1354 H(74)C(46)N(2)O(38)S(1) 1294.364027 H(74)C(46)N(2)O(38)S(1) O-linked glycosylation 1674 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(2)Sulf(1)@S 1294.364027 1295.1354 H(74)C(46)N(2)O(38)S(1) 1294.364027 H(74)C(46)N(2)O(38)S(1) O-linked glycosylation 1674 0.5 -dHex(1)Hex(1)HexNAc(2)NeuAc(2)@S 1296.460311 1297.176 H(80)C(50)N(4)O(35) 1296.460311 H(80)C(50)N(4)O(35) O-linked glycosylation 1675 0.5 -dHex(1)Hex(1)HexNAc(2)NeuAc(2)@T 1296.460311 1297.176 H(80)C(50)N(4)O(35) 1296.460311 H(80)C(50)N(4)O(35) O-linked glycosylation 1675 0.5 -dHex(3)HexNAc(3)Kdn(1)@T 1297.480712 1298.2038 H(83)C(51)N(3)O(35) 1297.480712 H(83)C(51)N(3)O(35) O-linked glycosylation 1676 0.5 -dHex(3)HexNAc(3)Kdn(1)@S 1297.480712 1298.2038 H(83)C(51)N(3)O(35) 1297.480712 H(83)C(51)N(3)O(35) O-linked glycosylation 1676 0.5 -Hex(2)HexNAc(3)NeuAc(1)Sulf(1)@T 1304.395996 1305.1765 H(76)C(47)N(4)O(36)S(1) 1304.395996 H(76)C(47)N(4)O(36)S(1) O-linked glycosylation 1678 0.5 -Hex(2)HexNAc(3)NeuAc(1)Sulf(1)@S 1304.395996 1305.1765 H(76)C(47)N(4)O(36)S(1) 1304.395996 H(76)C(47)N(4)O(36)S(1) O-linked glycosylation 1678 0.5 -dHex(2)Hex(2)HexNAc(3)Sulf(1)@T 1305.416397 1306.2044 H(79)C(48)N(3)O(36)S(1) 1305.416397 H(79)C(48)N(3)O(36)S(1) O-linked glycosylation 1679 0.5 -dHex(2)Hex(2)HexNAc(3)Sulf(1)@S 1305.416397 1306.2044 H(79)C(48)N(3)O(36)S(1) 1305.416397 H(79)C(48)N(3)O(36)S(1) O-linked glycosylation 1679 0.5 -dHex(2)HexNAc(5)@S 1307.512681 1308.245 H(85)C(52)N(5)O(33) 1307.512681 H(85)C(52)N(5)O(33) O-linked glycosylation 1680 0.5 -dHex(2)HexNAc(5)@T 1307.512681 1308.245 H(85)C(52)N(5)O(33) 1307.512681 H(85)C(52)N(5)O(33) O-linked glycosylation 1680 0.5 -Hex(2)HexNAc(2)NeuAc(2)@S 1312.455225 1313.1754 H(80)C(50)N(4)O(36) 1312.455225 H(80)C(50)N(4)O(36) O-linked glycosylation 1681 0.5 -Hex(2)HexNAc(2)NeuAc(2)@T 1312.455225 1313.1754 H(80)C(50)N(4)O(36) 1312.455225 H(80)C(50)N(4)O(36) O-linked glycosylation 1681 0.5 -dHex(2)Hex(2)HexNAc(2)NeuAc(1)@T 1313.475627 1314.2032 H(83)C(51)N(3)O(36) 1313.475627 H(83)C(51)N(3)O(36) O-linked glycosylation 1682 0.5 -dHex(2)Hex(2)HexNAc(2)NeuAc(1)@S 1313.475627 1314.2032 H(83)C(51)N(3)O(36) 1313.475627 H(83)C(51)N(3)O(36) O-linked glycosylation 1682 0.5 -dHex(1)Hex(3)HexNAc(3)Sulf(1)@T 1321.411312 1322.2038 H(79)C(48)N(3)O(37)S(1) 1321.411312 H(79)C(48)N(3)O(37)S(1) O-linked glycosylation 1683 0.5 -dHex(1)Hex(3)HexNAc(3)Sulf(1)@S 1321.411312 1322.2038 H(79)C(48)N(3)O(37)S(1) 1321.411312 H(79)C(48)N(3)O(37)S(1) O-linked glycosylation 1683 0.5 -dHex(2)Hex(2)HexNAc(2)NeuGc(1)@T 1329.470541 1330.2026 H(83)C(51)N(3)O(37) 1329.470541 H(83)C(51)N(3)O(37) O-linked glycosylation 1684 0.5 -dHex(2)Hex(2)HexNAc(2)NeuGc(1)@S 1329.470541 1330.2026 H(83)C(51)N(3)O(37) 1329.470541 H(83)C(51)N(3)O(37) O-linked glycosylation 1684 0.5 -Hex(2)HexNAc(5)@S 1339.50251 1340.2438 H(85)C(52)N(5)O(35) 1339.50251 H(85)C(52)N(5)O(35) O-linked glycosylation 1685 0.5 -Hex(2)HexNAc(5)@T 1339.50251 1340.2438 H(85)C(52)N(5)O(35) 1339.50251 H(85)C(52)N(5)O(35) O-linked glycosylation 1685 0.5 -dHex(1)Hex(3)HexNAc(2)NeuGc(1)@S 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) O-linked glycosylation 1686 0.5 -dHex(1)Hex(3)HexNAc(2)NeuGc(1)@T 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) O-linked glycosylation 1686 0.5 -Hex(1)HexNAc(3)NeuAc(2)@S 1353.481775 1354.2273 H(83)C(52)N(5)O(36) 1353.481775 H(83)C(52)N(5)O(36) O-linked glycosylation 1687 0.5 -Hex(1)HexNAc(3)NeuAc(2)@T 1353.481775 1354.2273 H(83)C(52)N(5)O(36) 1353.481775 H(83)C(52)N(5)O(36) O-linked glycosylation 1687 0.5 -dHex(1)Hex(2)HexNAc(3)NeuAc(1)@S 1370.49709 1371.2545 H(86)C(53)N(4)O(37) 1370.49709 H(86)C(53)N(4)O(37) O-linked glycosylation 1688 0.5 -dHex(1)Hex(2)HexNAc(3)NeuAc(1)@T 1370.49709 1371.2545 H(86)C(53)N(4)O(37) 1370.49709 H(86)C(53)N(4)O(37) O-linked glycosylation 1688 0.5 -dHex(3)Hex(2)HexNAc(3)@S 1371.517491 1372.2824 H(89)C(54)N(3)O(37) 1371.517491 H(89)C(54)N(3)O(37) O-linked glycosylation 1689 0.5 -dHex(3)Hex(2)HexNAc(3)@T 1371.517491 1372.2824 H(89)C(54)N(3)O(37) 1371.517491 H(89)C(54)N(3)O(37) O-linked glycosylation 1689 0.5 -Hex(7)Phos(3)@T 1374.268757 1374.9239 H(73)C(42)O(44)P(3) 1374.268757 H(73)C(42)O(44)P(3) O-linked glycosylation 1690 0.5 -Hex(7)Phos(3)@S 1374.268757 1374.9239 H(73)C(42)O(44)P(3) 1374.268757 H(73)C(42)O(44)P(3) O-linked glycosylation 1690 0.5 -dHex(1)Hex(4)HexA(1)HexNAc(2)@S 1376.460036 1377.2128 H(84)C(52)N(2)O(40) 1376.460036 H(84)C(52)N(2)O(40) O-linked glycosylation 1691 0.5 -dHex(1)Hex(4)HexA(1)HexNAc(2)@T 1376.460036 1377.2128 H(84)C(52)N(2)O(40) 1376.460036 H(84)C(52)N(2)O(40) O-linked glycosylation 1691 0.5 -Hex(3)HexNAc(3)NeuAc(1)@T 1386.492005 1387.2539 H(86)C(53)N(4)O(38) 1386.492005 H(86)C(53)N(4)O(38) O-linked glycosylation 1692 0.5 -Hex(3)HexNAc(3)NeuAc(1)@S 1386.492005 1387.2539 H(86)C(53)N(4)O(38) 1386.492005 H(86)C(53)N(4)O(38) O-linked glycosylation 1692 0.5 -dHex(1)Hex(3)HexA(2)HexNAc(2)@S 1390.439301 1391.1963 H(82)C(52)N(2)O(41) 1390.439301 H(82)C(52)N(2)O(41) O-linked glycosylation 1693 0.5 -dHex(1)Hex(3)HexA(2)HexNAc(2)@T 1390.439301 1391.1963 H(82)C(52)N(2)O(41) 1390.439301 H(82)C(52)N(2)O(41) O-linked glycosylation 1693 0.5 -Hex(2)HexNAc(2)NeuAc(2)Sulf(1)@T 1392.41204 1393.2386 H(80)C(50)N(4)O(39)S(1) 1392.41204 H(80)C(50)N(4)O(39)S(1) O-linked glycosylation 1694 0.5 -Hex(2)HexNAc(2)NeuAc(2)Sulf(1)@S 1392.41204 1393.2386 H(80)C(50)N(4)O(39)S(1) 1392.41204 H(80)C(50)N(4)O(39)S(1) O-linked glycosylation 1694 0.5 -dHex(2)Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@T 1393.432441 1394.2664 H(83)C(51)N(3)O(39)S(1) 1393.432441 H(83)C(51)N(3)O(39)S(1) O-linked glycosylation 1695 0.5 -dHex(2)Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@S 1393.432441 1394.2664 H(83)C(51)N(3)O(39)S(1) 1393.432441 H(83)C(51)N(3)O(39)S(1) O-linked glycosylation 1695 0.5 -Hex(3)HexNAc(3)NeuGc(1)@S 1402.48692 1403.2533 H(86)C(53)N(4)O(39) 1402.48692 H(86)C(53)N(4)O(39) O-linked glycosylation 1696 0.5 -Hex(3)HexNAc(3)NeuGc(1)@T 1402.48692 1403.2533 H(86)C(53)N(4)O(39) 1402.48692 H(86)C(53)N(4)O(39) O-linked glycosylation 1696 0.5 -dHex(4)Hex(1)HexNAc(2)Kdn(1)@T 1402.512072 1403.2931 H(90)C(55)N(2)O(39) 1402.512072 H(90)C(55)N(2)O(39) O-linked glycosylation 1697 0.5 -dHex(4)Hex(1)HexNAc(2)Kdn(1)@S 1402.512072 1403.2931 H(90)C(55)N(2)O(39) 1402.512072 H(90)C(55)N(2)O(39) O-linked glycosylation 1697 0.5 -dHex(3)Hex(2)HexNAc(2)Kdn(1)@T 1418.506986 1419.2925 H(90)C(55)N(2)O(40) 1418.506986 H(90)C(55)N(2)O(40) O-linked glycosylation 1698 0.5 -dHex(3)Hex(2)HexNAc(2)Kdn(1)@S 1418.506986 1419.2925 H(90)C(55)N(2)O(40) 1418.506986 H(90)C(55)N(2)O(40) O-linked glycosylation 1698 0.5 -dHex(3)Hex(2)HexA(1)HexNAc(2)Sulf(1)@T 1424.427021 1425.2772 H(84)C(52)N(2)O(41)S(1) 1424.427021 H(84)C(52)N(2)O(41)S(1) O-linked glycosylation 1699 0.5 -dHex(3)Hex(2)HexA(1)HexNAc(2)Sulf(1)@S 1424.427021 1425.2772 H(84)C(52)N(2)O(41)S(1) 1424.427021 H(84)C(52)N(2)O(41)S(1) O-linked glycosylation 1699 0.5 -Hex(2)HexNAc(4)NeuAc(1)@S 1427.518554 1428.3059 H(89)C(55)N(5)O(38) 1427.518554 H(89)C(55)N(5)O(38) O-linked glycosylation 1700 0.5 -Hex(2)HexNAc(4)NeuAc(1)@T 1427.518554 1428.3059 H(89)C(55)N(5)O(38) 1427.518554 H(89)C(55)N(5)O(38) O-linked glycosylation 1700 0.5 -dHex(2)Hex(2)HexNAc(4)@S 1428.538955 1429.3337 H(92)C(56)N(4)O(38) 1428.538955 H(92)C(56)N(4)O(38) O-linked glycosylation 1701 0.5 -dHex(2)Hex(2)HexNAc(4)@T 1428.538955 1429.3337 H(92)C(56)N(4)O(38) 1428.538955 H(92)C(56)N(4)O(38) O-linked glycosylation 1701 0.5 -dHex(2)Hex(3)HexA(1)HexNAc(2)Sulf(1)@T 1440.421936 1441.2766 H(84)C(52)N(2)O(42)S(1) 1440.421936 H(84)C(52)N(2)O(42)S(1) O-linked glycosylation 1702 0.5 -dHex(2)Hex(3)HexA(1)HexNAc(2)Sulf(1)@S 1440.421936 1441.2766 H(84)C(52)N(2)O(42)S(1) 1440.421936 H(84)C(52)N(2)O(42)S(1) O-linked glycosylation 1702 0.5 -dHex(4)HexNAc(3)Kdn(1)@T 1443.538621 1444.345 H(93)C(57)N(3)O(39) 1443.538621 H(93)C(57)N(3)O(39) O-linked glycosylation 1703 0.5 -dHex(4)HexNAc(3)Kdn(1)@S 1443.538621 1444.345 H(93)C(57)N(3)O(39) 1443.538621 H(93)C(57)N(3)O(39) O-linked glycosylation 1703 0.5 -Hex(2)HexNAc(1)NeuGc(3)@S 1448.456013 1449.2357 H(84)C(53)N(4)O(42) 1448.456013 H(84)C(53)N(4)O(42) O-linked glycosylation 1705 0.5 -Hex(2)HexNAc(1)NeuGc(3)@T 1448.456013 1449.2357 H(84)C(53)N(4)O(42) 1448.456013 H(84)C(53)N(4)O(42) O-linked glycosylation 1705 0.5 -dHex(4)Hex(1)HexNAc(1)Kdn(2)@T 1449.501567 1450.3032 H(91)C(56)N(1)O(42) 1449.501567 H(91)C(56)N(1)O(42) O-linked glycosylation 1706 0.5 -dHex(4)Hex(1)HexNAc(1)Kdn(2)@S 1449.501567 1450.3032 H(91)C(56)N(1)O(42) 1449.501567 H(91)C(56)N(1)O(42) O-linked glycosylation 1706 0.5 -dHex(1)Hex(2)HexNAc(3)NeuAc(1)Sulf(1)@T 1450.453905 1451.3177 H(86)C(53)N(4)O(40)S(1) 1450.453905 H(86)C(53)N(4)O(40)S(1) O-linked glycosylation 1707 0.5 -dHex(1)Hex(2)HexNAc(3)NeuAc(1)Sulf(1)@S 1450.453905 1451.3177 H(86)C(53)N(4)O(40)S(1) 1450.453905 H(86)C(53)N(4)O(40)S(1) O-linked glycosylation 1707 0.5 -dHex(1)Hex(2)HexNAc(2)NeuAc(2)@S 1458.513134 1459.3166 H(90)C(56)N(4)O(40) 1458.513134 H(90)C(56)N(4)O(40) O-linked glycosylation 1708 0.5 -dHex(1)Hex(2)HexNAc(2)NeuAc(2)@T 1458.513134 1459.3166 H(90)C(56)N(4)O(40) 1458.513134 H(90)C(56)N(4)O(40) O-linked glycosylation 1708 0.5 -dHex(3)Hex(1)HexNAc(3)Kdn(1)@T 1459.533535 1460.3444 H(93)C(57)N(3)O(40) 1459.533535 H(93)C(57)N(3)O(40) O-linked glycosylation 1709 0.5 -dHex(3)Hex(1)HexNAc(3)Kdn(1)@S 1459.533535 1460.3444 H(93)C(57)N(3)O(40) 1459.533535 H(93)C(57)N(3)O(40) O-linked glycosylation 1709 0.5 -Hex(3)HexNAc(3)NeuAc(1)Sulf(1)@T 1466.44882 1467.3171 H(86)C(53)N(4)O(41)S(1) 1466.44882 H(86)C(53)N(4)O(41)S(1) O-linked glycosylation 1711 0.5 -Hex(3)HexNAc(3)NeuAc(1)Sulf(1)@S 1466.44882 1467.3171 H(86)C(53)N(4)O(41)S(1) 1466.44882 H(86)C(53)N(4)O(41)S(1) O-linked glycosylation 1711 0.5 -Hex(3)HexNAc(2)NeuAc(2)@S 1474.508049 1475.316 H(90)C(56)N(4)O(41) 1474.508049 H(90)C(56)N(4)O(41) O-linked glycosylation 1712 0.5 -Hex(3)HexNAc(2)NeuAc(2)@T 1474.508049 1475.316 H(90)C(56)N(4)O(41) 1474.508049 H(90)C(56)N(4)O(41) O-linked glycosylation 1712 0.5 -Hex(3)HexNAc(3)NeuGc(1)Sulf(1)@T 1482.443734 1483.3165 H(86)C(53)N(4)O(42)S(1) 1482.443734 H(86)C(53)N(4)O(42)S(1) O-linked glycosylation 1713 0.5 -Hex(3)HexNAc(3)NeuGc(1)Sulf(1)@S 1482.443734 1483.3165 H(86)C(53)N(4)O(42)S(1) 1482.443734 H(86)C(53)N(4)O(42)S(1) O-linked glycosylation 1713 0.5 -dHex(1)Hex(2)HexNAc(2)NeuGc(2)@S 1490.502964 1491.3154 H(90)C(56)N(4)O(42) 1490.502964 H(90)C(56)N(4)O(42) O-linked glycosylation 1714 0.5 -dHex(1)Hex(2)HexNAc(2)NeuGc(2)@T 1490.502964 1491.3154 H(90)C(56)N(4)O(42) 1490.502964 H(90)C(56)N(4)O(42) O-linked glycosylation 1714 0.5 -dHex(2)Hex(3)HexNAc(2)NeuGc(1)@T 1491.523365 1492.3432 H(93)C(57)N(3)O(42) 1491.523365 H(93)C(57)N(3)O(42) O-linked glycosylation 1715 0.5 -dHex(2)Hex(3)HexNAc(2)NeuGc(1)@S 1491.523365 1492.3432 H(93)C(57)N(3)O(42) 1491.523365 H(93)C(57)N(3)O(42) O-linked glycosylation 1715 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(3)Sulf(1)@T 1497.4434 1498.3279 H(87)C(54)N(3)O(43)S(1) 1497.4434 H(87)C(54)N(3)O(43)S(1) O-linked glycosylation 1716 0.5 -dHex(1)Hex(3)HexA(1)HexNAc(3)Sulf(1)@S 1497.4434 1498.3279 H(87)C(54)N(3)O(43)S(1) 1497.4434 H(87)C(54)N(3)O(43)S(1) O-linked glycosylation 1716 0.5 -Hex(2)HexNAc(3)NeuAc(2)@S 1515.534598 1516.3679 H(93)C(58)N(5)O(41) 1515.534598 H(93)C(58)N(5)O(41) O-linked glycosylation 1717 0.5 -Hex(2)HexNAc(3)NeuAc(2)@T 1515.534598 1516.3679 H(93)C(58)N(5)O(41) 1515.534598 H(93)C(58)N(5)O(41) O-linked glycosylation 1717 0.5 -dHex(2)Hex(2)HexNAc(3)NeuAc(1)@S 1516.554999 1517.3957 H(96)C(59)N(4)O(41) 1516.554999 H(96)C(59)N(4)O(41) O-linked glycosylation 1718 0.5 -dHex(2)Hex(2)HexNAc(3)NeuAc(1)@T 1516.554999 1517.3957 H(96)C(59)N(4)O(41) 1516.554999 H(96)C(59)N(4)O(41) O-linked glycosylation 1718 0.5 -dHex(4)Hex(2)HexNAc(3)@S 1517.5754 1518.4236 H(99)C(60)N(3)O(41) 1517.5754 H(99)C(60)N(3)O(41) O-linked glycosylation 1719 0.5 -dHex(4)Hex(2)HexNAc(3)@T 1517.5754 1518.4236 H(99)C(60)N(3)O(41) 1517.5754 H(99)C(60)N(3)O(41) O-linked glycosylation 1719 0.5 -Hex(2)HexNAc(3)NeuAc(1)NeuGc(1)@S 1531.529513 1532.3673 H(93)C(58)N(5)O(42) 1531.529513 H(93)C(58)N(5)O(42) O-linked glycosylation 1720 0.5 -Hex(2)HexNAc(3)NeuAc(1)NeuGc(1)@T 1531.529513 1532.3673 H(93)C(58)N(5)O(42) 1531.529513 H(93)C(58)N(5)O(42) O-linked glycosylation 1720 0.5 -dHex(2)Hex(2)HexNAc(3)NeuGc(1)@T 1532.549914 1533.3951 H(96)C(59)N(4)O(42) 1532.549914 H(96)C(59)N(4)O(42) O-linked glycosylation 1721 0.5 -dHex(2)Hex(2)HexNAc(3)NeuGc(1)@S 1532.549914 1533.3951 H(96)C(59)N(4)O(42) 1532.549914 H(96)C(59)N(4)O(42) O-linked glycosylation 1721 0.5 -dHex(3)Hex(3)HexNAc(3)@S 1533.570315 1534.423 H(99)C(60)N(3)O(42) 1533.570315 H(99)C(60)N(3)O(42) O-linked glycosylation 1722 0.5 -dHex(3)Hex(3)HexNAc(3)@T 1533.570315 1534.423 H(99)C(60)N(3)O(42) 1533.570315 H(99)C(60)N(3)O(42) O-linked glycosylation 1722 0.5 -Hex(8)Phos(3)@T 1536.321581 1537.0645 H(83)C(48)O(49)P(3) 1536.321581 H(83)C(48)O(49)P(3) O-linked glycosylation 1723 0.5 -Hex(8)Phos(3)@S 1536.321581 1537.0645 H(83)C(48)O(49)P(3) 1536.321581 H(83)C(48)O(49)P(3) O-linked glycosylation 1723 0.5 -dHex(1)Hex(2)HexNAc(2)NeuAc(2)Sulf(1)@T 1538.469949 1539.3798 H(90)C(56)N(4)O(43)S(1) 1538.469949 H(90)C(56)N(4)O(43)S(1) O-linked glycosylation 1724 0.5 -dHex(1)Hex(2)HexNAc(2)NeuAc(2)Sulf(1)@S 1538.469949 1539.3798 H(90)C(56)N(4)O(43)S(1) 1538.469949 H(90)C(56)N(4)O(43)S(1) O-linked glycosylation 1724 0.5 -Hex(2)HexNAc(3)NeuGc(2)@S 1547.524427 1548.3667 H(93)C(58)N(5)O(43) 1547.524427 H(93)C(58)N(5)O(43) O-linked glycosylation 1725 0.5 -Hex(2)HexNAc(3)NeuGc(2)@T 1547.524427 1548.3667 H(93)C(58)N(5)O(43) 1547.524427 H(93)C(58)N(5)O(43) O-linked glycosylation 1725 0.5 -dHex(4)Hex(2)HexNAc(2)Kdn(1)@T 1564.564895 1565.4337 H(100)C(61)N(2)O(44) 1564.564895 H(100)C(61)N(2)O(44) O-linked glycosylation 1726 0.5 -dHex(4)Hex(2)HexNAc(2)Kdn(1)@S 1564.564895 1565.4337 H(100)C(61)N(2)O(44) 1564.564895 H(100)C(61)N(2)O(44) O-linked glycosylation 1726 0.5 -dHex(1)Hex(2)HexNAc(4)NeuAc(1)@S 1573.576463 1574.4471 H(99)C(61)N(5)O(42) 1573.576463 H(99)C(61)N(5)O(42) O-linked glycosylation 1727 0.5 -dHex(1)Hex(2)HexNAc(4)NeuAc(1)@T 1573.576463 1574.4471 H(99)C(61)N(5)O(42) 1573.576463 H(99)C(61)N(5)O(42) O-linked glycosylation 1727 0.5 -dHex(3)Hex(2)HexNAc(4)@S 1574.596864 1575.4749 H(102)C(62)N(4)O(42) 1574.596864 H(102)C(62)N(4)O(42) O-linked glycosylation 1728 0.5 -dHex(3)Hex(2)HexNAc(4)@T 1574.596864 1575.4749 H(102)C(62)N(4)O(42) 1574.596864 H(102)C(62)N(4)O(42) O-linked glycosylation 1728 0.5 -Hex(1)HexNAc(1)NeuGc(4)@S 1593.493521 1594.349 H(91)C(58)N(5)O(46) 1593.493521 H(91)C(58)N(5)O(46) O-linked glycosylation 1729 0.5 -Hex(1)HexNAc(1)NeuGc(4)@T 1593.493521 1594.349 H(91)C(58)N(5)O(46) 1593.493521 H(91)C(58)N(5)O(46) O-linked glycosylation 1729 0.5 -dHex(4)Hex(1)HexNAc(3)Kdn(1)@T 1605.591444 1606.4856 H(103)C(63)N(3)O(44) 1605.591444 H(103)C(63)N(3)O(44) O-linked glycosylation 1730 0.5 -dHex(4)Hex(1)HexNAc(3)Kdn(1)@S 1605.591444 1606.4856 H(103)C(63)N(3)O(44) 1605.591444 H(103)C(63)N(3)O(44) O-linked glycosylation 1730 0.5 -Hex(4)HexNAc(4)Sulf(2)@T 1620.442414 1621.4589 H(92)C(56)N(4)O(46)S(2) 1620.442414 H(92)C(56)N(4)O(46)S(2) O-linked glycosylation 1732 0.5 -Hex(4)HexNAc(4)Sulf(2)@S 1620.442414 1621.4589 H(92)C(56)N(4)O(46)S(2) 1620.442414 H(92)C(56)N(4)O(46)S(2) O-linked glycosylation 1732 0.5 -dHex(3)Hex(2)HexNAc(3)Kdn(1)@T 1621.586359 1622.485 H(103)C(63)N(3)O(45) 1621.586359 H(103)C(63)N(3)O(45) O-linked glycosylation 1733 0.5 -dHex(3)Hex(2)HexNAc(3)Kdn(1)@S 1621.586359 1622.485 H(103)C(63)N(3)O(45) 1621.586359 H(103)C(63)N(3)O(45) O-linked glycosylation 1733 0.5 -dHex(2)Hex(2)HexNAc(5)@S 1631.618328 1632.5262 H(105)C(64)N(5)O(43) 1631.618328 H(105)C(64)N(5)O(43) O-linked glycosylation 1735 0.5 -dHex(2)Hex(2)HexNAc(5)@T 1631.618328 1632.5262 H(105)C(64)N(5)O(43) 1631.618328 H(105)C(64)N(5)O(43) O-linked glycosylation 1735 0.5 -dHex(2)Hex(3)HexA(1)HexNAc(3)Sulf(1)@T 1643.501309 1644.4691 H(97)C(60)N(3)O(47)S(1) 1643.501309 H(97)C(60)N(3)O(47)S(1) O-linked glycosylation 1736 0.5 -dHex(2)Hex(3)HexA(1)HexNAc(3)Sulf(1)@S 1643.501309 1644.4691 H(97)C(60)N(3)O(47)S(1) 1643.501309 H(97)C(60)N(3)O(47)S(1) O-linked glycosylation 1736 0.5 -dHex(1)Hex(4)HexA(1)HexNAc(3)Sulf(1)@T 1659.496223 1660.4685 H(97)C(60)N(3)O(48)S(1) 1659.496223 H(97)C(60)N(3)O(48)S(1) O-linked glycosylation 1737 0.5 -dHex(1)Hex(4)HexA(1)HexNAc(3)Sulf(1)@S 1659.496223 1660.4685 H(97)C(60)N(3)O(48)S(1) 1659.496223 H(97)C(60)N(3)O(48)S(1) O-linked glycosylation 1737 0.5 -Hex(3)HexNAc(3)NeuAc(2)@S 1677.587422 1678.5085 H(103)C(64)N(5)O(46) 1677.587422 H(103)C(64)N(5)O(46) O-linked glycosylation 1738 0.5 -Hex(3)HexNAc(3)NeuAc(2)@T 1677.587422 1678.5085 H(103)C(64)N(5)O(46) 1677.587422 H(103)C(64)N(5)O(46) O-linked glycosylation 1738 0.5 -dHex(2)Hex(3)HexNAc(3)NeuAc(1)@T 1678.607823 1679.5363 H(106)C(65)N(4)O(46) 1678.607823 H(106)C(65)N(4)O(46) O-linked glycosylation 1739 0.5 -dHex(2)Hex(3)HexNAc(3)NeuAc(1)@S 1678.607823 1679.5363 H(106)C(65)N(4)O(46) 1678.607823 H(106)C(65)N(4)O(46) O-linked glycosylation 1739 0.5 -dHex(4)Hex(3)HexNAc(3)@S 1679.628224 1680.5642 H(109)C(66)N(3)O(46) 1679.628224 H(109)C(66)N(3)O(46) O-linked glycosylation 1740 0.5 -dHex(4)Hex(3)HexNAc(3)@T 1679.628224 1680.5642 H(109)C(66)N(3)O(46) 1679.628224 H(109)C(66)N(3)O(46) O-linked glycosylation 1740 0.5 -Hex(9)Phos(3)@T 1698.374404 1699.2051 H(93)C(54)O(54)P(3) 1698.374404 H(93)C(54)O(54)P(3) O-linked glycosylation 1742 0.5 -Hex(9)Phos(3)@S 1698.374404 1699.2051 H(93)C(54)O(54)P(3) 1698.374404 H(93)C(54)O(54)P(3) O-linked glycosylation 1742 0.5 -dHex(2)HexNAc(7)@S 1713.671426 1714.63 H(111)C(68)N(7)O(43) 1713.671426 H(111)C(68)N(7)O(43) O-linked glycosylation 1743 0.5 -dHex(2)HexNAc(7)@T 1713.671426 1714.63 H(111)C(68)N(7)O(43) 1713.671426 H(111)C(68)N(7)O(43) O-linked glycosylation 1743 0.5 -Hex(2)HexNAc(1)NeuGc(4)@S 1755.546345 1756.4896 H(101)C(64)N(5)O(51) 1755.546345 H(101)C(64)N(5)O(51) O-linked glycosylation 1744 0.5 -Hex(2)HexNAc(1)NeuGc(4)@T 1755.546345 1756.4896 H(101)C(64)N(5)O(51) 1755.546345 H(101)C(64)N(5)O(51) O-linked glycosylation 1744 0.5 -Hex(3)HexNAc(3)NeuAc(2)Sulf(1)@T 1757.544236 1758.5717 H(103)C(64)N(5)O(49)S(1) 1757.544236 H(103)C(64)N(5)O(49)S(1) O-linked glycosylation 1745 0.5 -Hex(3)HexNAc(3)NeuAc(2)Sulf(1)@S 1757.544236 1758.5717 H(103)C(64)N(5)O(49)S(1) 1757.544236 H(103)C(64)N(5)O(49)S(1) O-linked glycosylation 1745 0.5 -dHex(2)Hex(3)HexNAc(5)@T 1793.671151 1794.6668 H(115)C(70)N(5)O(48) 1793.671151 H(115)C(70)N(5)O(48) O-linked glycosylation 1746 0.5 -dHex(2)Hex(3)HexNAc(5)@S 1793.671151 1794.6668 H(115)C(70)N(5)O(48) 1793.671151 H(115)C(70)N(5)O(48) O-linked glycosylation 1746 0.5 -dHex(2)Hex(3)HexNAc(5)@N 1793.671151 1794.6668 H(115)C(70)N(5)O(48) 1793.671151 H(115)C(70)N(5)O(48) N-linked glycosylation 1746 0.5 -dHex(1)Hex(2)HexNAc(2)NeuGc(3)@S 1797.593295 1798.5694 H(107)C(67)N(5)O(51) 1797.593295 H(107)C(67)N(5)O(51) O-linked glycosylation 1747 0.5 -dHex(1)Hex(2)HexNAc(2)NeuGc(3)@T 1797.593295 1798.5694 H(107)C(67)N(5)O(51) 1797.593295 H(107)C(67)N(5)O(51) O-linked glycosylation 1747 0.5 -dHex(2)Hex(4)HexA(1)HexNAc(3)Sulf(1)@T 1805.554132 1806.6097 H(107)C(66)N(3)O(52)S(1) 1805.554132 H(107)C(66)N(3)O(52)S(1) O-linked glycosylation 1748 0.5 -dHex(2)Hex(4)HexA(1)HexNAc(3)Sulf(1)@S 1805.554132 1806.6097 H(107)C(66)N(3)O(52)S(1) 1805.554132 H(107)C(66)N(3)O(52)S(1) O-linked glycosylation 1748 0.5 -Hex(2)HexNAc(3)NeuAc(3)@S 1806.630015 1807.6225 H(110)C(69)N(6)O(49) 1806.630015 H(110)C(69)N(6)O(49) O-linked glycosylation 1749 0.5 -Hex(2)HexNAc(3)NeuAc(3)@T 1806.630015 1807.6225 H(110)C(69)N(6)O(49) 1806.630015 H(110)C(69)N(6)O(49) O-linked glycosylation 1749 0.5 -dHex(1)Hex(3)HexNAc(3)NeuAc(2)@S 1823.64533 1824.6497 H(113)C(70)N(5)O(50) 1823.64533 H(113)C(70)N(5)O(50) O-linked glycosylation 1750 0.5 -dHex(1)Hex(3)HexNAc(3)NeuAc(2)@T 1823.64533 1824.6497 H(113)C(70)N(5)O(50) 1823.64533 H(113)C(70)N(5)O(50) O-linked glycosylation 1750 0.5 -dHex(3)Hex(3)HexNAc(3)NeuAc(1)@S 1824.665732 1825.6775 H(116)C(71)N(4)O(50) 1824.665732 H(116)C(71)N(4)O(50) O-linked glycosylation 1751 0.5 -dHex(3)Hex(3)HexNAc(3)NeuAc(1)@T 1824.665732 1825.6775 H(116)C(71)N(4)O(50) 1824.665732 H(116)C(71)N(4)O(50) O-linked glycosylation 1751 0.5 -Hex(2)HexNAc(3)NeuGc(3)@S 1854.614759 1855.6207 H(110)C(69)N(6)O(52) 1854.614759 H(110)C(69)N(6)O(52) O-linked glycosylation 1752 0.5 -Hex(2)HexNAc(3)NeuGc(3)@T 1854.614759 1855.6207 H(110)C(69)N(6)O(52) 1854.614759 H(110)C(69)N(6)O(52) O-linked glycosylation 1752 0.5 -Hex(10)Phos(3)@T 1860.427228 1861.3457 H(103)C(60)O(59)P(3) 1860.427228 H(103)C(60)O(59)P(3) O-linked glycosylation 1753 0.5 -Hex(10)Phos(3)@S 1860.427228 1861.3457 H(103)C(60)O(59)P(3) 1860.427228 H(103)C(60)O(59)P(3) O-linked glycosylation 1753 0.5 -dHex(1)Hex(2)HexNAc(4)NeuAc(2)@S 1864.67188 1865.7016 H(116)C(72)N(6)O(50) 1864.67188 H(116)C(72)N(6)O(50) O-linked glycosylation 1754 0.5 -dHex(1)Hex(2)HexNAc(4)NeuAc(2)@T 1864.67188 1865.7016 H(116)C(72)N(6)O(50) 1864.67188 H(116)C(72)N(6)O(50) O-linked glycosylation 1754 0.5 -Hex(1)HexNAc(1)NeuGc(5)@S 1900.583852 1901.603 H(108)C(69)N(6)O(55) 1900.583852 H(108)C(69)N(6)O(55) O-linked glycosylation 1755 0.5 -Hex(1)HexNAc(1)NeuGc(5)@T 1900.583852 1901.603 H(108)C(69)N(6)O(55) 1900.583852 H(108)C(69)N(6)O(55) O-linked glycosylation 1755 0.5 -Hex(4)HexNAc(4)NeuAc(1)Sulf(2)@T 1911.53783 1912.7135 H(109)C(67)N(5)O(54)S(2) 1911.53783 H(109)C(67)N(5)O(54)S(2) O-linked glycosylation 1756 0.5 -Hex(4)HexNAc(4)NeuAc(1)Sulf(2)@S 1911.53783 1912.7135 H(109)C(67)N(5)O(54)S(2) 1911.53783 H(109)C(67)N(5)O(54)S(2) O-linked glycosylation 1756 0.5 -Hex(4)HexNAc(4)NeuGc(1)Sulf(2)@T 1927.532745 1928.7129 H(109)C(67)N(5)O(55)S(2) 1927.532745 H(109)C(67)N(5)O(55)S(2) O-linked glycosylation 1757 0.5 -Hex(4)HexNAc(4)NeuGc(1)Sulf(2)@S 1927.532745 1928.7129 H(109)C(67)N(5)O(55)S(2) 1927.532745 H(109)C(67)N(5)O(55)S(2) O-linked glycosylation 1757 0.5 -dHex(2)Hex(3)HexNAc(3)NeuAc(2)@S 1969.703239 1970.7909 H(123)C(76)N(5)O(54) 1969.703239 H(123)C(76)N(5)O(54) O-linked glycosylation 1758 0.5 -dHex(2)Hex(3)HexNAc(3)NeuAc(2)@T 1969.703239 1970.7909 H(123)C(76)N(5)O(54) 1969.703239 H(123)C(76)N(5)O(54) O-linked glycosylation 1758 0.5 -Hex(4)HexNAc(4)NeuAc(1)Sulf(3)@T 1991.494645 1992.7767 H(109)C(67)N(5)O(57)S(3) 1991.494645 H(109)C(67)N(5)O(57)S(3) O-linked glycosylation 1759 0.5 -Hex(4)HexNAc(4)NeuAc(1)Sulf(3)@S 1991.494645 1992.7767 H(109)C(67)N(5)O(57)S(3) 1991.494645 H(109)C(67)N(5)O(57)S(3) O-linked glycosylation 1759 0.5 -dHex(2)Hex(2)HexNAc(2)@S 1022.38021 1022.9486 H(66)C(40)N(2)O(28) 1022.38021 H(66)C(40)N(2)O(28) O-linked glycosylation 1760 0.5 -dHex(2)Hex(2)HexNAc(2)@T 1022.38021 1022.9486 H(66)C(40)N(2)O(28) 1022.38021 H(66)C(40)N(2)O(28) O-linked glycosylation 1760 0.5 -dHex(2)Hex(2)HexNAc(2)@N 1022.38021 1022.9486 H(66)C(40)N(2)O(28) 1022.38021 H(66)C(40)N(2)O(28) N-linked glycosylation 1760 0.5 -dHex(1)Hex(3)HexNAc(2)@S 1038.375125 1038.948 H(66)C(40)N(2)O(29) 1038.375125 H(66)C(40)N(2)O(29) O-linked glycosylation 1761 0.5 -dHex(1)Hex(3)HexNAc(2)@T 1038.375125 1038.948 H(66)C(40)N(2)O(29) 1038.375125 H(66)C(40)N(2)O(29) O-linked glycosylation 1761 0.5 -dHex(1)Hex(3)HexNAc(2)@N 1038.375125 1038.948 H(66)C(40)N(2)O(29) 1038.375125 H(66)C(40)N(2)O(29) N-linked glycosylation 1761 0.5 -dHex(1)Hex(2)HexNAc(3)@S 1079.401674 1080.0 H(69)C(42)N(3)O(29) 1079.401674 H(69)C(42)N(3)O(29) O-linked glycosylation 1762 0.5 -dHex(1)Hex(2)HexNAc(3)@T 1079.401674 1080.0 H(69)C(42)N(3)O(29) 1079.401674 H(69)C(42)N(3)O(29) O-linked glycosylation 1762 0.5 -dHex(1)Hex(2)HexNAc(3)@N 1079.401674 1080.0 H(69)C(42)N(3)O(29) 1079.401674 H(69)C(42)N(3)O(29) N-linked glycosylation 1762 0.5 -Hex(3)HexNAc(3)@S 1095.396588 1095.9994 H(69)C(42)N(3)O(30) 1095.396588 H(69)C(42)N(3)O(30) O-linked glycosylation 1763 0.5 -Hex(3)HexNAc(3)@T 1095.396588 1095.9994 H(69)C(42)N(3)O(30) 1095.396588 H(69)C(42)N(3)O(30) O-linked glycosylation 1763 0.5 -Hex(3)HexNAc(3)@N 1095.396588 1095.9994 H(69)C(42)N(3)O(30) 1095.396588 H(69)C(42)N(3)O(30) N-linked glycosylation 1763 0.5 -dHex(1)Hex(3)HexNAc(2)Sulf(1)@N 1118.331939 1119.0112 H(66)C(40)N(2)O(32)S(1) 1118.331939 H(66)C(40)N(2)O(32)S(1) N-linked glycosylation 1764 0.5 -dHex(1)Hex(3)HexNAc(2)Sulf(1)@T 1118.331939 1119.0112 H(66)C(40)N(2)O(32)S(1) 1118.331939 H(66)C(40)N(2)O(32)S(1) O-linked glycosylation 1764 0.5 -dHex(1)Hex(3)HexNAc(2)Sulf(1)@S 1118.331939 1119.0112 H(66)C(40)N(2)O(32)S(1) 1118.331939 H(66)C(40)N(2)O(32)S(1) O-linked glycosylation 1764 0.5 -dHex(2)Hex(3)HexNAc(2)@S 1184.433033 1185.0892 H(76)C(46)N(2)O(33) 1184.433033 H(76)C(46)N(2)O(33) O-linked glycosylation 1765 0.5 -dHex(2)Hex(3)HexNAc(2)@T 1184.433033 1185.0892 H(76)C(46)N(2)O(33) 1184.433033 H(76)C(46)N(2)O(33) O-linked glycosylation 1765 0.5 -dHex(2)Hex(3)HexNAc(2)@N 1184.433033 1185.0892 H(76)C(46)N(2)O(33) 1184.433033 H(76)C(46)N(2)O(33) N-linked glycosylation 1765 0.5 -dHex(1)Hex(4)HexNAc(2)@S 1200.427948 1201.0886 H(76)C(46)N(2)O(34) 1200.427948 H(76)C(46)N(2)O(34) O-linked glycosylation 1766 0.5 -dHex(1)Hex(4)HexNAc(2)@T 1200.427948 1201.0886 H(76)C(46)N(2)O(34) 1200.427948 H(76)C(46)N(2)O(34) O-linked glycosylation 1766 0.5 -dHex(1)Hex(4)HexNAc(2)@N 1200.427948 1201.0886 H(76)C(46)N(2)O(34) 1200.427948 H(76)C(46)N(2)O(34) N-linked glycosylation 1766 0.5 -dHex(2)Hex(2)HexNAc(3)@S 1225.459583 1226.1412 H(79)C(48)N(3)O(33) 1225.459583 H(79)C(48)N(3)O(33) O-linked glycosylation 1767 0.5 -dHex(2)Hex(2)HexNAc(3)@T 1225.459583 1226.1412 H(79)C(48)N(3)O(33) 1225.459583 H(79)C(48)N(3)O(33) O-linked glycosylation 1767 0.5 -dHex(2)Hex(2)HexNAc(3)@N 1225.459583 1226.1412 H(79)C(48)N(3)O(33) 1225.459583 H(79)C(48)N(3)O(33) N-linked glycosylation 1767 0.5 -dHex(1)Hex(3)HexNAc(3)@S 1241.454497 1242.1406 H(79)C(48)N(3)O(34) 1241.454497 H(79)C(48)N(3)O(34) O-linked glycosylation 1768 0.5 -dHex(1)Hex(3)HexNAc(3)@T 1241.454497 1242.1406 H(79)C(48)N(3)O(34) 1241.454497 H(79)C(48)N(3)O(34) O-linked glycosylation 1768 0.5 -dHex(1)Hex(3)HexNAc(3)@N 1241.454497 1242.1406 H(79)C(48)N(3)O(34) 1241.454497 H(79)C(48)N(3)O(34) N-linked glycosylation 1768 0.5 -Hex(4)HexNAc(3)@S 1257.449412 1258.14 H(79)C(48)N(3)O(35) 1257.449412 H(79)C(48)N(3)O(35) O-linked glycosylation 1769 0.5 -Hex(4)HexNAc(3)@T 1257.449412 1258.14 H(79)C(48)N(3)O(35) 1257.449412 H(79)C(48)N(3)O(35) O-linked glycosylation 1769 0.5 -Hex(4)HexNAc(3)@N 1257.449412 1258.14 H(79)C(48)N(3)O(35) 1257.449412 H(79)C(48)N(3)O(35) N-linked glycosylation 1769 0.5 -dHex(2)Hex(4)HexNAc(2)@S 1346.485857 1347.2298 H(86)C(52)N(2)O(38) 1346.485857 H(86)C(52)N(2)O(38) O-linked glycosylation 1770 0.5 -dHex(2)Hex(4)HexNAc(2)@T 1346.485857 1347.2298 H(86)C(52)N(2)O(38) 1346.485857 H(86)C(52)N(2)O(38) O-linked glycosylation 1770 0.5 -dHex(2)Hex(4)HexNAc(2)@N 1346.485857 1347.2298 H(86)C(52)N(2)O(38) 1346.485857 H(86)C(52)N(2)O(38) N-linked glycosylation 1770 0.5 -dHex(2)Hex(3)HexNAc(3)@S 1387.512406 1388.2818 H(89)C(54)N(3)O(38) 1387.512406 H(89)C(54)N(3)O(38) O-linked glycosylation 1771 0.5 -dHex(2)Hex(3)HexNAc(3)@T 1387.512406 1388.2818 H(89)C(54)N(3)O(38) 1387.512406 H(89)C(54)N(3)O(38) O-linked glycosylation 1771 0.5 -dHex(2)Hex(3)HexNAc(3)@N 1387.512406 1388.2818 H(89)C(54)N(3)O(38) 1387.512406 H(89)C(54)N(3)O(38) N-linked glycosylation 1771 0.5 -Hex(3)HexNAc(5)@S 1501.555334 1502.3844 H(95)C(58)N(5)O(40) 1501.555334 H(95)C(58)N(5)O(40) O-linked glycosylation 1772 0.5 -Hex(3)HexNAc(5)@T 1501.555334 1502.3844 H(95)C(58)N(5)O(40) 1501.555334 H(95)C(58)N(5)O(40) O-linked glycosylation 1772 0.5 -Hex(3)HexNAc(5)@N 1501.555334 1502.3844 H(95)C(58)N(5)O(40) 1501.555334 H(95)C(58)N(5)O(40) N-linked glycosylation 1772 0.5 -Hex(4)HexNAc(3)NeuAc(1)@N 1548.544828 1549.3945 H(96)C(59)N(4)O(43) 1548.544828 H(96)C(59)N(4)O(43) N-linked glycosylation 1773 0.5 -Hex(4)HexNAc(3)NeuAc(1)@T 1548.544828 1549.3945 H(96)C(59)N(4)O(43) 1548.544828 H(96)C(59)N(4)O(43) O-linked glycosylation 1773 0.5 -Hex(4)HexNAc(3)NeuAc(1)@S 1548.544828 1549.3945 H(96)C(59)N(4)O(43) 1548.544828 H(96)C(59)N(4)O(43) O-linked glycosylation 1773 0.5 -dHex(2)Hex(3)HexNAc(4)@S 1590.591779 1591.4743 H(102)C(62)N(4)O(43) 1590.591779 H(102)C(62)N(4)O(43) O-linked glycosylation 1774 0.5 -dHex(2)Hex(3)HexNAc(4)@T 1590.591779 1591.4743 H(102)C(62)N(4)O(43) 1590.591779 H(102)C(62)N(4)O(43) O-linked glycosylation 1774 0.5 -dHex(2)Hex(3)HexNAc(4)@N 1590.591779 1591.4743 H(102)C(62)N(4)O(43) 1590.591779 H(102)C(62)N(4)O(43) N-linked glycosylation 1774 0.5 -dHex(1)Hex(3)HexNAc(5)@S 1647.613242 1648.5256 H(105)C(64)N(5)O(44) 1647.613242 H(105)C(64)N(5)O(44) O-linked glycosylation 1775 0.5 -dHex(1)Hex(3)HexNAc(5)@T 1647.613242 1648.5256 H(105)C(64)N(5)O(44) 1647.613242 H(105)C(64)N(5)O(44) O-linked glycosylation 1775 0.5 -dHex(1)Hex(3)HexNAc(5)@N 1647.613242 1648.5256 H(105)C(64)N(5)O(44) 1647.613242 H(105)C(64)N(5)O(44) N-linked glycosylation 1775 0.5 -Hex(3)HexNAc(6)@S 1704.634706 1705.5769 H(108)C(66)N(6)O(45) 1704.634706 H(108)C(66)N(6)O(45) O-linked glycosylation 1776 0.5 -Hex(3)HexNAc(6)@T 1704.634706 1705.5769 H(108)C(66)N(6)O(45) 1704.634706 H(108)C(66)N(6)O(45) O-linked glycosylation 1776 0.5 -Hex(3)HexNAc(6)@N 1704.634706 1705.5769 H(108)C(66)N(6)O(45) 1704.634706 H(108)C(66)N(6)O(45) N-linked glycosylation 1776 0.5 -Hex(4)HexNAc(4)NeuAc(1)@S 1751.624201 1752.5871 H(109)C(67)N(5)O(48) 1751.624201 H(109)C(67)N(5)O(48) O-linked glycosylation 1777 0.5 -Hex(4)HexNAc(4)NeuAc(1)@T 1751.624201 1752.5871 H(109)C(67)N(5)O(48) 1751.624201 H(109)C(67)N(5)O(48) O-linked glycosylation 1777 0.5 -Hex(4)HexNAc(4)NeuAc(1)@N 1751.624201 1752.5871 H(109)C(67)N(5)O(48) 1751.624201 H(109)C(67)N(5)O(48) N-linked glycosylation 1777 0.5 -dHex(2)Hex(4)HexNAc(4)@N 1752.644602 1753.6149 H(112)C(68)N(4)O(48) 1752.644602 H(112)C(68)N(4)O(48) N-linked glycosylation 1778 0.5 -dHex(2)Hex(4)HexNAc(4)@T 1752.644602 1753.6149 H(112)C(68)N(4)O(48) 1752.644602 H(112)C(68)N(4)O(48) O-linked glycosylation 1778 0.5 -dHex(2)Hex(4)HexNAc(4)@S 1752.644602 1753.6149 H(112)C(68)N(4)O(48) 1752.644602 H(112)C(68)N(4)O(48) O-linked glycosylation 1778 0.5 -Hex(6)HexNAc(4)@S 1784.634431 1785.6137 H(112)C(68)N(4)O(50) 1784.634431 H(112)C(68)N(4)O(50) O-linked glycosylation 1779 0.5 -Hex(6)HexNAc(4)@T 1784.634431 1785.6137 H(112)C(68)N(4)O(50) 1784.634431 H(112)C(68)N(4)O(50) O-linked glycosylation 1779 0.5 -Hex(6)HexNAc(4)@N 1784.634431 1785.6137 H(112)C(68)N(4)O(50) 1784.634431 H(112)C(68)N(4)O(50) N-linked glycosylation 1779 0.5 -Hex(5)HexNAc(5)@S 1825.660981 1826.6656 H(115)C(70)N(5)O(50) 1825.660981 H(115)C(70)N(5)O(50) O-linked glycosylation 1780 0.5 -Hex(5)HexNAc(5)@T 1825.660981 1826.6656 H(115)C(70)N(5)O(50) 1825.660981 H(115)C(70)N(5)O(50) O-linked glycosylation 1780 0.5 -Hex(5)HexNAc(5)@N 1825.660981 1826.6656 H(115)C(70)N(5)O(50) 1825.660981 H(115)C(70)N(5)O(50) N-linked glycosylation 1780 0.5 -dHex(1)Hex(3)HexNAc(6)@S 1850.692615 1851.7181 H(118)C(72)N(6)O(49) 1850.692615 H(118)C(72)N(6)O(49) O-linked glycosylation 1781 0.5 -dHex(1)Hex(3)HexNAc(6)@T 1850.692615 1851.7181 H(118)C(72)N(6)O(49) 1850.692615 H(118)C(72)N(6)O(49) O-linked glycosylation 1781 0.5 -dHex(1)Hex(3)HexNAc(6)@N 1850.692615 1851.7181 H(118)C(72)N(6)O(49) 1850.692615 H(118)C(72)N(6)O(49) N-linked glycosylation 1781 0.5 -dHex(1)Hex(4)HexNAc(4)NeuAc(1)@N 1897.68211 1898.7283 H(119)C(73)N(5)O(52) 1897.68211 H(119)C(73)N(5)O(52) N-linked glycosylation 1782 0.5 -dHex(1)Hex(4)HexNAc(4)NeuAc(1)@T 1897.68211 1898.7283 H(119)C(73)N(5)O(52) 1897.68211 H(119)C(73)N(5)O(52) O-linked glycosylation 1782 0.5 -dHex(1)Hex(4)HexNAc(4)NeuAc(1)@S 1897.68211 1898.7283 H(119)C(73)N(5)O(52) 1897.68211 H(119)C(73)N(5)O(52) O-linked glycosylation 1782 0.5 -dHex(3)Hex(4)HexNAc(4)@S 1898.702511 1899.7561 H(122)C(74)N(4)O(52) 1898.702511 H(122)C(74)N(4)O(52) O-linked glycosylation 1783 0.5 -dHex(3)Hex(4)HexNAc(4)@T 1898.702511 1899.7561 H(122)C(74)N(4)O(52) 1898.702511 H(122)C(74)N(4)O(52) O-linked glycosylation 1783 0.5 -dHex(3)Hex(4)HexNAc(4)@N 1898.702511 1899.7561 H(122)C(74)N(4)O(52) 1898.702511 H(122)C(74)N(4)O(52) N-linked glycosylation 1783 0.5 -dHex(1)Hex(3)HexNAc(5)NeuAc(1)@S 1938.708659 1939.7802 H(122)C(75)N(6)O(52) 1938.708659 H(122)C(75)N(6)O(52) O-linked glycosylation 1784 0.5 -dHex(1)Hex(3)HexNAc(5)NeuAc(1)@T 1938.708659 1939.7802 H(122)C(75)N(6)O(52) 1938.708659 H(122)C(75)N(6)O(52) O-linked glycosylation 1784 0.5 -dHex(1)Hex(3)HexNAc(5)NeuAc(1)@N 1938.708659 1939.7802 H(122)C(75)N(6)O(52) 1938.708659 H(122)C(75)N(6)O(52) N-linked glycosylation 1784 0.5 -dHex(2)Hex(4)HexNAc(5)@S 1955.723975 1956.8074 H(125)C(76)N(5)O(53) 1955.723975 H(125)C(76)N(5)O(53) O-linked glycosylation 1785 0.5 -dHex(2)Hex(4)HexNAc(5)@T 1955.723975 1956.8074 H(125)C(76)N(5)O(53) 1955.723975 H(125)C(76)N(5)O(53) O-linked glycosylation 1785 0.5 -dHex(2)Hex(4)HexNAc(5)@N 1955.723975 1956.8074 H(125)C(76)N(5)O(53) 1955.723975 H(125)C(76)N(5)O(53) N-linked glycosylation 1785 0.5 -NQIGG@K 469.228496 469.4921 H(31)C(19)N(7)O(7) 0.0 Post-translational 1799 0.0 -Carboxyethylpyrrole@K 122.036779 122.1213 H(6)C(7)O(2) 0.0 Other 1800 0.0 -Fluorescein-tyramine@Y 493.116152 493.4637 H(19)C(29)N(1)O(7) 0.0 Chemical derivative 1801 0.0 -dHex(1)Hex(7)HexNAc(4)@N 2092.745164 2093.8955 H(132)C(80)N(4)O(59) 0.0 N-linked glycosylation 1840 0.0 -betaFNA@C 454.210387 454.5155 H(30)C(25)N(2)O(6) 0.0 Chemical derivative 1839 0.0 -betaFNA@K 454.210387 454.5155 H(30)C(25)N(2)O(6) 0.0 Chemical derivative 1839 0.0 -Brij58@Any_N-term 224.250401 224.4253 H(32)C(16) 0.0 Other 1838 0.0 -Brij35@Any_N-term 168.187801 168.319 H(24)C(12) 0.0 Other 1837 0.0 -Triton@Any_N-term 188.156501 188.3086 H(20)C(14) 0.0 Other 1836 0.0 -Triton@Any_C-term 188.156501 188.3086 H(20)C(14) 0.0 Other 1836 0.0 -Tween80@Any_C-term 263.237491 263.4381 H(31)C(18)O(1) 0.0 Other 1835 0.0 -Tween20@Any_N-term 165.164326 165.2951 H(21)C(12) 0.0 Other 1834 0.0 -Tris@N 104.071154 104.1277 H(10)C(4)N(1)O(2) 0.0 Artefact 1831 0.0 -Biotin-tyramide@Y 361.146012 361.4585 H(23)C(18)N(3)O(3)S(1) 0.0 Chemical derivative 1830 0.0 -LRGG+dimethyl@K 411.259403 411.4991 H(33)C(18)N(7)O(4) 0.0 Post-translational 1829 0.0 -RNPXL@R^Any_N-term 324.035867 324.1813 H(13)C(9)N(2)O(9)P(1) 324.035867 H(13)C(9)N(2)O(9)P(1) Other 1825 0.5 -RNPXL@K^Any_N-term 324.035867 324.1813 H(13)C(9)N(2)O(9)P(1) 324.035867 H(13)C(9)N(2)O(9)P(1) Other 1825 0.5 -GEE@Q 86.036779 86.0892 H(6)C(4)O(2) 0.0 Chemical derivative 1824 0.0 -Glu->pyro-Glu+Methyl@E^Any_N-term -3.994915 -3.9887 C(1)O(-1) 0.0 Artefact 1826 0.0 -Glu->pyro-Glu+Methyl:2H(2)13C(1)@E^Any_N-term -0.979006 -0.9837 H(-2)2H(2)13C(1)O(-1) 0.0 Artefact 1827 0.0 -LRGG+methyl@K 397.243753 397.4725 H(31)C(17)N(7)O(4) 0.0 Post-translational 1828 0.0 -NP40@Any_N-term 220.182715 220.3505 H(24)C(15)O(1) 0.0 Other 1833 0.0 -IASD@C 452.034807 452.4582 H(16)C(18)N(2)O(8)S(2) 0.0 Chemical derivative 1832 0.0 -Biotin:Thermo-21328@K 389.090154 389.5564 H(23)C(15)N(3)O(3)S(3) 0.0 Chemical derivative 1841 0.0 -Biotin:Thermo-21328@Any_N-term 389.090154 389.5564 H(23)C(15)N(3)O(3)S(3) 0.0 Chemical derivative 1841 0.0 -PhosphoCytidine@Y 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 -PhosphoCytidine@T 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 -PhosphoCytidine@S 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 -AzidoF@F 41.001397 41.0122 H(-1)N(3) 0.0 Chemical derivative 1845 0.0 -Dimethylaminoethyl@C 71.073499 71.121 H(9)C(4)N(1) 0.0 Chemical derivative 1846 0.0 -Gluratylation@K 114.031694 114.0993 H(6)C(5)O(3) 0.0 Post-translational 1848 0.0 -hydroxyisobutyryl@K 86.036779 86.0892 H(6)C(4)O(2) 0.0 Post-translational 1849 0.0 -MeMePhosphorothioate@S 107.979873 108.0993 H(5)C(2)O(1)P(1)S(1) 0.0 Chemical derivative 1868 0.0 -Cation:Fe[III]@D 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 -Cation:Fe[III]@E 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 -Cation:Fe[III]@Any_C-term 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 -DTT@C 151.996571 152.2351 H(8)C(4)O(2)S(2) 0.0 Artefact 1871 0.0 -DYn-2@C 161.09664 161.2203 H(13)C(11)O(1) 0.0 Other 1872 0.0 -Xlink:DSSO[176]@K 176.01433 176.1903 H(8)C(6)O(4)S(1) 0.0 Chemical derivative 1878 0.0 -Xlink:DSSO[176]@Protein_N-term 176.01433 176.1903 H(8)C(6)O(4)S(1) 0.0 Chemical derivative 1878 0.0 -MesitylOxide@K 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 -MesitylOxide@H 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 -MesitylOxide@Protein_N-term 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 -Xlink:DSS[259]@K 259.141973 259.2988 H(21)C(12)N(1)O(5) 0.0 Chemical derivative 1877 0.0 -Xlink:DSS[259]@Protein_N-term 259.141973 259.2988 H(21)C(12)N(1)O(5) 0.0 Chemical derivative 1877 0.0 -methylol@Y 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 -methylol@W 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 -methylol@K 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 -Xlink:DSSO[175]@K 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1879 0.0 -Xlink:DSSO[175]@Protein_N-term 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1879 0.0 -Xlink:DSSO[279]@K 279.077658 279.3101 H(17)C(10)N(1)O(6)S(1) 0.0 Chemical derivative 1880 0.0 -Xlink:DSSO[279]@Protein_N-term 279.077658 279.3101 H(17)C(10)N(1)O(6)S(1) 0.0 Chemical derivative 1880 0.0 -Xlink:DSSO[54]@Protein_N-term 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 1881 0.0 -Xlink:DSSO[54]@K 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 1881 0.0 -Xlink:DSSO[86]@K 85.982635 86.1124 H(2)C(3)O(1)S(1) 0.0 Chemical derivative 1882 0.0 -Xlink:DSSO[86]@Protein_N-term 85.982635 86.1124 H(2)C(3)O(1)S(1) 0.0 Chemical derivative 1882 0.0 -Xlink:DSSO[104]@K 103.9932 104.1277 H(4)C(3)O(2)S(1) 0.0 Chemical derivative 1883 0.0 -Xlink:DSSO[104]@Protein_N-term 103.9932 104.1277 H(4)C(3)O(2)S(1) 0.0 Chemical derivative 1883 0.0 -Xlink:BuUrBu[111]@K 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 -Xlink:BuUrBu[111]@Protein_N-term 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 -Xlink:BuUrBu[85]@K 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 -Xlink:BuUrBu[85]@Protein_N-term 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 -Xlink:BuUrBu[214]@Protein_N-term 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 -Xlink:BuUrBu[214]@K 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 -Xlink:BuUrBu[317]@Protein_N-term 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 -Xlink:BuUrBu[317]@K 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 -Xlink:DSSO[158]@K 158.003765 158.175 H(6)C(6)O(3)S(1) 0.0 Chemical derivative 1896 0.0 -Xlink:DSSO[158]@Protein_N-term 158.003765 158.175 H(6)C(6)O(3)S(1) 0.0 Chemical derivative 1896 0.0 -Xlink:DSS[138]@K 138.06808 138.1638 H(10)C(8)O(2) 0.0 Chemical derivative 1898 0.0 -Xlink:DSS[138]@Protein_N-term 138.06808 138.1638 H(10)C(8)O(2) 0.0 Chemical derivative 1898 0.0 -Xlink:BuUrBu[196]@Protein_N-term 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 -Xlink:BuUrBu[196]@K 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 -Xlink:DTBP[172]@K 172.01289 172.2711 H(8)C(6)N(2)S(2) 0.0 Chemical derivative 1900 0.0 -Xlink:DTBP[172]@Protein_N-term 172.01289 172.2711 H(8)C(6)N(2)S(2) 0.0 Chemical derivative 1900 0.0 -Xlink:DST[114]@K 113.995309 114.0563 H(2)C(4)O(4) 0.0 Chemical derivative 1901 0.0 -Xlink:DST[114]@Protein_N-term 113.995309 114.0563 H(2)C(4)O(4) 0.0 Chemical derivative 1901 0.0 -Xlink:DTSSP[174]@K 173.980921 174.2406 H(6)C(6)O(2)S(2) 0.0 Chemical derivative 1902 0.0 -Xlink:DTSSP[174]@Protein_N-term 173.980921 174.2406 H(6)C(6)O(2)S(2) 0.0 Chemical derivative 1902 0.0 -Xlink:SMCC[219]@C 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 -Xlink:SMCC[219]@K 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 -Xlink:SMCC[219]@Protein_N-term 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 -Cation:Al[III]@D 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 -Cation:Al[III]@E 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 -Cation:Al[III]@Any_C-term 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 -Xlink:BS2G[113]@Protein_N-term 113.047679 113.1146 H(7)C(5)N(1)O(2) 0.0 Chemical derivative 1906 0.0 -Xlink:BS2G[113]@K 113.047679 113.1146 H(7)C(5)N(1)O(2) 0.0 Chemical derivative 1906 0.0 -Xlink:BS2G[114]@Protein_N-term 114.031694 114.0993 H(6)C(5)O(3) 0.0 Chemical derivative 1907 0.0 -Xlink:BS2G[114]@K 114.031694 114.0993 H(6)C(5)O(3) 0.0 Chemical derivative 1907 0.0 -Xlink:BS2G[217]@Protein_N-term 217.095023 217.2191 H(15)C(9)N(1)O(5) 0.0 Chemical derivative 1908 0.0 -Xlink:BS2G[217]@K 217.095023 217.2191 H(15)C(9)N(1)O(5) 0.0 Chemical derivative 1908 0.0 -Xlink:DMP[139]@K 139.110947 139.1982 H(13)C(7)N(3) 0.0 Chemical derivative 1911 0.0 -Xlink:DMP[139]@Protein_N-term 139.110947 139.1982 H(13)C(7)N(3) 0.0 Chemical derivative 1911 0.0 -Xlink:DMP[122]@K 122.084398 122.1677 H(10)C(7)N(2) 0.0 Chemical derivative 1912 0.0 -Xlink:DMP[122]@Protein_N-term 122.084398 122.1677 H(10)C(7)N(2) 0.0 Chemical derivative 1912 0.0 -glyoxalAGE@R 21.98435 22.0055 H(-2)C(2) 0.0 Post-translational 1913 0.0 -Met->AspSA@M -32.008456 -32.1081 H(-4)C(-1)O(1)S(-1) 0.0 Chemical derivative 1914 0.0 -Decarboxylation@D -30.010565 -30.026 H(-2)C(-1)O(-1) 0.0 Chemical derivative 1915 0.0 -Decarboxylation@E -30.010565 -30.026 H(-2)C(-1)O(-1) 0.0 Chemical derivative 1915 0.0 -Aspartylurea@H -10.031969 -10.0412 H(-2)C(-1)N(-2)O(2) 0.0 Chemical derivative 1916 0.0 -Formylasparagine@H 4.97893 4.9735 H(-1)C(-1)N(-1)O(2) 0.0 Chemical derivative 1917 0.0 -Carbonyl@S 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 -Carbonyl@R 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 -Carbonyl@Q 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 -Carbonyl@L 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 -Carbonyl@I 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 -Carbonyl@E 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 -Carbonyl@A 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 -Carbonyl@V 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 -Pro->HAVA@P 18.010565 18.0153 H(2)O(1) 0.0 Chemical derivative 1922 0.0 -AFB1_Dialdehyde@K 310.047738 310.2577 H(10)C(17)O(6) 0.0 Post-translational 1920 0.0 -Delta:H(-4)O(2)@W 27.958529 27.967 H(-4)O(2) 0.0 Chemical derivative 1923 0.0 -Delta:H(-4)O(3)@W 43.953444 43.9664 H(-4)O(3) 0.0 Chemical derivative 1924 0.0 -Delta:O(4)@W 63.979659 63.9976 O(4) 0.0 Artefact 1925 0.0 -Delta:H(3)C(3)O(2)@K 71.013304 71.0547 H(3)C(3)O(2) 0.0 Artefact 1926 0.0 -Delta:H(4)C(5)O(1)@R 80.026215 80.0847 H(4)C(5)O(1) 0.0 Chemical derivative 1927 0.0 -Delta:H(10)C(8)O(1)@K 122.073165 122.1644 H(10)C(8)O(1) 0.0 Artefact 1928 0.0 -Delta:H(6)C(7)O(4)@R 154.026609 154.1201 H(6)C(7)O(4) 0.0 Chemical derivative 1929 0.0 -Hex(2)Sulf(1)@T 404.062462 404.3444 H(20)C(12)O(13)S(1) 404.062462 H(20)C(12)O(13)S(1) O-linked glycosylation 1932 0.5 -Hex(2)Sulf(1)@S 404.062462 404.3444 H(20)C(12)O(13)S(1) 404.062462 H(20)C(12)O(13)S(1) O-linked glycosylation 1932 0.5 -Pent(2)@T 264.084518 264.2292 H(16)C(10)O(8) 264.084518 H(16)C(10)O(8) O-linked glycosylation 1930 0.5 -Pent(2)@S 264.084518 264.2292 H(16)C(10)O(8) 264.084518 H(16)C(10)O(8) O-linked glycosylation 1930 0.5 -Pent(1)HexNAc(1)@T 335.121631 335.3071 H(21)C(13)N(1)O(9) 335.121631 H(21)C(13)N(1)O(9) O-linked glycosylation 1931 0.5 -Pent(1)HexNAc(1)@S 335.121631 335.3071 H(21)C(13)N(1)O(9) 335.121631 H(21)C(13)N(1)O(9) O-linked glycosylation 1931 0.5 -Hex(1)Pent(2)Me(1)@T 440.152991 440.3964 H(28)C(17)O(13) 440.152991 H(28)C(17)O(13) O-linked glycosylation 1933 0.5 -Hex(1)Pent(2)Me(1)@S 440.152991 440.3964 H(28)C(17)O(13) 440.152991 H(28)C(17)O(13) O-linked glycosylation 1933 0.5 -HexNAc(2)Sulf(1)@S 486.11556 486.4482 H(26)C(16)N(2)O(13)S(1) 486.11556 H(26)C(16)N(2)O(13)S(1) O-linked glycosylation 1934 0.5 -HexNAc(2)Sulf(1)@T 486.11556 486.4482 H(26)C(16)N(2)O(13)S(1) 486.11556 H(26)C(16)N(2)O(13)S(1) O-linked glycosylation 1934 0.5 -Hex(1)Pent(3)Me(1)@S 572.19525 572.511 H(36)C(22)O(17) 572.19525 H(36)C(22)O(17) O-linked glycosylation 1935 0.5 -Hex(1)Pent(3)Me(1)@T 572.19525 572.511 H(36)C(22)O(17) 572.19525 H(36)C(22)O(17) O-linked glycosylation 1935 0.5 -Hex(2)Pent(2)@S 588.190165 588.5104 H(36)C(22)O(18) 588.190165 H(36)C(22)O(18) O-linked glycosylation 1936 0.5 -Hex(2)Pent(2)@T 588.190165 588.5104 H(36)C(22)O(18) 588.190165 H(36)C(22)O(18) O-linked glycosylation 1936 0.5 -Hex(2)Pent(2)Me(1)@S 602.205815 602.537 H(38)C(23)O(18) 602.205815 H(38)C(23)O(18) O-linked glycosylation 1937 0.5 -Hex(2)Pent(2)Me(1)@T 602.205815 602.537 H(38)C(23)O(18) 602.205815 H(38)C(23)O(18) O-linked glycosylation 1937 0.5 -Hex(4)HexA(1)@S 824.243382 824.6865 H(48)C(30)O(26) 824.243382 H(48)C(30)O(26) O-linked glycosylation 1938 0.5 -Hex(4)HexA(1)@T 824.243382 824.6865 H(48)C(30)O(26) 824.243382 H(48)C(30)O(26) O-linked glycosylation 1938 0.5 -Hex(2)HexNAc(1)Pent(1)HexA(1)@S 835.259366 835.7125 H(49)C(31)N(1)O(25) 835.259366 H(49)C(31)N(1)O(25) O-linked glycosylation 1939 0.5 -Hex(2)HexNAc(1)Pent(1)HexA(1)@T 835.259366 835.7125 H(49)C(31)N(1)O(25) 835.259366 H(49)C(31)N(1)O(25) O-linked glycosylation 1939 0.5 -Hex(3)HexNAc(1)HexA(1)@S 865.269931 865.7384 H(51)C(32)N(1)O(26) 865.269931 H(51)C(32)N(1)O(26) O-linked glycosylation 1940 0.5 -Hex(3)HexNAc(1)HexA(1)@T 865.269931 865.7384 H(51)C(32)N(1)O(26) 865.269931 H(51)C(32)N(1)O(26) O-linked glycosylation 1940 0.5 -Hex(1)HexNAc(2)dHex(2)Sulf(1)@S 940.284201 940.8712 H(56)C(34)N(2)O(26)S(1) 940.284201 H(56)C(34)N(2)O(26)S(1) O-linked glycosylation 1941 0.5 -Hex(1)HexNAc(2)dHex(2)Sulf(1)@T 940.284201 940.8712 H(56)C(34)N(2)O(26)S(1) 940.284201 H(56)C(34)N(2)O(26)S(1) O-linked glycosylation 1941 0.5 -HexA(2)HexNAc(3)@S 961.302294 961.8258 H(55)C(36)N(3)O(27) 961.302294 H(55)C(36)N(3)O(27) O-linked glycosylation 1942 0.5 -HexA(2)HexNAc(3)@T 961.302294 961.8258 H(55)C(36)N(3)O(27) 961.302294 H(55)C(36)N(3)O(27) O-linked glycosylation 1942 0.5 -dHex(1)Hex(4)HexA(1)@T 970.301291 970.8277 H(58)C(36)O(30) 970.301291 H(58)C(36)O(30) O-linked glycosylation 1943 0.5 -dHex(1)Hex(4)HexA(1)@S 970.301291 970.8277 H(58)C(36)O(30) 970.301291 H(58)C(36)O(30) O-linked glycosylation 1943 0.5 -Hex(5)HexA(1)@S 986.296206 986.8271 H(58)C(36)O(31) 986.296206 H(58)C(36)O(31) O-linked glycosylation 1944 0.5 -Hex(5)HexA(1)@T 986.296206 986.8271 H(58)C(36)O(31) 986.296206 H(58)C(36)O(31) O-linked glycosylation 1944 0.5 -Hex(4)HexA(1)HexNAc(1)@T 1027.322755 1027.879 H(61)C(38)N(1)O(31) 1027.322755 H(61)C(38)N(1)O(31) O-linked glycosylation 1945 0.5 -Hex(4)HexA(1)HexNAc(1)@S 1027.322755 1027.879 H(61)C(38)N(1)O(31) 1027.322755 H(61)C(38)N(1)O(31) O-linked glycosylation 1945 0.5 -dHex(3)Hex(3)HexNAc(1)@T 1127.41157 1128.0379 H(73)C(44)N(1)O(32) 1127.41157 H(73)C(44)N(1)O(32) O-linked glycosylation 1946 0.5 -dHex(3)Hex(3)HexNAc(1)@S 1127.41157 1128.0379 H(73)C(44)N(1)O(32) 1127.41157 H(73)C(44)N(1)O(32) O-linked glycosylation 1946 0.5 -Hex(6)HexNAc(1)@N 1175.396314 1176.0361 H(73)C(44)N(1)O(35) 1175.396314 H(73)C(44)N(1)O(35) N-linked glycosylation 1947 0.5 -Hex(1)HexNAc(4)dHex(1)Sulf(1)@T 1200.385037 1201.1151 H(72)C(44)N(4)O(32)S(1) 1200.385037 H(72)C(44)N(4)O(32)S(1) O-linked glycosylation 1948 0.5 -Hex(1)HexNAc(4)dHex(1)Sulf(1)@S 1200.385037 1201.1151 H(72)C(44)N(4)O(32)S(1) 1200.385037 H(72)C(44)N(4)O(32)S(1) O-linked glycosylation 1948 0.5 -dHex(1)Hex(2)HexNAc(1)NeuAc(2)@T 1255.433762 1256.1241 H(77)C(48)N(3)O(35) 1255.433762 H(77)C(48)N(3)O(35) O-linked glycosylation 1949 0.5 -dHex(1)Hex(2)HexNAc(1)NeuAc(2)@S 1255.433762 1256.1241 H(77)C(48)N(3)O(35) 1255.433762 H(77)C(48)N(3)O(35) O-linked glycosylation 1949 0.5 -dHex(3)Hex(3)HexNAc(2)@T 1330.490942 1331.2304 H(86)C(52)N(2)O(37) 1330.490942 H(86)C(52)N(2)O(37) O-linked glycosylation 1950 0.5 -dHex(3)Hex(3)HexNAc(2)@S 1330.490942 1331.2304 H(86)C(52)N(2)O(37) 1330.490942 H(86)C(52)N(2)O(37) O-linked glycosylation 1950 0.5 -dHex(2)Hex(1)HexNAc(4)Sulf(1)@T 1346.442946 1347.2563 H(82)C(50)N(4)O(36)S(1) 1346.442946 H(82)C(50)N(4)O(36)S(1) O-linked glycosylation 1951 0.5 -dHex(2)Hex(1)HexNAc(4)Sulf(1)@S 1346.442946 1347.2563 H(82)C(50)N(4)O(36)S(1) 1346.442946 H(82)C(50)N(4)O(36)S(1) O-linked glycosylation 1951 0.5 -dHex(1)Hex(2)HexNAc(4)Sulf(2)@T 1442.394675 1443.3189 H(82)C(50)N(4)O(40)S(2) 1442.394675 H(82)C(50)N(4)O(40)S(2) O-linked glycosylation 1952 0.5 -dHex(1)Hex(2)HexNAc(4)Sulf(2)@S 1442.394675 1443.3189 H(82)C(50)N(4)O(40)S(2) 1442.394675 H(82)C(50)N(4)O(40)S(2) O-linked glycosylation 1952 0.5 -Hex(9)@N 1458.475412 1459.2654 H(90)C(54)O(45) 1458.475412 H(90)C(54)O(45) N-linked glycosylation 1953 0.5 -dHex(2)Hex(3)HexNAc(3)Sulf(1)@T 1467.469221 1468.345 H(89)C(54)N(3)O(41)S(1) 1467.469221 H(89)C(54)N(3)O(41)S(1) O-linked glycosylation 1954 0.5 -dHex(2)Hex(3)HexNAc(3)Sulf(1)@S 1467.469221 1468.345 H(89)C(54)N(3)O(41)S(1) 1467.469221 H(89)C(54)N(3)O(41)S(1) O-linked glycosylation 1954 0.5 -dHex(2)Hex(5)HexNAc(2)Me(1)@T 1522.554331 1523.397 H(98)C(59)N(2)O(43) 1522.554331 H(98)C(59)N(2)O(43) O-linked glycosylation 1955 0.5 -dHex(2)Hex(5)HexNAc(2)Me(1)@S 1522.554331 1523.397 H(98)C(59)N(2)O(43) 1522.554331 H(98)C(59)N(2)O(43) O-linked glycosylation 1955 0.5 -dHex(2)Hex(2)HexNAc(4)Sulf(2)@T 1588.452584 1589.4601 H(92)C(56)N(4)O(44)S(2) 1588.452584 H(92)C(56)N(4)O(44)S(2) O-linked glycosylation 1956 0.5 -dHex(2)Hex(2)HexNAc(4)Sulf(2)@S 1588.452584 1589.4601 H(92)C(56)N(4)O(44)S(2) 1588.452584 H(92)C(56)N(4)O(44)S(2) O-linked glycosylation 1956 0.5 -Hex(9)HexNAc(1)@N 1661.554784 1662.4579 H(103)C(62)N(1)O(50) 1661.554784 H(103)C(62)N(1)O(50) N-linked glycosylation 1957 0.5 -dHex(3)Hex(2)HexNAc(4)Sulf(2)@S 1734.510493 1735.6013 H(102)C(62)N(4)O(48)S(2) 1734.510493 H(102)C(62)N(4)O(48)S(2) O-linked glycosylation 1958 0.5 -dHex(3)Hex(2)HexNAc(4)Sulf(2)@T 1734.510493 1735.6013 H(102)C(62)N(4)O(48)S(2) 1734.510493 H(102)C(62)N(4)O(48)S(2) O-linked glycosylation 1958 0.5 -Hex(4)HexNAc(4)NeuGc(1)@N 1767.619116 1768.5865 H(109)C(67)N(5)O(49) 1767.619116 H(109)C(67)N(5)O(49) N-linked glycosylation 1959 0.5 -Hex(4)HexNAc(4)NeuGc(1)@S 1767.619116 1768.5865 H(109)C(67)N(5)O(49) 1767.619116 H(109)C(67)N(5)O(49) O-linked glycosylation 1959 0.5 -Hex(4)HexNAc(4)NeuGc(1)@T 1767.619116 1768.5865 H(109)C(67)N(5)O(49) 1767.619116 H(109)C(67)N(5)O(49) O-linked glycosylation 1959 0.5 -dHex(4)Hex(3)HexNAc(2)NeuAc(1)@T 1767.644268 1768.6262 H(113)C(69)N(3)O(49) 1767.644268 H(113)C(69)N(3)O(49) O-linked glycosylation 1960 0.5 -dHex(4)Hex(3)HexNAc(2)NeuAc(1)@S 1767.644268 1768.6262 H(113)C(69)N(3)O(49) 1767.644268 H(113)C(69)N(3)O(49) O-linked glycosylation 1960 0.5 -Hex(3)HexNAc(5)NeuAc(1)@N 1792.65075 1793.639 H(112)C(69)N(6)O(48) 1792.65075 H(112)C(69)N(6)O(48) N-linked glycosylation 1961 0.5 -Hex(10)HexNAc(1)@N 1823.607608 1824.5985 H(113)C(68)N(1)O(55) 1823.607608 H(113)C(68)N(1)O(55) N-linked glycosylation 1962 0.5 -dHex(1)Hex(8)HexNAc(2)@N 1848.639242 1849.651 H(116)C(70)N(2)O(54) 1848.639242 H(116)C(70)N(2)O(54) N-linked glycosylation 1963 0.5 -Hex(3)HexNAc(4)NeuAc(2)@N 1880.666794 1881.701 H(116)C(72)N(6)O(51) 1880.666794 H(116)C(72)N(6)O(51) N-linked glycosylation 1964 0.5 -dHex(2)Hex(3)HexNAc(4)NeuAc(1)@N 1881.687195 1882.7289 H(119)C(73)N(5)O(51) 1881.687195 H(119)C(73)N(5)O(51) N-linked glycosylation 1965 0.5 -dHex(2)Hex(2)HexNAc(6)Sulf(1)@S 1914.654515 1915.7819 H(118)C(72)N(6)O(51)S(1) 1914.654515 H(118)C(72)N(6)O(51)S(1) O-linked glycosylation 1966 0.5 -dHex(2)Hex(2)HexNAc(6)Sulf(1)@T 1914.654515 1915.7819 H(118)C(72)N(6)O(51)S(1) 1914.654515 H(118)C(72)N(6)O(51)S(1) O-linked glycosylation 1966 0.5 -Hex(5)HexNAc(4)NeuAc(1)Ac(1)@N 1955.687589 1956.7643 H(121)C(75)N(5)O(54) 1955.687589 H(121)C(75)N(5)O(54) N-linked glycosylation 1967 0.5 -Hex(3)HexNAc(3)NeuAc(3)@S 1968.682838 1969.7631 H(120)C(75)N(6)O(54) 1968.682838 H(120)C(75)N(6)O(54) O-linked glycosylation 1968 0.5 -Hex(3)HexNAc(3)NeuAc(3)@T 1968.682838 1969.7631 H(120)C(75)N(6)O(54) 1968.682838 H(120)C(75)N(6)O(54) O-linked glycosylation 1968 0.5 -Hex(5)HexNAc(4)NeuAc(1)Ac(2)@N 1997.698154 1998.801 H(123)C(77)N(5)O(55) 1997.698154 H(123)C(77)N(5)O(55) N-linked glycosylation 1969 0.5 -Unknown:162@Any_C-term 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 -Unknown:162@E 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 -Unknown:162@D 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 -Unknown:162@Any_N-term 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 -Unknown:177@D 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 -Unknown:177@E 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 -Unknown:177@Any_C-term 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 -Unknown:177@Any_N-term 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 -Unknown:210@D 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 -Unknown:210@E 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 -Unknown:210@Any_C-term 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 -Unknown:210@Any_N-term 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 -Unknown:216@D 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 -Unknown:216@E 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 -Unknown:216@Any_C-term 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 -Unknown:216@Any_N-term 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 -Unknown:234@D 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 -Unknown:234@E 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 -Unknown:234@Any_C-term 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 -Unknown:234@Any_N-term 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 -Unknown:248@D 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 -Unknown:248@E 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 -Unknown:248@Any_C-term 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 -Unknown:248@Any_N-term 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 -Unknown:250@D 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 -Unknown:250@E 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 -Unknown:250@Any_C-term 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 -Unknown:250@Any_N-term 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 -Unknown:302@D 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 -Unknown:302@E 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 -Unknown:302@Any_C-term 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 -Unknown:302@Any_N-term 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 -Unknown:306@D 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 -Unknown:306@E 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 -Unknown:306@Any_C-term 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 -Unknown:306@Any_N-term 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 -Unknown:420@Any_N-term 420.051719 420.5888 H(24)C(12)N(2)O(6)S(4) 420.051719 H(24)C(12)N(2)O(6)S(4) Artefact 1979 0.5 -Unknown:420@Any_C-term 420.051719 420.5888 H(24)C(12)N(2)O(6)S(4) 420.051719 H(24)C(12)N(2)O(6)S(4) Artefact 1979 0.5 -Diethylphosphothione@Y 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 -Diethylphosphothione@T 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 -Diethylphosphothione@S 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 -Diethylphosphothione@K 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 -Diethylphosphothione@H 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 -Diethylphosphothione@C 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 -CIGG@K 330.136176 330.4032 H(22)C(13)N(4)O(4)S(1) 0.0 Post-translational 1990 0.0 -GNLLFLACYCIGG@K 1324.6308 1325.598 H(92)C(61)N(14)O(15)S(2) 0.0 Post-translational 1991 0.0 -Dimethylphosphothione@S 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 -Dimethylphosphothione@K 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 -Dimethylphosphothione@H 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 -Dimethylphosphothione@C 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 -Dimethylphosphothione@Y 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 -Dimethylphosphothione@T 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 -monomethylphosphothione@S 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 -monomethylphosphothione@K 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 -monomethylphosphothione@H 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 -monomethylphosphothione@C 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 -monomethylphosphothione@T 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 -monomethylphosphothione@Y 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 -TMPP-Ac:13C(9)@Y 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Artefact 1993 0.0 -TMPP-Ac:13C(9)@K 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Artefact 1993 0.0 -TMPP-Ac:13C(9)@Any_N-term 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Chemical derivative 1993 0.0 -ZQG@K 320.100836 320.2973 H(16)C(15)N(2)O(6) 134.036779 H(6)C(8)O(2) Chemical derivative 2001 0.5 -Xlink:DST[56]@Protein_N-term 55.989829 56.0202 C(2)O(2) 0.0 Chemical derivative 1999 0.0 -Xlink:DST[56]@K 55.989829 56.0202 C(2)O(2) 0.0 Chemical derivative 1999 0.0 -Haloxon@Y 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 -Haloxon@T 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 -Haloxon@S 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 -Haloxon@K 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 -Haloxon@H 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 -Haloxon@C 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 -Methamidophos-O@Y 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 -Methamidophos-O@T 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 -Methamidophos-O@S 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 -Methamidophos-O@K 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 -Methamidophos-O@H 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 -Methamidophos-O@C 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 -Nitrene@Y 12.995249 12.9988 H(-1)N(1) 0.0 Artefact 2014 0.0 -shTMT@Any_N-term 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 -shTMT@Protein_N-term 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 -shTMT@K 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 -TMTpro@S 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 -TMTpro@H 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 -TMTpro@Protein_N-term 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 -TMTpro@Any_N-term 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 -TMTpro@K 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 -TMTpro@T 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 -TMTpro_zero@S 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 -TMTpro_zero@H 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 -TMTpro_zero@Protein_N-term 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 -TMTpro_zero@Any_N-term 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 -TMTpro_zero@K 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 -TMTpro_zero@T 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 -Andro-H2O@C 332.19876 332.4339 H(28)C(20)O(4) 0.0 Chemical derivative 2025 0.0 -His+O(2)@H 169.048741 169.1381 H(7)C(6)N(3)O(3) 0.0 Post-translational 2027 0.0 -GlyGly@K 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Multiple 121 1000000.0 +mod_name unimod_mass unimod_avge_mass composition unimod_modloss modloss_composition classification unimod_id smiles modloss_importance +Acetyl@T 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 +Acetyl@Protein_N-term 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 +Acetyl@S 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 +Acetyl@C 42.010565 42.0367 H(2)C(2)O(1) 0.0 Post-translational 1 0.0 +Acetyl@Any_N-term 42.010565 42.0367 H(2)C(2)O(1) 0.0 Multiple 1 0.0 +Acetyl@K 42.010565 42.0367 H(2)C(2)O(1) 0.0 Multiple 1 0.0 +Acetyl@Y 42.010565 42.0367 H(2)C(2)O(1) 0.0 Chemical derivative 1 0.0 +Acetyl@H 42.010565 42.0367 H(2)C(2)O(1) 0.0 Chemical derivative 1 0.0 +Acetyl@R 42.010565 42.0367 H(2)C(2)O(1) 0.0 Artefact 1 0.0 +Amidated@Any_C-term -0.984016 -0.9848 H(1)N(1)O(-1) 0.0 Artefact 2 0.0 +Amidated@Protein_C-term -0.984016 -0.9848 H(1)N(1)O(-1) 0.0 Post-translational 2 0.0 +Biotin@Any_N-term 226.077598 226.2954 H(14)C(10)N(2)O(2)S(1) 0.0 Chemical derivative 3 0.0 +Biotin@K 226.077598 226.2954 H(14)C(10)N(2)O(2)S(1) 0.0 Post-translational 3 0.0 +Carbamidomethyl@Y 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@T 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@S 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@E 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@D 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@H 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@Any_N-term 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@K 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Artefact 4 0.0 +Carbamidomethyl@C 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 4 0.0 +Carbamidomethyl@U 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 4 0.0 +Carbamidomethyl@M 57.021464 57.0513 H(3)C(2)N(1)O(1) 105.024835 H(7)C(3)N(1)O(1)S(1) Chemical derivative 4 0.5 +Carbamyl@Y 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Chemical derivative 5 0.0 +Carbamyl@T 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Chemical derivative 5 0.0 +Carbamyl@S 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Chemical derivative 5 0.0 +Carbamyl@M 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 +Carbamyl@C 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 +Carbamyl@R 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Artefact 5 0.0 +Carbamyl@Any_N-term 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Multiple 5 0.0 +Carbamyl@K 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Multiple 5 0.0 +Carbamyl@Protein_N-term 43.005814 43.0247 H(1)C(1)N(1)O(1) 0.0 Post-translational 5 0.0 +Carboxymethyl@Any_N-term 58.005479 58.0361 H(2)C(2)O(2) 0.0 Artefact 6 0.0 +Carboxymethyl@K 58.005479 58.0361 H(2)C(2)O(2) 0.0 Artefact 6 0.0 +Carboxymethyl@C 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 +Carboxymethyl@W 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 +Carboxymethyl@U 58.005479 58.0361 H(2)C(2)O(2) 0.0 Chemical derivative 6 0.0 +Deamidated@Q 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Artefact 7 0.0 +Deamidated@R 0.984016 0.9848 H(-1)N(-1)O(1) 43.005814 H(1)C(1)N(1)O(1) Post-translational 7 0.5 +Deamidated@N 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Artefact 7 0.0 +Deamidated@F^Protein_N-term 0.984016 0.9848 H(-1)N(-1)O(1) 0.0 Post-translational 7 0.0 +ICAT-G@C 486.251206 486.6253 H(38)C(22)N(4)O(6)S(1) 0.0 Isotopic label 8 0.0 +ICAT-G:2H(8)@C 494.30142 494.6746 H(30)2H(8)C(22)N(4)O(6)S(1) 0.0 Isotopic label 9 0.0 +Met->Hse@M^Any_C-term -29.992806 -30.0922 H(-2)C(-1)O(1)S(-1) 0.0 Chemical derivative 10 0.0 +Met->Hsl@M^Any_C-term -48.003371 -48.1075 H(-4)C(-1)S(-1) 0.0 Chemical derivative 11 0.0 +ICAT-D:2H(8)@C 450.275205 450.6221 H(26)2H(8)C(20)N(4)O(5)S(1) 0.0 Isotopic label 12 0.0 +ICAT-D@C 442.224991 442.5728 H(34)C(20)N(4)O(5)S(1) 0.0 Isotopic label 13 0.0 +NIPCAM@C 99.068414 99.1311 H(9)C(5)N(1)O(1) 0.0 Chemical derivative 17 0.0 +PEO-Iodoacetyl-LC-Biotin@C 414.193691 414.5196 H(30)C(18)N(4)O(5)S(1) 0.0 Chemical derivative 20 0.0 +Phospho@E 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 +Phospho@R 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 +Phospho@K 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 +Phospho@H 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 +Phospho@C 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 +Phospho@D 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 +Phospho@Y 79.966331 79.9799 H(1)O(3)P(1) 0.0 Post-translational 21 0.0 +Phospho@T 79.966331 79.9799 H(1)O(3)P(1) 97.976896 H(3)O(4)P(1) Post-translational 21 10000000.0 +Phospho@S 79.966331 79.9799 H(1)O(3)P(1) 97.976896 H(3)O(4)P(1) Post-translational 21 100000000.0 +Methamidophos-S@Y 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 +Methamidophos-S@T 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 +Methamidophos-S@S 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 +Methamidophos-S@K 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 +Methamidophos-S@H 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 +Methamidophos-S@C 108.975121 109.0873 H(4)C(1)N(1)O(1)P(1)S(1) 0.0 Chemical derivative 2007 0.0 +Dehydrated@D -18.010565 -18.0153 H(-2)O(-1) 0.0 Chemical derivative 23 0.0 +Dehydrated@Y -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 +Dehydrated@T -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 +Dehydrated@S -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 +Dehydrated@N^Protein_C-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 +Dehydrated@Q^Protein_C-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Post-translational 23 0.0 +Dehydrated@C^Any_N-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Artefact 23 0.0 +Propionamide@C 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Artefact 24 0.0 +Propionamide@K 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Chemical derivative 24 0.0 +Propionamide@Any_N-term 71.037114 71.0779 H(5)C(3)N(1)O(1) 0.0 Chemical derivative 24 0.0 +Pyridylacetyl@Any_N-term 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 25 0.0 +Pyridylacetyl@K 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 25 0.0 +Pyro-carbamidomethyl@C^Any_N-term 39.994915 40.0208 C(2)O(1) 0.0 Artefact 26 0.0 +Glu->pyro-Glu@E^Any_N-term -18.010565 -18.0153 H(-2)O(-1) 0.0 Artefact 27 0.0 +Gln->pyro-Glu@Q^Any_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Artefact 28 0.0 +SMA@Any_N-term 127.063329 127.1412 H(9)C(6)N(1)O(2) 0.0 Chemical derivative 29 0.0 +SMA@K 127.063329 127.1412 H(9)C(6)N(1)O(2) 0.0 Chemical derivative 29 0.0 +Cation:Na@D 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 +Cation:Na@Any_C-term 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 +Cation:Na@E 21.981943 21.9818 H(-1)Na(1) 0.0 Artefact 30 0.0 +Pyridylethyl@C 105.057849 105.1372 H(7)C(7)N(1) 0.0 Chemical derivative 31 0.0 +Methyl@E 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@D 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@Any_C-term 14.01565 14.0266 H(2)C(1) 0.0 Multiple 34 0.0 +Methyl@Protein_N-term 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@L 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@I 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@R 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@Q 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@Any_N-term 14.01565 14.0266 H(2)C(1) 0.0 Chemical derivative 34 0.0 +Methyl@N 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@K 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@H 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@C 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@S 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Methyl@T 14.01565 14.0266 H(2)C(1) 0.0 Post-translational 34 0.0 +Oxidation@T 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 +Oxidation@E 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 +Oxidation@S 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 +Oxidation@Q 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 +Oxidation@L 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 +Oxidation@I 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 +Oxidation@U 15.994915 15.9994 O(1) 0.0 Multiple 35 0.0 +Oxidation@G^Any_C-term 15.994915 15.9994 O(1) 0.0 Pre-translational 35 0.0 +Oxidation@W 15.994915 15.9994 O(1) 0.0 Artefact 35 0.0 +Oxidation@C 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 +Oxidation@H 15.994915 15.9994 O(1) 0.0 Artefact 35 0.0 +Oxidation@V 15.994915 15.9994 O(1) 0.0 Chemical derivative 35 0.0 +Oxidation@R 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 +Oxidation@M 15.994915 15.9994 O(1) 63.998285 H(4)C(1)O(1)S(1) Artefact 35 0.5 +Oxidation@Y 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 +Oxidation@F 15.994915 15.9994 O(1) 0.0 Artefact 35 0.0 +Oxidation@P 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 +Oxidation@N 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 +Oxidation@K 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 +Oxidation@D 15.994915 15.9994 O(1) 0.0 Post-translational 35 0.0 +Dimethyl@Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Isotopic label 36 0.0 +Dimethyl@P^Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 +Dimethyl@N 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 +Dimethyl@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Isotopic label 36 0.0 +Dimethyl@K 28.0313 28.0532 H(4)C(2) 0.0 Multiple 36 0.0 +Dimethyl@R 28.0313 28.0532 H(4)C(2) 0.0 Post-translational 36 0.0 +Trimethyl@A^Protein_N-term 42.04695 42.0797 H(6)C(3) 0.0 Post-translational 37 0.0 +Trimethyl@R 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 37 0.0 +Trimethyl@K 42.04695 42.0797 H(6)C(3) 59.073499 H(9)C(3)N(1) Post-translational 37 0.5 +Methylthio@C 45.987721 46.0916 H(2)C(1)S(1) 0.0 Multiple 39 0.0 +Methylthio@N 45.987721 46.0916 H(2)C(1)S(1) 0.0 Post-translational 39 0.0 +Methylthio@D 45.987721 46.0916 H(2)C(1)S(1) 0.0 Post-translational 39 0.0 +Methylthio@K 45.987721 46.0916 H(2)C(1)S(1) 0.0 Artefact 39 0.0 +Methylthio@Any_N-term 45.987721 46.0916 H(2)C(1)S(1) 0.0 Artefact 39 0.0 +Sulfo@S 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 +Sulfo@T 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 +Sulfo@Y 79.956815 80.0632 O(3)S(1) 79.956815 O(3)S(1) Post-translational 40 0.5 +Sulfo@C 79.956815 80.0632 O(3)S(1) 0.0 Post-translational 40 0.0 +Hex@C 162.052824 162.1406 H(10)C(6)O(5) 0.0 Other glycosylation 41 0.0 +Hex@W 162.052824 162.1406 H(10)C(6)O(5) 0.0 Other glycosylation 41 0.0 +Hex@T 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) O-linked glycosylation 41 0.5 +Hex@S 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) O-linked glycosylation 41 0.5 +Hex@Any_N-term 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 +Hex@N 162.052824 162.1406 H(10)C(6)O(5) 162.052824 H(10)C(6)O(5) N-linked glycosylation 41 0.5 +Hex@R 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 +Hex@K 162.052824 162.1406 H(10)C(6)O(5) 54.031694 H(6)O(3) Other glycosylation 41 0.5 +Hex@Y 162.052824 162.1406 H(10)C(6)O(5) 0.0 O-linked glycosylation 41 0.0 +Lipoyl@K 188.032956 188.3103 H(12)C(8)O(1)S(2) 0.0 Post-translational 42 0.0 +HexNAc@C 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) Other glycosylation 43 0.5 +HexNAc@T 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) O-linked glycosylation 43 0.5 +HexNAc@S 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) O-linked glycosylation 43 0.5 +HexNAc@N 203.079373 203.1925 H(13)C(8)N(1)O(5) 203.079373 H(13)C(8)N(1)O(5) N-linked glycosylation 43 0.5 +Farnesyl@C 204.187801 204.3511 H(24)C(15) 0.0 Post-translational 44 0.0 +Myristoyl@C 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 +Myristoyl@K 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 +Myristoyl@G^Any_N-term 210.198366 210.3556 H(26)C(14)O(1) 0.0 Post-translational 45 0.0 +PyridoxalPhosphate@K 229.014009 229.1266 H(8)C(8)N(1)O(5)P(1) 0.0 Post-translational 46 0.0 +Palmitoyl@T 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 +Palmitoyl@S 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 +Palmitoyl@K 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 +Palmitoyl@C 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 +Palmitoyl@Protein_N-term 238.229666 238.4088 H(30)C(16)O(1) 0.0 Post-translational 47 0.0 +GeranylGeranyl@C 272.250401 272.4681 H(32)C(20) 0.0 Post-translational 48 0.0 +Phosphopantetheine@S 340.085794 340.333 H(21)C(11)N(2)O(6)P(1)S(1) 0.0 Post-translational 49 0.0 +FAD@Y 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 +FAD@H 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 +FAD@C 783.141486 783.5339 H(31)C(27)N(9)O(15)P(2) 0.0 Post-translational 50 0.0 +Tripalmitate@C^Protein_N-term 788.725777 789.3049 H(96)C(51)O(5) 0.0 Post-translational 51 0.0 +Guanidinyl@K 42.021798 42.04 H(2)C(1)N(2) 0.0 Chemical derivative 52 0.0 +Guanidinyl@Any_N-term 42.021798 42.04 H(2)C(1)N(2) 0.0 Chemical derivative 52 0.0 +HNE@K 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 +HNE@H 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 +HNE@C 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 +HNE@A 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 +HNE@L 156.11503 156.2221 H(16)C(9)O(2) 0.0 Post-translational 53 0.0 +Glucuronyl@T 176.032088 176.1241 H(8)C(6)O(6) 176.032088 H(8)C(6)O(6) O-linked glycosylation 54 0.5 +Glucuronyl@S 176.032088 176.1241 H(8)C(6)O(6) 176.032088 H(8)C(6)O(6) O-linked glycosylation 54 0.5 +Glucuronyl@Protein_N-term 176.032088 176.1241 H(8)C(6)O(6) 0.0 Other glycosylation 54 0.0 +Glutathione@C 305.068156 305.3076 H(15)C(10)N(3)O(6)S(1) 0.0 Post-translational 55 0.0 +Acetyl:2H(3)@Y 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Acetyl:2H(3)@T 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Acetyl:2H(3)@S 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Acetyl:2H(3)@H 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Acetyl:2H(3)@Any_N-term 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Acetyl:2H(3)@K 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Acetyl:2H(3)@Protein_N-term 45.029395 45.0552 H(-1)2H(3)C(2)O(1) 0.0 Isotopic label 56 0.0 +Propionyl@Protein_N-term 56.026215 56.0633 H(4)C(3)O(1) 0.0 Multiple 58 0.0 +Propionyl@T 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 +Propionyl@S 56.026215 56.0633 H(4)C(3)O(1) 0.0 Chemical derivative 58 0.0 +Propionyl@K 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 +Propionyl@Any_N-term 56.026215 56.0633 H(4)C(3)O(1) 0.0 Isotopic label 58 0.0 +Propionyl:13C(3)@Any_N-term 59.036279 59.0412 H(4)13C(3)O(1) 0.0 Isotopic label 59 0.0 +Propionyl:13C(3)@K 59.036279 59.0412 H(4)13C(3)O(1) 0.0 Isotopic label 59 0.0 +GIST-Quat@Any_N-term 127.099714 127.1842 H(13)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 60 0.5 +GIST-Quat@K 127.099714 127.1842 H(13)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 60 0.5 +GIST-Quat:2H(3)@Any_N-term 130.118544 130.2027 H(10)2H(3)C(7)N(1)O(1) 62.09233 H(6)2H(3)C(3)N(1) Isotopic label 61 0.5 +GIST-Quat:2H(3)@K 130.118544 130.2027 H(10)2H(3)C(7)N(1)O(1) 62.09233 H(6)2H(3)C(3)N(1) Isotopic label 61 0.5 +GIST-Quat:2H(6)@Any_N-term 133.137375 133.2212 H(7)2H(6)C(7)N(1)O(1) 65.11116 H(3)2H(6)C(3)N(1) Isotopic label 62 0.5 +GIST-Quat:2H(6)@K 133.137375 133.2212 H(7)2H(6)C(7)N(1)O(1) 65.11116 H(3)2H(6)C(3)N(1) Isotopic label 62 0.5 +GIST-Quat:2H(9)@Any_N-term 136.156205 136.2397 H(4)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 63 0.5 +GIST-Quat:2H(9)@K 136.156205 136.2397 H(4)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 63 0.5 +Succinyl@Protein_N-term 100.016044 100.0728 H(4)C(4)O(3) 0.0 Post-translational 64 0.0 +Succinyl@Any_N-term 100.016044 100.0728 H(4)C(4)O(3) 0.0 Isotopic label 64 0.0 +Succinyl@K 100.016044 100.0728 H(4)C(4)O(3) 0.0 Isotopic label 64 0.0 +Succinyl:2H(4)@Any_N-term 104.041151 104.0974 2H(4)C(4)O(3) 0.0 Isotopic label 65 0.0 +Succinyl:2H(4)@K 104.041151 104.0974 2H(4)C(4)O(3) 0.0 Isotopic label 65 0.0 +Succinyl:13C(4)@Any_N-term 104.029463 104.0434 H(4)13C(4)O(3) 0.0 Isotopic label 66 0.0 +Succinyl:13C(4)@K 104.029463 104.0434 H(4)13C(4)O(3) 0.0 Isotopic label 66 0.0 +probiotinhydrazide@P 258.115047 258.3405 H(18)C(10)N(4)O(2)S(1) 0.0 Chemical derivative 357 0.0 +Pro->pyro-Glu@P 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 359 0.0 +His->Asn@H -23.015984 -23.0366 H(-1)C(-2)N(-1)O(1) 0.0 AA substitution 348 0.0 +His->Asp@H -22.031969 -22.0519 H(-2)C(-2)N(-2)O(2) 0.0 AA substitution 349 0.0 +Trp->Hydroxykynurenin@W 19.989829 19.9881 C(-1)O(2) 0.0 Chemical derivative 350 0.0 +Delta:H(4)C(3)@K 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 +Delta:H(4)C(3)@H 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 +Delta:H(4)C(3)@Protein_N-term 40.0313 40.0639 H(4)C(3) 0.0 Other 256 0.0 +Delta:H(4)C(2)@K 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 +Delta:H(4)C(2)@H 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 +Delta:H(4)C(2)@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Other 255 0.0 +Cys->Dha@C -33.987721 -34.0809 H(-2)S(-1) 0.0 Chemical derivative 368 0.0 +Arg->GluSA@R -43.053433 -43.0711 H(-5)C(-1)N(-3)O(1) 0.0 Chemical derivative 344 0.0 +Trioxidation@Y 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 +Trioxidation@W 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 +Trioxidation@C 47.984744 47.9982 O(3) 0.0 Chemical derivative 345 0.0 +Trioxidation@F 47.984744 47.9982 O(3) 0.0 Artefact 345 0.0 +Iminobiotin@Any_N-term 225.093583 225.3106 H(15)C(10)N(3)O(1)S(1) 0.0 Chemical derivative 89 0.0 +Iminobiotin@K 225.093583 225.3106 H(15)C(10)N(3)O(1)S(1) 0.0 Chemical derivative 89 0.0 +ESP@Any_N-term 338.177647 338.4682 H(26)C(16)N(4)O(2)S(1) 0.0 Isotopic label 90 0.0 +ESP@K 338.177647 338.4682 H(26)C(16)N(4)O(2)S(1) 0.0 Isotopic label 90 0.0 +ESP:2H(10)@Any_N-term 348.240414 348.5299 H(16)2H(10)C(16)N(4)O(2)S(1) 0.0 Isotopic label 91 0.0 +ESP:2H(10)@K 348.240414 348.5299 H(16)2H(10)C(16)N(4)O(2)S(1) 0.0 Isotopic label 91 0.0 +NHS-LC-Biotin@Any_N-term 339.161662 339.453 H(25)C(16)N(3)O(3)S(1) 0.0 Chemical derivative 92 0.0 +NHS-LC-Biotin@K 339.161662 339.453 H(25)C(16)N(3)O(3)S(1) 0.0 Chemical derivative 92 0.0 +EDT-maleimide-PEO-biotin@T 601.206246 601.8021 H(39)C(25)N(5)O(6)S(3) 0.0 Chemical derivative 93 0.0 +EDT-maleimide-PEO-biotin@S 601.206246 601.8021 H(39)C(25)N(5)O(6)S(3) 0.0 Chemical derivative 93 0.0 +IMID@K 68.037448 68.0773 H(4)C(3)N(2) 0.0 Isotopic label 94 0.0 +IMID:2H(4)@K 72.062555 72.1019 2H(4)C(3)N(2) 0.0 Isotopic label 95 0.0 +Lysbiotinhydrazide@K 241.088497 241.31 H(15)C(10)N(3)O(2)S(1) 0.0 Chemical derivative 353 0.0 +Propionamide:2H(3)@C 74.055944 74.0964 H(2)2H(3)C(3)N(1)O(1) 0.0 Isotopic label 97 0.0 +Nitro@Y 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Chemical derivative 354 0.0 +Nitro@W 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Chemical derivative 354 0.0 +Nitro@F 44.985078 44.9976 H(-1)N(1)O(2) 0.0 Artefact 354 0.0 +ICAT-C@C 227.126991 227.2603 H(17)C(10)N(3)O(3) 0.0 Isotopic label 105 0.0 +Delta:H(2)C(2)@Protein_N-term 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 +Delta:H(2)C(2)@K 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 +Delta:H(2)C(2)@H 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 +Delta:H(2)C(2)@Any_N-term 26.01565 26.0373 H(2)C(2) 0.0 Other 254 0.0 +Trp->Kynurenin@W 3.994915 3.9887 C(-1)O(1) 0.0 Chemical derivative 351 0.0 +Lys->Allysine@K -1.031634 -1.0311 H(-3)N(-1)O(1) 0.0 Post-translational 352 0.0 +ICAT-C:13C(9)@C 236.157185 236.1942 H(17)C(1)13C(9)N(3)O(3) 0.0 Isotopic label 106 0.0 +FormylMet@Protein_N-term 159.035399 159.2062 H(9)C(6)N(1)O(2)S(1) 0.0 Pre-translational 107 0.0 +Nethylmaleimide@C 125.047679 125.1253 H(7)C(6)N(1)O(2) 0.0 Chemical derivative 108 0.0 +OxLysBiotinRed@K 354.172562 354.4676 H(26)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 112 0.0 +IBTP@C 316.138088 316.3759 H(21)C(22)P(1) 0.0 Chemical derivative 119 0.0 +OxLysBiotin@K 352.156911 352.4518 H(24)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 113 0.0 +OxProBiotinRed@P 371.199111 371.4982 H(29)C(16)N(5)O(3)S(1) 0.0 Chemical derivative 114 0.0 +OxProBiotin@P 369.183461 369.4823 H(27)C(16)N(5)O(3)S(1) 0.0 Chemical derivative 115 0.0 +OxArgBiotin@R 310.135113 310.4118 H(22)C(15)N(2)O(3)S(1) 0.0 Chemical derivative 116 0.0 +OxArgBiotinRed@R 312.150763 312.4277 H(24)C(15)N(2)O(3)S(1) 0.0 Chemical derivative 117 0.0 +EDT-iodoacetyl-PEO-biotin@T 490.174218 490.7034 H(34)C(20)N(4)O(4)S(3) 0.0 Chemical derivative 118 0.0 +EDT-iodoacetyl-PEO-biotin@S 490.174218 490.7034 H(34)C(20)N(4)O(4)S(3) 0.0 Chemical derivative 118 0.0 +GG@C 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 +GG@T 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 +GG@S 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 0.0 +GG@K 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Other 121 1000000.0 +GG@Protein_N-term 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Post-translational 121 0.0 +Formyl@Protein_N-term 27.994915 28.0101 C(1)O(1) 0.0 Post-translational 122 0.0 +Formyl@T 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 +Formyl@K 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 +Formyl@Any_N-term 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 +Formyl@S 27.994915 28.0101 C(1)O(1) 0.0 Artefact 122 0.0 +ICAT-H@C 345.097915 345.7754 H(20)C(15)N(1)O(6)Cl(1) 0.0 Isotopic label 123 0.0 +ICAT-H:13C(6)@C 351.118044 351.7313 H(20)C(9)13C(6)N(1)O(6)Cl(1) 0.0 Isotopic label 124 0.0 +Cation:K@Any_C-term 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 +Cation:K@E 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 +Cation:K@D 37.955882 38.0904 H(-1)K(1) 0.0 Artefact 530 0.0 +Xlink:DTSSP[88]@Protein_N-term 87.998285 88.1283 H(4)C(3)O(1)S(1) 0.0 Chemical derivative 126 0.0 +Xlink:DTSSP[88]@K 87.998285 88.1283 H(4)C(3)O(1)S(1) 0.0 Chemical derivative 126 0.0 +Xlink:EGS[226]@K 226.047738 226.1828 H(10)C(10)O(6) 0.0 Chemical derivative 1897 0.0 +Xlink:EGS[226]@Protein_N-term 226.047738 226.1828 H(10)C(10)O(6) 0.0 Chemical derivative 1897 0.0 +Fluoro@Y 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 +Fluoro@W 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 +Fluoro@F 17.990578 17.9905 H(-1)F(1) 0.0 Non-standard residue 127 0.0 +Fluoro@A 17.990578 17.9905 H(-1)F(1) 0.0 Chemical derivative 127 0.0 +Fluorescein@C 387.074287 387.3417 H(13)C(22)N(1)O(6) 0.0 Chemical derivative 128 0.0 +Iodo@H 125.896648 125.8965 H(-1)I(1) 0.0 Chemical derivative 129 0.0 +Iodo@Y 125.896648 125.8965 H(-1)I(1) 0.0 Chemical derivative 129 0.0 +Diiodo@Y 251.793296 251.7931 H(-2)I(2) 0.0 Chemical derivative 130 0.0 +Diiodo@H 251.793296 251.7931 H(-2)I(2) 0.0 Chemical derivative 130 0.0 +Triiodo@Y 377.689944 377.6896 H(-3)I(3) 0.0 Chemical derivative 131 0.0 +Myristoleyl@G^Protein_N-term 208.182715 208.3398 H(24)C(14)O(1) 0.0 Co-translational 134 0.0 +Pro->Pyrrolidinone@P -30.010565 -30.026 H(-2)C(-1)O(-1) 0.0 Chemical derivative 360 0.0 +Myristoyl+Delta:H(-4)@G^Protein_N-term 206.167065 206.3239 H(22)C(14)O(1) 0.0 Co-translational 135 0.0 +Benzoyl@Any_N-term 104.026215 104.1061 H(4)C(7)O(1) 0.0 Isotopic label 136 0.0 +Benzoyl@K 104.026215 104.1061 H(4)C(7)O(1) 0.0 Isotopic label 136 0.0 +Hex(5)HexNAc(2)@N 1216.422863 1217.088 H(76)C(46)N(2)O(35) 1216.422863 H(76)C(46)N(2)O(35) N-linked glycosylation 137 0.5 +Dansyl@Any_N-term 233.051049 233.2862 H(11)C(12)N(1)O(2)S(1) 0.0 Chemical derivative 139 0.0 +Dansyl@K 233.051049 233.2862 H(11)C(12)N(1)O(2)S(1) 0.0 Chemical derivative 139 0.0 +a-type-ion@Any_C-term -46.005479 -46.0254 H(-2)C(-1)O(-2) 0.0 Other 140 0.0 +Amidine@Any_N-term 41.026549 41.0519 H(3)C(2)N(1) 0.0 Chemical derivative 141 0.0 +Amidine@K 41.026549 41.0519 H(3)C(2)N(1) 0.0 Chemical derivative 141 0.0 +HexNAc(1)dHex(1)@T 349.137281 349.3337 H(23)C(14)N(1)O(9) 349.137281 H(23)C(14)N(1)O(9) O-linked glycosylation 142 0.5 +HexNAc(1)dHex(1)@S 349.137281 349.3337 H(23)C(14)N(1)O(9) 349.137281 H(23)C(14)N(1)O(9) O-linked glycosylation 142 0.5 +HexNAc(1)dHex(1)@N 349.137281 349.3337 H(23)C(14)N(1)O(9) 349.137281 H(23)C(14)N(1)O(9) N-linked glycosylation 142 0.5 +HexNAc(2)@T 406.158745 406.385 H(26)C(16)N(2)O(10) 406.158745 H(26)C(16)N(2)O(10) O-linked glycosylation 143 0.5 +HexNAc(2)@S 406.158745 406.385 H(26)C(16)N(2)O(10) 406.158745 H(26)C(16)N(2)O(10) O-linked glycosylation 143 0.5 +HexNAc(2)@N 406.158745 406.385 H(26)C(16)N(2)O(10) 406.158745 H(26)C(16)N(2)O(10) N-linked glycosylation 143 0.5 +Hex(3)@T 486.158471 486.4218 H(30)C(18)O(15) 486.158471 H(30)C(18)O(15) O-linked glycosylation 144 0.5 +Hex(3)@S 486.158471 486.4218 H(30)C(18)O(15) 486.158471 H(30)C(18)O(15) O-linked glycosylation 144 0.5 +Hex(3)@N 486.158471 486.4218 H(30)C(18)O(15) 486.158471 H(30)C(18)O(15) N-linked glycosylation 144 0.5 +HexNAc(1)dHex(2)@N 495.19519 495.4749 H(33)C(20)N(1)O(13) 495.19519 H(33)C(20)N(1)O(13) N-linked glycosylation 145 0.5 +Hex(1)HexNAc(1)dHex(1)@T 511.190105 511.4743 H(33)C(20)N(1)O(14) 511.190105 H(33)C(20)N(1)O(14) O-linked glycosylation 146 0.5 +Hex(1)HexNAc(1)dHex(1)@S 511.190105 511.4743 H(33)C(20)N(1)O(14) 511.190105 H(33)C(20)N(1)O(14) O-linked glycosylation 146 0.5 +Hex(1)HexNAc(1)dHex(1)@N 511.190105 511.4743 H(33)C(20)N(1)O(14) 511.190105 H(33)C(20)N(1)O(14) N-linked glycosylation 146 0.5 +HexNAc(2)dHex(1)@N 552.216654 552.5262 H(36)C(22)N(2)O(14) 552.216654 H(36)C(22)N(2)O(14) N-linked glycosylation 147 0.5 +Hex(1)HexNAc(2)@T 568.211569 568.5256 H(36)C(22)N(2)O(15) 568.211569 H(36)C(22)N(2)O(15) O-linked glycosylation 148 0.5 +Hex(1)HexNAc(2)@S 568.211569 568.5256 H(36)C(22)N(2)O(15) 568.211569 H(36)C(22)N(2)O(15) O-linked glycosylation 148 0.5 +Hex(1)HexNAc(2)@N 568.211569 568.5256 H(36)C(22)N(2)O(15) 568.211569 H(36)C(22)N(2)O(15) N-linked glycosylation 148 0.5 +Hex(1)HexNAc(1)NeuAc(1)@T 656.227613 656.5877 H(40)C(25)N(2)O(18) 656.227613 H(40)C(25)N(2)O(18) O-linked glycosylation 149 0.5 +Hex(1)HexNAc(1)NeuAc(1)@S 656.227613 656.5877 H(40)C(25)N(2)O(18) 656.227613 H(40)C(25)N(2)O(18) O-linked glycosylation 149 0.5 +Hex(1)HexNAc(1)NeuAc(1)@N 656.227613 656.5877 H(40)C(25)N(2)O(18) 656.227613 H(40)C(25)N(2)O(18) N-linked glycosylation 149 0.5 +HexNAc(2)dHex(2)@N 698.274563 698.6674 H(46)C(28)N(2)O(18) 698.274563 H(46)C(28)N(2)O(18) N-linked glycosylation 150 0.5 +Hex(1)HexNAc(2)Pent(1)@N 700.253828 700.6403 H(44)C(27)N(2)O(19) 700.253828 H(44)C(27)N(2)O(19) N-linked glycosylation 151 0.5 +Hex(1)HexNAc(2)dHex(1)@T 714.269478 714.6668 H(46)C(28)N(2)O(19) 714.269478 H(46)C(28)N(2)O(19) O-linked glycosylation 152 0.5 +Hex(1)HexNAc(2)dHex(1)@S 714.269478 714.6668 H(46)C(28)N(2)O(19) 714.269478 H(46)C(28)N(2)O(19) O-linked glycosylation 152 0.5 +Hex(1)HexNAc(2)dHex(1)@N 714.269478 714.6668 H(46)C(28)N(2)O(19) 714.269478 H(46)C(28)N(2)O(19) N-linked glycosylation 152 0.5 +Hex(2)HexNAc(2)@T 730.264392 730.6662 H(46)C(28)N(2)O(20) 730.264392 H(46)C(28)N(2)O(20) O-linked glycosylation 153 0.5 +Hex(2)HexNAc(2)@S 730.264392 730.6662 H(46)C(28)N(2)O(20) 730.264392 H(46)C(28)N(2)O(20) O-linked glycosylation 153 0.5 +Hex(2)HexNAc(2)@N 730.264392 730.6662 H(46)C(28)N(2)O(20) 730.264392 H(46)C(28)N(2)O(20) N-linked glycosylation 153 0.5 +Hex(3)HexNAc(1)Pent(1)@N 821.280102 821.7289 H(51)C(31)N(1)O(24) 821.280102 H(51)C(31)N(1)O(24) N-linked glycosylation 154 0.5 +Hex(1)HexNAc(2)dHex(1)Pent(1)@N 846.311736 846.7815 H(54)C(33)N(2)O(23) 846.311736 H(54)C(33)N(2)O(23) N-linked glycosylation 155 0.5 +Hex(1)HexNAc(2)dHex(2)@T 860.327386 860.808 H(56)C(34)N(2)O(23) 860.327386 H(56)C(34)N(2)O(23) O-linked glycosylation 156 0.5 +Hex(1)HexNAc(2)dHex(2)@S 860.327386 860.808 H(56)C(34)N(2)O(23) 860.327386 H(56)C(34)N(2)O(23) O-linked glycosylation 156 0.5 +Hex(1)HexNAc(2)dHex(2)@N 860.327386 860.808 H(56)C(34)N(2)O(23) 860.327386 H(56)C(34)N(2)O(23) N-linked glycosylation 156 0.5 +Hex(2)HexNAc(2)Pent(1)@N 862.306651 862.7809 H(54)C(33)N(2)O(24) 862.306651 H(54)C(33)N(2)O(24) N-linked glycosylation 157 0.5 +Hex(2)HexNAc(2)dHex(1)@T 876.322301 876.8074 H(56)C(34)N(2)O(24) 876.322301 H(56)C(34)N(2)O(24) O-linked glycosylation 158 0.5 +Hex(2)HexNAc(2)dHex(1)@S 876.322301 876.8074 H(56)C(34)N(2)O(24) 876.322301 H(56)C(34)N(2)O(24) O-linked glycosylation 158 0.5 +Hex(2)HexNAc(2)dHex(1)@N 876.322301 876.8074 H(56)C(34)N(2)O(24) 876.322301 H(56)C(34)N(2)O(24) N-linked glycosylation 158 0.5 +Hex(3)HexNAc(2)@T 892.317216 892.8068 H(56)C(34)N(2)O(25) 892.317216 H(56)C(34)N(2)O(25) O-linked glycosylation 159 0.5 +Hex(3)HexNAc(2)@S 892.317216 892.8068 H(56)C(34)N(2)O(25) 892.317216 H(56)C(34)N(2)O(25) O-linked glycosylation 159 0.5 +Hex(3)HexNAc(2)@N 892.317216 892.8068 H(56)C(34)N(2)O(25) 892.317216 H(56)C(34)N(2)O(25) N-linked glycosylation 159 0.5 +Hex(1)HexNAc(1)NeuAc(2)@T 947.323029 947.8423 H(57)C(36)N(3)O(26) 947.323029 H(57)C(36)N(3)O(26) O-linked glycosylation 160 0.5 +Hex(1)HexNAc(1)NeuAc(2)@S 947.323029 947.8423 H(57)C(36)N(3)O(26) 947.323029 H(57)C(36)N(3)O(26) O-linked glycosylation 160 0.5 +Hex(1)HexNAc(1)NeuAc(2)@N 947.323029 947.8423 H(57)C(36)N(3)O(26) 947.323029 H(57)C(36)N(3)O(26) N-linked glycosylation 160 0.5 +Hex(3)HexNAc(2)Phos(1)@N 972.283547 972.7867 H(57)C(34)N(2)O(28)P(1) 972.283547 H(57)C(34)N(2)O(28)P(1) N-linked glycosylation 161 0.5 +Delta:S(-1)Se(1)@M 47.944449 46.895 S(-1)Se(1) 0.0 Non-standard residue 162 0.0 +Delta:S(-1)Se(1)@C 47.944449 46.895 S(-1)Se(1) 0.0 Non-standard residue 162 0.0 +NBS:13C(6)@W 159.008578 159.1144 H(3)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 171 0.0 +Methyl:2H(3)13C(1)@K 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 +Methyl:2H(3)13C(1)@R 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 +Methyl:2H(3)13C(1)@Any_N-term 18.037835 18.0377 H(-1)2H(3)13C(1) 0.0 Isotopic label 329 0.0 +Dimethyl:2H(6)13C(2)@Protein_N-term 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 +Dimethyl:2H(6)13C(2)@Any_N-term 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 +Dimethyl:2H(6)13C(2)@R 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 +Dimethyl:2H(6)13C(2)@K 36.07567 36.0754 H(-2)2H(6)13C(2) 0.0 Isotopic label 330 0.0 +NBS@W 152.988449 153.1585 H(3)C(6)N(1)O(2)S(1) 0.0 Chemical derivative 172 0.0 +Delta:H(-1)N(-1)18O(1)@N 2.988261 2.9845 H(-1)N(-1)18O(1) 0.0 Isotopic label 170 0.0 +QAT@C 171.149738 171.26 H(19)C(9)N(2)O(1) 0.0 Chemical derivative 195 0.0 +BHT@H 218.167065 218.3346 H(22)C(15)O(1) 0.0 Other 176 0.0 +BHT@K 218.167065 218.3346 H(22)C(15)O(1) 0.0 Other 176 0.0 +BHT@C 218.167065 218.3346 H(22)C(15)O(1) 0.0 Other 176 0.0 +Delta:H(4)C(2)O(-1)S(1)@S 44.008456 44.1188 H(4)C(2)O(-1)S(1) 0.0 Chemical derivative 327 0.0 +DAET@T 87.050655 87.1866 H(9)C(4)N(1)O(-1)S(1) 0.0 Chemical derivative 178 0.0 +DAET@S 87.050655 87.1866 H(9)C(4)N(1)O(-1)S(1) 0.0 Chemical derivative 178 0.0 +Pro->Pyrrolidone@P -27.994915 -28.0101 C(-1)O(-1) 0.0 Chemical derivative 369 0.0 +Label:13C(9)@Y 9.030193 8.9339 C(-9)13C(9) 0.0 Isotopic label 184 0.0 +Label:13C(9)@F 9.030193 8.9339 C(-9)13C(9) 0.0 Isotopic label 184 0.0 +Label:13C(9)+Phospho@Y 88.996524 88.9138 H(1)C(-9)13C(9)O(3)P(1) 0.0 Isotopic label 185 0.0 +Label:13C(6)@I 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 +Label:13C(6)@L 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 +Label:13C(6)@K 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 +Label:13C(6)@R 6.020129 5.9559 C(-6)13C(6) 0.0 Isotopic label 188 0.0 +HPG@R 132.021129 132.1162 H(4)C(8)O(2) 0.0 Chemical derivative 186 0.0 +2HPG@R 282.052824 282.2476 H(10)C(16)O(5) 0.0 Chemical derivative 187 0.0 +QAT:2H(3)@C 174.168569 174.2784 H(16)2H(3)C(9)N(2)O(1) 0.0 Isotopic label 196 0.0 +Label:18O(2)@Any_C-term 4.008491 3.9995 O(-2)18O(2) 0.0 Isotopic label 193 0.0 +AccQTag@Any_N-term 170.048013 170.1674 H(6)C(10)N(2)O(1) 0.0 Chemical derivative 194 0.0 +AccQTag@K 170.048013 170.1674 H(6)C(10)N(2)O(1) 0.0 Chemical derivative 194 0.0 +Dimethyl:2H(4)@Protein_N-term 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 +Dimethyl:2H(4)@Any_N-term 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 +Dimethyl:2H(4)@K 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 +Dimethyl:2H(4)@R 32.056407 32.0778 2H(4)C(2) 0.0 Isotopic label 199 0.0 +EQAT@C 184.157563 184.2786 H(20)C(10)N(2)O(1) 0.0 Chemical derivative 197 0.0 +EQAT:2H(5)@C 189.188947 189.3094 H(15)2H(5)C(10)N(2)O(1) 0.0 Isotopic label 198 0.0 +Ethanedithiol@T 75.980527 76.1838 H(4)C(2)O(-1)S(2) 0.0 Chemical derivative 200 0.0 +Ethanedithiol@S 75.980527 76.1838 H(4)C(2)O(-1)S(2) 0.0 Chemical derivative 200 0.0 +NEIAA:2H(5)@Y 90.084148 90.1353 H(2)2H(5)C(4)N(1)O(1) 0.0 Isotopic label 212 0.0 +NEIAA:2H(5)@C 90.084148 90.1353 H(2)2H(5)C(4)N(1)O(1) 0.0 Isotopic label 212 0.0 +Delta:H(6)C(6)O(1)@K 94.041865 94.1112 H(6)C(6)O(1) 0.0 Other 205 0.0 +Delta:H(4)C(3)O(1)@K 56.026215 56.0633 H(4)C(3)O(1) 0.0 Other 206 0.0 +Delta:H(4)C(3)O(1)@H 56.026215 56.0633 H(4)C(3)O(1) 0.0 Other 206 0.0 +Delta:H(4)C(3)O(1)@C 56.026215 56.0633 H(4)C(3)O(1) 0.0 Other 206 0.0 +Delta:H(4)C(3)O(1)@R 56.026215 56.0633 H(4)C(3)O(1) 0.0 Artefact 206 0.0 +Delta:H(2)C(3)@K 38.01565 38.048 H(2)C(3) 0.0 Other 207 0.0 +Delta:H(4)C(6)@K 76.0313 76.096 H(4)C(6) 0.0 Other 208 0.0 +Delta:H(8)C(6)O(2)@K 112.05243 112.1265 H(8)C(6)O(2) 0.0 Other 209 0.0 +ADP-Ribosyl@D 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 +ADP-Ribosyl@K 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 +ADP-Ribosyl@E 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 +ADP-Ribosyl@T 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 541.06111 H(21)C(15)N(5)O(13)P(2) O-linked glycosylation 213 0.5 +ADP-Ribosyl@S 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 541.06111 H(21)C(15)N(5)O(13)P(2) O-linked glycosylation 213 0.5 +ADP-Ribosyl@C 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 +ADP-Ribosyl@N 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 541.06111 H(21)C(15)N(5)O(13)P(2) N-linked glycosylation 213 0.5 +ADP-Ribosyl@R 541.06111 541.3005 H(21)C(15)N(5)O(13)P(2) 0.0 Other glycosylation 213 0.0 +NEIAA@Y 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Isotopic label 211 0.0 +NEIAA@C 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Isotopic label 211 0.0 +iTRAQ4plex@C 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@T 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@Protein_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@S 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@H 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@Y 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@Any_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +iTRAQ4plex@K 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 214 0.0 +Crotonaldehyde@K 70.041865 70.0898 H(6)C(4)O(1) 0.0 Other 253 0.0 +Crotonaldehyde@H 70.041865 70.0898 H(6)C(4)O(1) 0.0 Other 253 0.0 +Crotonaldehyde@C 70.041865 70.0898 H(6)C(4)O(1) 0.0 Other 253 0.0 +Bromo@F 77.910511 78.8961 H(-1)Br(1) 0.0 Post-translational 340 0.0 +Bromo@H 77.910511 78.8961 H(-1)Br(1) 0.0 Post-translational 340 0.0 +Bromo@W 77.910511 78.8961 H(-1)Br(1) 0.0 Post-translational 340 0.0 +Bromo@Y 77.910511 78.8961 H(-1)Br(1) 0.0 Artefact 340 0.0 +Amino@Y 15.010899 15.0146 H(1)N(1) 0.0 Chemical derivative 342 0.0 +Argbiotinhydrazide@R 199.066699 199.27 H(13)C(9)N(1)O(2)S(1) 0.0 Chemical derivative 343 0.0 +Label:18O(1)@Y 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 +Label:18O(1)@T 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 +Label:18O(1)@S 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 +Label:18O(1)@Any_C-term 2.004246 1.9998 O(-1)18O(1) 0.0 Isotopic label 258 0.0 +Label:13C(6)15N(2)@K 8.014199 7.9427 C(-6)13C(6)N(-2)15N(2) 0.0 Isotopic label 259 0.0 +Thiophospho@Y 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 +Thiophospho@T 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 +Thiophospho@S 95.943487 96.0455 H(1)O(2)P(1)S(1) 0.0 Other 260 0.0 +SPITC@K 214.971084 215.2495 H(5)C(7)N(1)O(3)S(2) 0.0 Chemical derivative 261 0.0 +SPITC@Any_N-term 214.971084 215.2495 H(5)C(7)N(1)O(3)S(2) 0.0 Chemical derivative 261 0.0 +IGBP@C 296.016039 297.1478 H(13)C(12)N(2)O(2)Br(1) 0.0 Isotopic label 243 0.0 +Cytopiloyne@Y 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 +Cytopiloyne@S 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 +Cytopiloyne@R 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 +Cytopiloyne@P 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 +Cytopiloyne@Any_N-term 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 +Cytopiloyne@K 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 +Cytopiloyne@C 362.136553 362.3738 H(22)C(19)O(7) 0.0 Chemical derivative 270 0.0 +Cytopiloyne+water@Y 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 +Cytopiloyne+water@T 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 +Cytopiloyne+water@S 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 +Cytopiloyne+water@R 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 +Cytopiloyne+water@Any_N-term 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 +Cytopiloyne+water@K 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 +Cytopiloyne+water@C 380.147118 380.3891 H(24)C(19)O(8) 0.0 Chemical derivative 271 0.0 +Label:13C(6)15N(4)@R 10.008269 9.9296 C(-6)13C(6)N(-4)15N(4) 0.0 Isotopic label 267 0.0 +Label:13C(9)15N(1)@F 10.027228 9.9273 C(-9)13C(9)N(-1)15N(1) 0.0 Isotopic label 269 0.0 +Label:2H(3)@L 3.01883 3.0185 H(-3)2H(3) 0.0 Isotopic label 262 0.0 +Label:2H(3)@M 3.01883 3.0185 H(-3)2H(3) 0.0 Isotopic label 262 0.0 +Label:13C(5)15N(1)@M 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 +Label:13C(5)15N(1)@P 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 +Label:13C(5)15N(1)@V 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 +Label:13C(5)15N(1)@E 6.013809 5.9567 C(-5)13C(5)N(-1)15N(1) 0.0 Isotopic label 268 0.0 +PET@T 121.035005 121.2028 H(7)C(7)N(1)O(-1)S(1) 0.0 Chemical derivative 264 0.0 +PET@S 121.035005 121.2028 H(7)C(7)N(1)O(-1)S(1) 0.0 Chemical derivative 264 0.0 +CAF@Any_N-term 135.983029 136.1265 H(4)C(3)O(4)S(1) 0.0 Chemical derivative 272 0.0 +Xlink:BS2G[96]@Protein_N-term 96.021129 96.0841 H(4)C(5)O(2) 0.0 Chemical derivative 1905 0.0 +Xlink:BS2G[96]@K 96.021129 96.0841 H(4)C(5)O(2) 0.0 Chemical derivative 1905 0.0 +Nitrosyl@C 28.990164 28.9982 H(-1)N(1)O(1) 0.0 Post-translational 275 0.0 +Nitrosyl@Y 28.990164 28.9982 H(-1)N(1)O(1) 0.0 Chemical derivative 275 0.0 +Ser/Thr-KDO@T 220.058303 220.1767 H(12)C(8)O(7) 220.058303 H(12)C(8)O(7) O-linked glycosylation 2022 0.5 +Ser/Thr-KDO@S 220.058303 220.1767 H(12)C(8)O(7) 220.058303 H(12)C(8)O(7) O-linked glycosylation 2022 0.5 +AEBS@Y 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 +AEBS@S 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 +AEBS@Protein_N-term 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 +AEBS@K 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 +AEBS@H 183.035399 183.2276 H(9)C(8)N(1)O(2)S(1) 0.0 Artefact 276 0.0 +Ethanolyl@K 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 +Ethanolyl@C 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 +Ethanolyl@R 44.026215 44.0526 H(4)C(2)O(1) 0.0 Chemical derivative 278 0.0 +Label:13C(6)15N(2)+Dimethyl@K 36.045499 35.9959 H(4)C(-4)13C(6)N(-2)15N(2) 0.0 Isotopic label 987 0.0 +HMVK@C 86.036779 86.0892 H(6)C(4)O(2) 0.0 Chemical derivative 371 0.0 +Ethyl@Any_C-term 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 +Ethyl@Protein_N-term 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 +Ethyl@E 28.0313 28.0532 H(4)C(2) 0.0 Artefact 280 0.0 +Ethyl@Any_N-term 28.0313 28.0532 H(4)C(2) 0.0 Multiple 280 0.0 +Ethyl@K 28.0313 28.0532 H(4)C(2) 0.0 Multiple 280 0.0 +Ethyl@D 28.0313 28.0532 H(4)C(2) 0.0 Chemical derivative 280 0.0 +CoenzymeA@C 765.09956 765.5182 H(34)C(21)N(7)O(16)P(3)S(1) 0.0 Post-translational 281 0.0 +Methyl+Deamidated@Q 14.999666 15.0113 H(1)C(1)N(-1)O(1) 0.0 Post-translational 528 0.0 +Methyl+Deamidated@N 14.999666 15.0113 H(1)C(1)N(-1)O(1) 0.0 Chemical derivative 528 0.0 +Delta:H(5)C(2)@P 29.039125 29.0611 H(5)C(2) 0.0 Post-translational 529 0.0 +Methyl:2H(2)@K 16.028204 16.0389 2H(2)C(1) 0.0 Isotopic label 284 0.0 +Methyl:2H(2)@Any_N-term 16.028204 16.0389 2H(2)C(1) 0.0 Isotopic label 284 0.0 +SulfanilicAcid@E 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 +SulfanilicAcid@D 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 +SulfanilicAcid@Any_C-term 155.004099 155.1744 H(5)C(6)N(1)O(2)S(1) 0.0 Isotopic label 285 0.0 +SulfanilicAcid:13C(6)@E 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 +SulfanilicAcid:13C(6)@D 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 +SulfanilicAcid:13C(6)@Any_C-term 161.024228 161.1303 H(5)13C(6)N(1)O(2)S(1) 0.0 Chemical derivative 286 0.0 +Biotin-PEO-Amine@D 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 +Biotin-PEO-Amine@Protein_C-term 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 +Biotin-PEO-Amine@E 356.188212 356.4835 H(28)C(16)N(4)O(3)S(1) 0.0 Chemical derivative 289 0.0 +Trp->Oxolactone@W 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 288 0.0 +Biotin-HPDP@C 428.191582 428.6124 H(32)C(19)N(4)O(3)S(2) 0.0 Chemical derivative 290 0.0 +Delta:Hg(1)@C 201.970617 200.59 Hg(1) 0.0 Chemical derivative 291 0.0 +IodoU-AMP@Y 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 +IodoU-AMP@W 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 +IodoU-AMP@F 322.020217 322.1654 H(11)C(9)N(2)O(9)P(1) 0.0 Chemical derivative 292 0.0 +CAMthiopropanoyl@Protein_N-term 145.019749 145.1796 H(7)C(5)N(1)O(2)S(1) 0.0 Chemical derivative 293 0.0 +CAMthiopropanoyl@K 145.019749 145.1796 H(7)C(5)N(1)O(2)S(1) 0.0 Chemical derivative 293 0.0 +IED-Biotin@C 326.141261 326.4145 H(22)C(14)N(4)O(3)S(1) 0.0 Chemical derivative 294 0.0 +dHex@N 146.057909 146.1412 H(10)C(6)O(4) 146.057909 H(10)C(6)O(4) N-linked glycosylation 295 0.5 +dHex@T 146.057909 146.1412 H(10)C(6)O(4) 146.057909 H(10)C(6)O(4) O-linked glycosylation 295 0.5 +dHex@S 146.057909 146.1412 H(10)C(6)O(4) 146.057909 H(10)C(6)O(4) O-linked glycosylation 295 0.5 +Methyl:2H(3)@Anywhere 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 +Methyl:2H(3)@D 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 +Methyl:2H(3)@E 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 +Methyl:2H(3)@K 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 +Methyl:2H(3)@R 17.03448 17.0451 H(-1)2H(3)C(1) 0.0 Isotopic label 298 0.0 +Carboxy@E 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 +Carboxy@D 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 +Carboxy@K 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 +Carboxy@W 43.989829 44.0095 C(1)O(2) 0.0 Chemical derivative 299 0.0 +Carboxy@M^Protein_N-term 43.989829 44.0095 C(1)O(2) 0.0 Post-translational 299 0.0 +Bromobimane@C 190.074228 190.1986 H(10)C(10)N(2)O(2) 0.0 Chemical derivative 301 0.0 +Menadione@K 170.036779 170.1641 H(6)C(11)O(2) 0.0 Chemical derivative 302 0.0 +Menadione@C 170.036779 170.1641 H(6)C(11)O(2) 0.0 Chemical derivative 302 0.0 +DeStreak@C 75.998285 76.1176 H(4)C(2)O(1)S(1) 0.0 Chemical derivative 303 0.0 +dHex(1)Hex(3)HexNAc(4)@T 1444.53387 1445.3331 H(92)C(56)N(4)O(39) 1444.53387 H(92)C(56)N(4)O(39) O-linked glycosylation 305 0.5 +dHex(1)Hex(3)HexNAc(4)@S 1444.53387 1445.3331 H(92)C(56)N(4)O(39) 1444.53387 H(92)C(56)N(4)O(39) O-linked glycosylation 305 0.5 +dHex(1)Hex(3)HexNAc(4)@N 1444.53387 1445.3331 H(92)C(56)N(4)O(39) 1444.53387 H(92)C(56)N(4)O(39) N-linked glycosylation 305 0.5 +dHex(1)Hex(4)HexNAc(4)@T 1606.586693 1607.4737 H(102)C(62)N(4)O(44) 1606.586693 H(102)C(62)N(4)O(44) O-linked glycosylation 307 0.5 +dHex(1)Hex(4)HexNAc(4)@S 1606.586693 1607.4737 H(102)C(62)N(4)O(44) 1606.586693 H(102)C(62)N(4)O(44) O-linked glycosylation 307 0.5 +dHex(1)Hex(4)HexNAc(4)@N 1606.586693 1607.4737 H(102)C(62)N(4)O(44) 1606.586693 H(102)C(62)N(4)O(44) N-linked glycosylation 307 0.5 +Pro+O(2)@H 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Post-translational 2035 0.0 +dHex(1)Hex(5)HexNAc(4)@N 1768.639517 1769.6143 H(112)C(68)N(4)O(49) 1768.639517 H(112)C(68)N(4)O(49) N-linked glycosylation 308 0.5 +Hex(3)HexNAc(4)@T 1298.475961 1299.1919 H(82)C(50)N(4)O(35) 1298.475961 H(82)C(50)N(4)O(35) O-linked glycosylation 309 0.5 +Hex(3)HexNAc(4)@S 1298.475961 1299.1919 H(82)C(50)N(4)O(35) 1298.475961 H(82)C(50)N(4)O(35) O-linked glycosylation 309 0.5 +Hex(3)HexNAc(4)@N 1298.475961 1299.1919 H(82)C(50)N(4)O(35) 1298.475961 H(82)C(50)N(4)O(35) N-linked glycosylation 309 0.5 +Hex(4)HexNAc(4)@T 1460.528784 1461.3325 H(92)C(56)N(4)O(40) 1460.528784 H(92)C(56)N(4)O(40) O-linked glycosylation 310 0.5 +Hex(4)HexNAc(4)@S 1460.528784 1461.3325 H(92)C(56)N(4)O(40) 1460.528784 H(92)C(56)N(4)O(40) O-linked glycosylation 310 0.5 +Hex(4)HexNAc(4)@N 1460.528784 1461.3325 H(92)C(56)N(4)O(40) 1460.528784 H(92)C(56)N(4)O(40) N-linked glycosylation 310 0.5 +Hex(5)HexNAc(4)@T 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C(62)N(4)O(45) O-linked glycosylation 311 0.5 +Hex(5)HexNAc(4)@S 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C(62)N(4)O(45) O-linked glycosylation 311 0.5 +Hex(5)HexNAc(4)@N 1622.581608 1623.4731 H(102)C(62)N(4)O(45) 1622.581608 H(102)C(62)N(4)O(45) N-linked glycosylation 311 0.5 +Cysteinyl@C 119.004099 119.1423 H(5)C(3)N(1)O(2)S(1) 0.0 Multiple 312 0.0 +Lys-loss@K -128.094963 -128.1723 H(-12)C(-6)N(-2)O(-1) 0.0 Artefact 313 0.0 +Lys-loss@K^Protein_C-term -128.094963 -128.1723 H(-12)C(-6)N(-2)O(-1) 0.0 Post-translational 313 0.0 +Nmethylmaleimide@K 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 314 0.0 +Nmethylmaleimide@C 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 314 0.0 +CyDye-Cy3@C 672.298156 672.8335 H(44)C(37)N(4)O(6)S(1) 0.0 Chemical derivative 494 0.0 +DimethylpyrroleAdduct@K 78.04695 78.1118 H(6)C(6) 0.0 Chemical derivative 316 0.0 +Delta:H(2)C(5)@K 62.01565 62.0694 H(2)C(5) 0.0 Chemical derivative 318 0.0 +Delta:H(2)C(3)O(1)@K 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 319 0.0 +Delta:H(2)C(3)O(1)@R 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 319 0.0 +Nethylmaleimide+water@K 143.058243 143.1406 H(9)C(6)N(1)O(3) 0.0 Chemical derivative 320 0.0 +Nethylmaleimide+water@C 143.058243 143.1406 H(9)C(6)N(1)O(3) 0.0 Chemical derivative 320 0.0 +Methyl+Acetyl:2H(3)@K 59.045045 59.0817 H(1)2H(3)C(3)O(1) 0.0 Isotopic label 768 0.0 +Xlink:B10621@C 713.093079 713.5626 H(30)C(31)N(4)O(6)S(1)I(1) 0.0 Chemical derivative 323 0.0 +Xlink:DTBP[87]@Protein_N-term 87.01427 87.1435 H(5)C(3)N(1)S(1) 0.0 Chemical derivative 324 0.0 +Xlink:DTBP[87]@K 87.01427 87.1435 H(5)C(3)N(1)S(1) 0.0 Chemical derivative 324 0.0 +FP-Biotin@K 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 +FP-Biotin@T 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 +FP-Biotin@Y 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 +FP-Biotin@S 572.316129 572.7405 H(49)C(27)N(4)O(5)P(1)S(1) 0.0 Chemical derivative 325 0.0 +Thiophos-S-S-biotin@Y 525.142894 525.6658 H(34)C(19)N(4)O(5)P(1)S(3) 525.142894 H(34)C(19)N(4)O(5)P(1)S(3) Chemical derivative 332 0.5 +Thiophos-S-S-biotin@T 525.142894 525.6658 H(34)C(19)N(4)O(5)P(1)S(3) 525.142894 H(34)C(19)N(4)O(5)P(1)S(3) Chemical derivative 332 0.5 +Thiophos-S-S-biotin@S 525.142894 525.6658 H(34)C(19)N(4)O(5)P(1)S(3) 525.142894 H(34)C(19)N(4)O(5)P(1)S(3) Chemical derivative 332 0.5 +Can-FP-biotin@T 447.195679 447.5291 H(34)C(19)N(3)O(5)P(1)S(1) 0.0 Chemical derivative 333 0.0 +Can-FP-biotin@Y 447.195679 447.5291 H(34)C(19)N(3)O(5)P(1)S(1) 0.0 Chemical derivative 333 0.0 +Can-FP-biotin@S 447.195679 447.5291 H(34)C(19)N(3)O(5)P(1)S(1) 0.0 Chemical derivative 333 0.0 +HNE+Delta:H(2)@K 158.13068 158.238 H(18)C(9)O(2) 0.0 Chemical derivative 335 0.0 +HNE+Delta:H(2)@H 158.13068 158.238 H(18)C(9)O(2) 0.0 Chemical derivative 335 0.0 +HNE+Delta:H(2)@C 158.13068 158.238 H(18)C(9)O(2) 0.0 Chemical derivative 335 0.0 +Thrbiotinhydrazide@T 240.104482 240.3252 H(16)C(10)N(4)O(1)S(1) 0.0 Chemical derivative 361 0.0 +Methylamine@T 13.031634 13.0418 H(3)C(1)N(1)O(-1) 0.0 Artefact 337 0.0 +Methylamine@S 13.031634 13.0418 H(3)C(1)N(1)O(-1) 0.0 Artefact 337 0.0 +Diisopropylphosphate@K 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 +Diisopropylphosphate@Y 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 +Diisopropylphosphate@T 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 +Diisopropylphosphate@S 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 +Diisopropylphosphate@Any_N-term 164.060231 164.1394 H(13)C(6)O(3)P(1) 0.0 Chemical derivative 362 0.0 +Isopropylphospho@Y 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 +Isopropylphospho@T 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 +Isopropylphospho@S 122.013281 122.0596 H(7)C(3)O(3)P(1) 0.0 Chemical derivative 363 0.0 +ICPL:13C(6)@Any_N-term 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 +ICPL:13C(6)@Protein_N-term 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 +ICPL:13C(6)@K 111.041593 111.05 H(3)13C(6)N(1)O(1) 0.0 Isotopic label 364 0.0 +CarbamidomethylDTT@C 209.018035 209.2864 H(11)C(6)N(1)O(3)S(2) 0.0 Artefact 893 0.0 +ICPL@Protein_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 +ICPL@K 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 +ICPL@Any_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 365 0.0 +Deamidated:18O(1)@Q 2.988261 2.9845 H(-1)N(-1)18O(1) 0.0 Isotopic label 366 0.0 +Deamidated:18O(1)@N 2.988261 2.9845 H(-1)N(-1)18O(1) 0.0 Isotopic label 366 0.0 +Arg->Orn@R -42.021798 -42.04 H(-2)C(-1)N(-2) 0.0 Artefact 372 0.0 +Cation:Cu[I]@Any_C-term 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 +Cation:Cu[I]@E 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 +Cation:Cu[I]@D 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 +Cation:Cu[I]@H 61.921774 62.5381 H(-1)Cu(1) 0.0 Artefact 531 0.0 +Dehydro@C -1.007825 -1.0079 H(-1) 0.0 Multiple 374 0.0 +Diphthamide@H 142.110613 142.1989 H(14)C(7)N(2)O(1) 0.0 Post-translational 375 0.0 +Hydroxyfarnesyl@C 220.182715 220.3505 H(24)C(15)O(1) 0.0 Post-translational 376 0.0 +Diacylglycerol@C 576.511761 576.9334 H(68)C(37)O(4) 0.0 Post-translational 377 0.0 +Carboxyethyl@H 72.021129 72.0627 H(4)C(3)O(2) 0.0 Chemical derivative 378 0.0 +Carboxyethyl@K 72.021129 72.0627 H(4)C(3)O(2) 0.0 Post-translational 378 0.0 +Hypusine@K 87.068414 87.1204 H(9)C(4)N(1)O(1) 0.0 Post-translational 379 0.0 +Retinylidene@K 266.203451 266.4204 H(26)C(20) 0.0 Post-translational 380 0.0 +Lys->AminoadipicAcid@K 14.96328 14.9683 H(-3)N(-1)O(2) 0.0 Post-translational 381 0.0 +Cys->PyruvicAcid@C^Protein_N-term -33.003705 -33.0961 H(-3)N(-1)O(1)S(-1) 0.0 Post-translational 382 0.0 +Ammonia-loss@C^Any_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Artefact 385 0.0 +Ammonia-loss@S^Protein_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Post-translational 385 0.0 +Ammonia-loss@T^Protein_N-term -17.026549 -17.0305 H(-3)N(-1) 0.0 Post-translational 385 0.0 +Ammonia-loss@N -17.026549 -17.0305 H(-3)N(-1) 0.0 Chemical derivative 385 0.0 +Phycocyanobilin@C 586.279135 586.678 H(38)C(33)N(4)O(6) 0.0 Post-translational 387 0.0 +Phycoerythrobilin@C 588.294785 588.6939 H(40)C(33)N(4)O(6) 0.0 Post-translational 388 0.0 +Phytochromobilin@C 584.263485 584.6621 H(36)C(33)N(4)O(6) 0.0 Post-translational 389 0.0 +Heme@H 616.177295 616.4873 H(32)C(34)N(4)O(4)Fe(1) 0.0 Post-translational 390 0.0 +Heme@C 616.177295 616.4873 H(32)C(34)N(4)O(4)Fe(1) 0.0 Post-translational 390 0.0 +Molybdopterin@C 521.884073 520.2668 H(11)C(10)N(5)O(8)P(1)S(2)Mo(1) 0.0 Post-translational 391 0.0 +Quinone@W 29.974179 29.9829 H(-2)O(2) 0.0 Post-translational 392 0.0 +Quinone@Y 29.974179 29.9829 H(-2)O(2) 0.0 Post-translational 392 0.0 +Glucosylgalactosyl@K 340.100562 340.2806 H(20)C(12)O(11) 340.100562 H(20)C(12)O(11) Other glycosylation 393 0.5 +GPIanchor@Protein_C-term 123.00853 123.0477 H(6)C(2)N(1)O(3)P(1) 0.0 Post-translational 394 0.0 +PhosphoribosyldephosphoCoA@S 881.146904 881.6335 H(42)C(26)N(7)O(19)P(3)S(1) 0.0 Post-translational 395 0.0 +GlycerylPE@E 197.04531 197.1262 H(12)C(5)N(1)O(5)P(1) 0.0 Post-translational 396 0.0 +Triiodothyronine@Y 469.716159 469.785 H(1)C(6)O(1)I(3) 0.0 Post-translational 397 0.0 +Thyroxine@Y 595.612807 595.6815 C(6)O(1)I(4) 0.0 Post-translational 398 0.0 +Tyr->Dha@Y -94.041865 -94.1112 H(-6)C(-6)O(-1) 0.0 Post-translational 400 0.0 +Didehydro@S -2.01565 -2.0159 H(-2) 0.0 Post-translational 401 0.0 +Didehydro@Y -2.01565 -2.0159 H(-2) 0.0 Post-translational 401 0.0 +Didehydro@T -2.01565 -2.0159 H(-2) 0.0 Chemical derivative 401 0.0 +Didehydro@K^Any_C-term -2.01565 -2.0159 H(-2) 0.0 Artefact 401 0.0 +Cys->Oxoalanine@C -17.992806 -18.0815 H(-2)O(1)S(-1) 0.0 Post-translational 402 0.0 +Ser->LacticAcid@S^Protein_N-term -15.010899 -15.0146 H(-1)N(-1) 0.0 Post-translational 403 0.0 +GluGlu@E 258.085186 258.228 H(14)C(10)N(2)O(6) 0.0 Post-translational 451 0.0 +GluGlu@Protein_C-term 258.085186 258.228 H(14)C(10)N(2)O(6) 0.0 Post-translational 451 0.0 +Phosphoadenosine@S 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 347.063085 H(14)C(10)N(5)O(7)P(1) Post-translational 405 0.5 +Phosphoadenosine@H 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 +Phosphoadenosine@T 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 347.063085 H(14)C(10)N(5)O(7)P(1) Post-translational 405 0.5 +Phosphoadenosine@Y 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 135.054495 H(5)C(5)N(5) Post-translational 405 0.5 +Phosphoadenosine@K 329.05252 329.2059 H(12)C(10)N(5)O(6)P(1) 0.0 Post-translational 405 0.0 +Glu@E 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Post-translational 450 0.0 +Glu@Protein_C-term 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Chemical derivative 450 0.0 +Hydroxycinnamyl@C 146.036779 146.1427 H(6)C(9)O(2) 0.0 Post-translational 407 0.0 +Glycosyl@P 148.037173 148.114 H(8)C(5)O(5) 0.0 Other glycosylation 408 0.0 +FMNH@H 454.088965 454.3279 H(19)C(17)N(4)O(9)P(1) 0.0 Post-translational 409 0.0 +FMNH@C 454.088965 454.3279 H(19)C(17)N(4)O(9)P(1) 0.0 Post-translational 409 0.0 +Archaeol@C 634.662782 635.1417 H(86)C(43)O(2) 0.0 Post-translational 410 0.0 +Phenylisocyanate@Any_N-term 119.037114 119.1207 H(5)C(7)N(1)O(1) 0.0 Chemical derivative 411 0.0 +Phenylisocyanate:2H(5)@Any_N-term 124.068498 124.1515 2H(5)C(7)N(1)O(1) 0.0 Chemical derivative 412 0.0 +Phosphoguanosine@H 345.047435 345.2053 H(12)C(10)N(5)O(7)P(1) 0.0 Post-translational 413 0.0 +Phosphoguanosine@K 345.047435 345.2053 H(12)C(10)N(5)O(7)P(1) 0.0 Post-translational 413 0.0 +Hydroxymethyl@N 30.010565 30.026 H(2)C(1)O(1) 0.0 Post-translational 414 0.0 +MolybdopterinGD+Delta:S(-1)Se(1)@C 1620.930224 1618.9096 H(47)C(40)N(20)O(26)P(4)S(3)Se(1)Mo(1) 0.0 Post-translational 415 0.0 +Dipyrrolylmethanemethyl@C 418.137616 418.3973 H(22)C(20)N(2)O(8) 0.0 Post-translational 416 0.0 +PhosphoUridine@H 306.025302 306.166 H(11)C(9)N(2)O(8)P(1) 0.0 Post-translational 417 0.0 +PhosphoUridine@Y 306.025302 306.166 H(11)C(9)N(2)O(8)P(1) 0.0 Post-translational 417 0.0 +Glycerophospho@S 154.00311 154.0584 H(7)C(3)O(5)P(1) 0.0 Post-translational 419 0.0 +Carboxy->Thiocarboxy@G^Protein_C-term 15.977156 16.0656 O(-1)S(1) 0.0 Post-translational 420 0.0 +Sulfide@D 31.972071 32.065 S(1) 0.0 Post-translational 421 0.0 +Sulfide@C 31.972071 32.065 S(1) 0.0 Post-translational 421 0.0 +Sulfide@W 31.972071 32.065 S(1) 0.0 Chemical derivative 421 0.0 +PyruvicAcidIminyl@K 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 +PyruvicAcidIminyl@V^Protein_N-term 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 +PyruvicAcidIminyl@C^Protein_N-term 70.005479 70.0468 H(2)C(3)O(2) 0.0 Post-translational 422 0.0 +Delta:Se(1)@C 79.91652 78.96 Se(1) 0.0 Post-translational 423 0.0 +MolybdopterinGD@D 1572.985775 1572.0146 H(47)C(40)N(20)O(26)P(4)S(4)Mo(1) 0.0 Post-translational 424 0.0 +MolybdopterinGD@C 1572.985775 1572.0146 H(47)C(40)N(20)O(26)P(4)S(4)Mo(1) 0.0 Post-translational 424 0.0 +MolybdopterinGD@U 1572.985775 1572.0146 H(47)C(40)N(20)O(26)P(4)S(4)Mo(1) 0.0 Post-translational 424 0.0 +Dioxidation@U 31.989829 31.9988 O(2) 0.0 Multiple 425 0.0 +Dioxidation@C 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 +Dioxidation@W 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 +Dioxidation@Y 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 +Dioxidation@F 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 +Dioxidation@M 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 +Dioxidation@K 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 +Dioxidation@R 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 +Dioxidation@P 31.989829 31.9988 O(2) 0.0 Post-translational 425 0.0 +Dioxidation@E 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 +Dioxidation@I 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 +Dioxidation@L 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 +Dioxidation@V 31.989829 31.9988 O(2) 0.0 Chemical derivative 425 0.0 +Octanoyl@T 126.104465 126.1962 H(14)C(8)O(1) 0.0 Post-translational 426 0.0 +Octanoyl@S 126.104465 126.1962 H(14)C(8)O(1) 0.0 Post-translational 426 0.0 +Octanoyl@C 126.104465 126.1962 H(14)C(8)O(1) 0.0 Post-translational 426 0.0 +PhosphoHexNAc@T 283.045704 283.1724 H(14)C(8)N(1)O(8)P(1) 283.045704 H(14)C(8)N(1)O(8)P(1) O-linked glycosylation 428 0.5 +PhosphoHexNAc@S 283.045704 283.1724 H(14)C(8)N(1)O(8)P(1) 283.045704 H(14)C(8)N(1)O(8)P(1) O-linked glycosylation 428 0.5 +PhosphoHex@T 242.019154 242.1205 H(11)C(6)O(8)P(1) 242.019154 H(11)C(6)O(8)P(1) O-linked glycosylation 429 0.5 +PhosphoHex@S 242.019154 242.1205 H(11)C(6)O(8)P(1) 242.019154 H(11)C(6)O(8)P(1) O-linked glycosylation 429 0.5 +Palmitoleyl@C 236.214016 236.3929 H(28)C(16)O(1) 0.0 Post-translational 431 0.0 +Palmitoleyl@S 236.214016 236.3929 H(28)C(16)O(1) 0.0 Post-translational 431 0.0 +Palmitoleyl@T 236.214016 236.3929 H(28)C(16)O(1) 0.0 Pre-translational 431 0.0 +Cholesterol@Protein_C-term 368.344302 368.6383 H(44)C(27) 0.0 Post-translational 432 0.0 +Didehydroretinylidene@K 264.187801 264.4046 H(24)C(20) 0.0 Post-translational 433 0.0 +CHDH@D 294.183109 294.3859 H(26)C(17)O(4) 0.0 Post-translational 434 0.0 +Methylpyrroline@K 109.052764 109.1259 H(7)C(6)N(1)O(1) 0.0 Post-translational 435 0.0 +Hydroxyheme@E 614.161645 614.4714 H(30)C(34)N(4)O(4)Fe(1) 0.0 Post-translational 436 0.0 +MicrocinC7@Protein_C-term 386.110369 386.3003 H(19)C(13)N(6)O(6)P(1) 0.0 Post-translational 437 0.0 +Cyano@C 24.995249 25.0095 H(-1)C(1)N(1) 0.0 Post-translational 438 0.0 +Diironsubcluster@C 342.786916 342.876 H(-1)C(5)N(2)O(5)S(2)Fe(2) 0.0 Post-translational 439 0.0 +Amidino@C 42.021798 42.04 H(2)C(1)N(2) 0.0 Post-translational 440 0.0 +FMN@S 438.094051 438.3285 H(19)C(17)N(4)O(8)P(1) 0.0 Post-translational 442 0.0 +FMN@T 438.094051 438.3285 H(19)C(17)N(4)O(8)P(1) 0.0 Post-translational 442 0.0 +FMNC@C 456.104615 456.3438 H(21)C(17)N(4)O(9)P(1) 0.0 Post-translational 443 0.0 +CuSMo@C 922.834855 922.067 H(24)C(19)N(8)O(15)P(2)S(3)Mo(1)Cu(1) 0.0 Post-translational 444 0.0 +Hydroxytrimethyl@K 59.04969 59.0871 H(7)C(3)O(1) 0.0 Post-translational 445 0.0 +Deoxy@T -15.994915 -15.9994 O(-1) 0.0 Chemical derivative 447 0.0 +Deoxy@D -15.994915 -15.9994 O(-1) 0.0 Post-translational 447 0.0 +Deoxy@S -15.994915 -15.9994 O(-1) 0.0 Chemical derivative 447 0.0 +Microcin@Protein_C-term 831.197041 831.6871 H(37)C(36)N(3)O(20) 0.0 Post-translational 448 0.0 +Decanoyl@T 154.135765 154.2493 H(18)C(10)O(1) 0.0 Post-translational 449 0.0 +Decanoyl@S 154.135765 154.2493 H(18)C(10)O(1) 0.0 Post-translational 449 0.0 +GluGluGlu@Protein_C-term 387.127779 387.3419 H(21)C(15)N(3)O(9) 0.0 Post-translational 452 0.0 +GluGluGlu@E 387.127779 387.3419 H(21)C(15)N(3)O(9) 0.0 Post-translational 452 0.0 +GluGluGluGlu@Protein_C-term 516.170373 516.4559 H(28)C(20)N(4)O(12) 0.0 Post-translational 453 0.0 +GluGluGluGlu@E 516.170373 516.4559 H(28)C(20)N(4)O(12) 0.0 Post-translational 453 0.0 +HexN@W 161.068808 161.1558 H(11)C(6)N(1)O(4) 0.0 Other glycosylation 454 0.0 +HexN@T 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) O-linked glycosylation 454 0.5 +HexN@S 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) O-linked glycosylation 454 0.5 +HexN@N 161.068808 161.1558 H(11)C(6)N(1)O(4) 161.068808 H(11)C(6)N(1)O(4) N-linked glycosylation 454 0.5 +HexN@K 161.068808 161.1558 H(11)C(6)N(1)O(4) 0.0 Synth. pep. protect. gp. 454 0.0 +Xlink:DMP[154]@Protein_N-term 154.110613 154.2096 H(14)C(8)N(2)O(1) 0.0 Chemical derivative 455 0.0 +Xlink:DMP[154]@K 154.110613 154.2096 H(14)C(8)N(2)O(1) 0.0 Chemical derivative 455 0.0 +NDA@Any_N-term 175.042199 175.1855 H(5)C(13)N(1) 0.0 Chemical derivative 457 0.0 +NDA@K 175.042199 175.1855 H(5)C(13)N(1) 0.0 Chemical derivative 457 0.0 +SPITC:13C(6)@Any_N-term 220.991213 221.2054 H(5)C(1)13C(6)N(1)O(3)S(2) 0.0 Chemical derivative 464 0.0 +SPITC:13C(6)@K 220.991213 221.2054 H(5)C(1)13C(6)N(1)O(3)S(2) 0.0 Chemical derivative 464 0.0 +TMAB:2H(9)@Any_N-term 137.16403 137.2476 H(5)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 477 0.5 +TMAB:2H(9)@K 137.16403 137.2476 H(5)2H(9)C(7)N(1)O(1) 68.12999 2H(9)C(3)N(1) Isotopic label 477 0.5 +TMAB@Any_N-term 128.107539 128.1922 H(14)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 476 0.5 +TMAB@K 128.107539 128.1922 H(14)C(7)N(1)O(1) 59.073499 H(9)C(3)N(1) Isotopic label 476 0.5 +FTC@S 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 +FTC@R 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 +FTC@P 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 +FTC@K 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 +FTC@C 421.073241 421.4259 H(15)C(21)N(3)O(5)S(1) 0.0 Chemical derivative 478 0.0 +AEC-MAEC@T 59.019355 59.1334 H(5)C(2)N(1)O(-1)S(1) 0.0 Chemical derivative 472 0.0 +AEC-MAEC@S 59.019355 59.1334 H(5)C(2)N(1)O(-1)S(1) 0.0 Chemical derivative 472 0.0 +BADGE@C 340.167459 340.4129 H(24)C(21)O(4) 0.0 Non-standard residue 493 0.0 +Label:2H(4)@A 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 +Label:2H(4)@Y 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 +Label:2H(4)@F 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 +Label:2H(4)@K 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 +Label:2H(4)@U 4.025107 4.0246 H(-4)2H(4) 0.0 Isotopic label 481 0.0 +Hep@T 192.063388 192.1666 H(12)C(7)O(6) 192.063388 H(12)C(7)O(6) O-linked glycosylation 490 0.5 +Hep@S 192.063388 192.1666 H(12)C(7)O(6) 192.063388 H(12)C(7)O(6) O-linked glycosylation 490 0.5 +Hep@R 192.063388 192.1666 H(12)C(7)O(6) 0.0 N-linked glycosylation 490 0.0 +Hep@Q 192.063388 192.1666 H(12)C(7)O(6) 0.0 Other glycosylation 490 0.0 +Hep@N 192.063388 192.1666 H(12)C(7)O(6) 192.063388 H(12)C(7)O(6) N-linked glycosylation 490 0.5 +Hep@K 192.063388 192.1666 H(12)C(7)O(6) 0.0 Other glycosylation 490 0.0 +CyDye-Cy5@C 684.298156 684.8442 H(44)C(38)N(4)O(6)S(1) 0.0 Chemical derivative 495 0.0 +DHP@C 118.065674 118.1558 H(8)C(8)N(1) 0.0 Chemical derivative 488 0.0 +BHTOH@H 234.16198 234.334 H(22)C(15)O(2) 0.0 Other 498 0.0 +BHTOH@C 234.16198 234.334 H(22)C(15)O(2) 0.0 Other 498 0.0 +BHTOH@K 234.16198 234.334 H(22)C(15)O(2) 0.0 Other 498 0.0 +IGBP:13C(2)@C 298.022748 299.1331 H(13)C(10)13C(2)N(2)O(2)Br(1) 0.0 Isotopic label 499 0.0 +Nmethylmaleimide+water@C 129.042593 129.114 H(7)C(5)N(1)O(3) 0.0 Chemical derivative 500 0.0 +PyMIC@Any_N-term 134.048013 134.1353 H(6)C(7)N(2)O(1) 0.0 Chemical derivative 501 0.0 +LG-lactam-K@Protein_N-term 332.19876 332.4339 H(28)C(20)O(4) 0.0 Post-translational 503 0.0 +LG-lactam-K@K 332.19876 332.4339 H(28)C(20)O(4) 0.0 Post-translational 503 0.0 +BisANS@K 594.091928 594.6569 H(22)C(32)N(2)O(6)S(2) 0.0 Chemical derivative 519 0.0 +Piperidine@Any_N-term 68.0626 68.117 H(8)C(5) 0.0 Chemical derivative 520 0.0 +Piperidine@K 68.0626 68.117 H(8)C(5) 0.0 Chemical derivative 520 0.0 +Diethyl@Any_N-term 56.0626 56.1063 H(8)C(4) 0.0 Chemical derivative 518 0.0 +Diethyl@K 56.0626 56.1063 H(8)C(4) 0.0 Chemical derivative 518 0.0 +LG-Hlactam-K@Protein_N-term 348.193674 348.4333 H(28)C(20)O(5) 0.0 Post-translational 504 0.0 +LG-Hlactam-K@K 348.193674 348.4333 H(28)C(20)O(5) 0.0 Post-translational 504 0.0 +Dimethyl:2H(4)13C(2)@Protein_N-term 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 +Dimethyl:2H(4)13C(2)@R 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 +Dimethyl:2H(4)13C(2)@K 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 +Dimethyl:2H(4)13C(2)@Any_N-term 34.063117 34.0631 2H(4)13C(2) 0.0 Isotopic label 510 0.0 +C8-QAT@Any_N-term 227.224915 227.3862 H(29)C(14)N(1)O(1) 0.0 Chemical derivative 513 0.0 +C8-QAT@K 227.224915 227.3862 H(29)C(14)N(1)O(1) 0.0 Chemical derivative 513 0.0 +Hex(2)@R 324.105647 324.2812 H(20)C(12)O(10) 0.0 Other glycosylation 512 0.0 +Hex(2)@K 324.105647 324.2812 H(20)C(12)O(10) 0.0 Other glycosylation 512 0.0 +Hex(2)@S 324.105647 324.2812 H(20)C(12)O(10) 324.105647 H(20)C(12)O(10) O-linked glycosylation 512 0.5 +Hex(2)@T 324.105647 324.2812 H(20)C(12)O(10) 324.105647 H(20)C(12)O(10) O-linked glycosylation 512 0.5 +LG-lactam-R@R 290.176961 290.3939 H(26)C(19)N(-2)O(4) 0.0 Post-translational 505 0.0 +Withaferin@C 470.266839 470.5977 H(38)C(28)O(6) 0.0 Chemical derivative 1036 0.0 +Biotin:Thermo-88317@S 443.291294 443.5603 H(42)C(22)N(3)O(4)P(1) 0.0 Chemical derivative 1037 0.0 +Biotin:Thermo-88317@Y 443.291294 443.5603 H(42)C(22)N(3)O(4)P(1) 0.0 Chemical derivative 1037 0.0 +CLIP_TRAQ_2@Any_N-term 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 +CLIP_TRAQ_2@K 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 +CLIP_TRAQ_2@Y 141.098318 141.1756 H(12)C(6)13C(1)N(2)O(1) 0.0 Isotopic label 525 0.0 +LG-Hlactam-R@R 306.171876 306.3933 H(26)C(19)N(-2)O(5) 0.0 Post-translational 506 0.0 +Maleimide-PEO2-Biotin@C 525.225719 525.6183 H(35)C(23)N(5)O(7)S(1) 0.0 Chemical derivative 522 0.0 +Sulfo-NHS-LC-LC-Biotin@Any_N-term 452.245726 452.6106 H(36)C(22)N(4)O(4)S(1) 0.0 Chemical derivative 523 0.0 +Sulfo-NHS-LC-LC-Biotin@K 452.245726 452.6106 H(36)C(22)N(4)O(4)S(1) 0.0 Chemical derivative 523 0.0 +FNEM@C 427.069202 427.3625 H(13)C(24)N(1)O(7) 0.0 Chemical derivative 515 0.0 +PropylNAGthiazoline@C 232.064354 232.2768 H(14)C(9)N(1)O(4)S(1) 0.0 Chemical derivative 514 0.0 +Dethiomethyl@M -48.003371 -48.1075 H(-4)C(-1)S(-1) 0.0 Artefact 526 0.0 +iTRAQ4plex114@Y 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 +iTRAQ4plex114@Any_N-term 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 +iTRAQ4plex114@K 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 +iTRAQ4plex114@C 144.105918 144.168 H(12)C(5)13C(2)N(2)18O(1) 0.0 Isotopic label 532 0.0 +iTRAQ4plex115@Y 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 +iTRAQ4plex115@Any_N-term 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 +iTRAQ4plex115@K 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 +iTRAQ4plex115@C 144.099599 144.1688 H(12)C(6)13C(1)N(1)15N(1)18O(1) 0.0 Isotopic label 533 0.0 +Dibromo@Y 155.821022 157.7921 H(-2)Br(2) 0.0 Chemical derivative 534 0.0 +LRGG@K 383.228103 383.446 H(29)C(16)N(7)O(4) 0.0 Chemical derivative 535 0.0 +CLIP_TRAQ_3@Y 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 +CLIP_TRAQ_3@Any_N-term 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 +CLIP_TRAQ_3@K 271.148736 271.2976 H(20)C(11)13C(1)N(3)O(4) 0.0 Isotopic label 536 0.0 +CLIP_TRAQ_4@Any_N-term 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 +CLIP_TRAQ_4@K 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 +CLIP_TRAQ_4@Y 244.101452 244.2292 H(15)C(9)13C(1)N(2)O(5) 0.0 Isotopic label 537 0.0 +Biotin:Cayman-10141@C 626.386577 626.8927 H(54)C(35)N(4)O(4)S(1) 0.0 Other 538 0.0 +Biotin:Cayman-10013@C 660.428442 660.9504 H(60)C(36)N(4)O(5)S(1) 0.0 Other 539 0.0 +Ala->Ser@A 15.994915 15.9994 H(0)C(0)N(0)O(1)S(0) 0.0 AA substitution 540 0.0 +Ala->Thr@A 30.010565 30.026 H(2)C(1)N(0)O(1)S(0) 0.0 AA substitution 541 0.0 +Ala->Asp@A 43.989829 44.0095 H(0)C(1)N(0)O(2)S(0) 0.0 AA substitution 542 0.0 +Ala->Pro@A 26.01565 26.0373 H(2)C(2)N(0)O(0)S(0) 0.0 AA substitution 543 0.0 +Ala->Gly@A -14.01565 -14.0266 H(-2)C(-1)N(0)O(0)S(0) 0.0 AA substitution 544 0.0 +Ala->Glu@A 58.005479 58.0361 H(2)C(2)N(0)O(2)S(0) 0.0 AA substitution 545 0.0 +Ala->Val@A 28.0313 28.0532 H(4)C(2)N(0)O(0)S(0) 0.0 AA substitution 546 0.0 +Cys->Phe@C 44.059229 44.031 H(4)C(6)N(0)O(0)S(-1) 0.0 AA substitution 547 0.0 +Cys->Ser@C -15.977156 -16.0656 H(0)C(0)N(0)O(1)S(-1) 0.0 AA substitution 548 0.0 +Cys->Trp@C 83.070128 83.067 H(5)C(8)N(1)O(0)S(-1) 0.0 AA substitution 549 0.0 +Cys->Tyr@C 60.054144 60.0304 H(4)C(6)N(0)O(1)S(-1) 0.0 AA substitution 550 0.0 +Cys->Arg@C 53.091927 53.0428 H(7)C(3)N(3)O(0)S(-1) 0.0 AA substitution 551 0.0 +Cys->Gly@C -45.987721 -46.0916 H(-2)C(-1)N(0)O(0)S(-1) 0.0 AA substitution 552 0.0 +Asp->Ala@D -43.989829 -44.0095 H(0)C(-1)N(0)O(-2)S(0) 0.0 AA substitution 553 0.0 +Asp->His@D 22.031969 22.0519 H(2)C(2)N(2)O(-2)S(0) 0.0 AA substitution 554 0.0 +Asp->Asn@D -0.984016 -0.9848 H(1)C(0)N(1)O(-1)S(0) 0.0 AA substitution 555 0.0 +Asp->Gly@D -58.005479 -58.0361 H(-2)C(-2)N(0)O(-2)S(0) 0.0 AA substitution 556 0.0 +Asp->Tyr@D 48.036386 48.0859 H(4)C(5)N(0)O(-1)S(0) 0.0 AA substitution 557 0.0 +Asp->Glu@D 14.01565 14.0266 H(2)C(1)N(0)O(0)S(0) 0.0 AA substitution 558 0.0 +Asp->Val@D -15.958529 -15.9563 H(4)C(1)N(0)O(-2)S(0) 0.0 AA substitution 559 0.0 +Glu->Ala@E -58.005479 -58.0361 H(-2)C(-2)N(0)O(-2)S(0) 0.0 AA substitution 560 0.0 +Glu->Gln@E -0.984016 -0.9848 H(1)C(0)N(1)O(-1)S(0) 0.0 AA substitution 561 0.0 +Glu->Asp@E -14.01565 -14.0266 H(-2)C(-1)N(0)O(0)S(0) 0.0 AA substitution 562 0.0 +Glu->Lys@E -0.94763 -0.9417 H(5)C(1)N(1)O(-2)S(0) 0.0 AA substitution 563 0.0 +Glu->Gly@E -72.021129 -72.0627 H(-4)C(-3)N(0)O(-2)S(0) 0.0 AA substitution 564 0.0 +Glu->Val@E -29.974179 -29.9829 H(2)C(0)N(0)O(-2)S(0) 0.0 AA substitution 565 0.0 +Phe->Ser@F -60.036386 -60.0966 H(-4)C(-6)N(0)O(1)S(0) 0.0 AA substitution 566 0.0 +Phe->Cys@F -44.059229 -44.031 H(-4)C(-6)N(0)O(0)S(1) 0.0 AA substitution 567 0.0 +Phe->Xle@F -33.98435 -34.0162 H(2)C(-3) 0.0 AA substitution 568 0.0 +Phe->Tyr@F 15.994915 15.9994 H(0)C(0)N(0)O(1)S(0) 0.0 AA substitution 569 0.0 +Phe->Val@F -48.0 -48.0428 H(0)C(-4)N(0)O(0)S(0) 0.0 AA substitution 570 0.0 +Gly->Ala@G 14.01565 14.0266 H(2)C(1)N(0)O(0)S(0) 0.0 AA substitution 571 0.0 +Gly->Ser@G 30.010565 30.026 H(2)C(1)N(0)O(1)S(0) 0.0 AA substitution 572 0.0 +Gly->Trp@G 129.057849 129.1586 H(7)C(9)N(1)O(0)S(0) 0.0 AA substitution 573 0.0 +Gly->Glu@G 72.021129 72.0627 H(4)C(3)N(0)O(2)S(0) 0.0 AA substitution 574 0.0 +Gly->Val@G 42.04695 42.0797 H(6)C(3)N(0)O(0)S(0) 0.0 AA substitution 575 0.0 +Gly->Asp@G 58.005479 58.0361 H(2)C(2)N(0)O(2)S(0) 0.0 AA substitution 576 0.0 +Gly->Cys@G 45.987721 46.0916 H(2)C(1)N(0)O(0)S(1) 0.0 AA substitution 577 0.0 +Gly->Arg@G 99.079647 99.1344 H(9)C(4)N(3)O(0)S(0) 0.0 AA substitution 578 0.0 +dNIC@Any_N-term 109.048119 109.1205 H(1)2H(3)C(6)N(1)O(1) 0.0 Isotopic label 698 0.0 +dNIC@K 109.048119 109.1205 H(1)2H(3)C(6)N(1)O(1) 0.0 Isotopic label 698 0.0 +His->Pro@H -40.006148 -40.0241 H(0)C(-1)N(-2)O(0)S(0) 0.0 AA substitution 580 0.0 +His->Tyr@H 26.004417 26.034 H(2)C(3)N(-2)O(1)S(0) 0.0 AA substitution 581 0.0 +His->Gln@H -9.000334 -9.0101 H(1)C(-1)N(-1)O(1)S(0) 0.0 AA substitution 582 0.0 +NIC@Any_N-term 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 697 0.0 +NIC@K 105.021464 105.0941 H(3)C(6)N(1)O(1) 0.0 Isotopic label 697 0.0 +His->Arg@H 19.042199 19.0464 H(5)C(0)N(1)O(0)S(0) 0.0 AA substitution 584 0.0 +His->Xle@H -23.974848 -23.9816 H(4)N(-2) 0.0 AA substitution 585 0.0 +Xle->Ala@L -42.04695 -42.0797 H(-6)C(-3)N(0)O(0)S(0) 0.0 AA substitution 1125 0.0 +Xle->Ala@I -42.04695 -42.0797 H(-6)C(-3)N(0)O(0)S(0) 0.0 AA substitution 1125 0.0 +Xle->Thr@L -12.036386 -12.0538 H(-4)C(-2)O(1) 0.0 AA substitution 588 0.0 +Xle->Thr@I -12.036386 -12.0538 H(-4)C(-2)O(1) 0.0 AA substitution 588 0.0 +Xle->Asn@L 0.958863 0.945 H(-5)C(-2)N(1)O(1) 0.0 AA substitution 589 0.0 +Xle->Asn@I 0.958863 0.945 H(-5)C(-2)N(1)O(1) 0.0 AA substitution 589 0.0 +Xle->Lys@L 15.010899 15.0146 H(1)N(1) 0.0 AA substitution 590 0.0 +Xle->Lys@I 15.010899 15.0146 H(1)N(1) 0.0 AA substitution 590 0.0 +Lys->Thr@K -27.047285 -27.0684 H(-5)C(-2)N(-1)O(1)S(0) 0.0 AA substitution 594 0.0 +Lys->Asn@K -14.052036 -14.0696 H(-6)C(-2)N(0)O(1)S(0) 0.0 AA substitution 595 0.0 +Lys->Glu@K 0.94763 0.9417 H(-5)C(-1)N(-1)O(2)S(0) 0.0 AA substitution 596 0.0 +Lys->Gln@K -0.036386 -0.0431 H(-4)C(-1)N(0)O(1)S(0) 0.0 AA substitution 597 0.0 +Lys->Met@K 2.945522 3.0238 H(-3)C(-1)N(-1)O(0)S(1) 0.0 AA substitution 598 0.0 +Lys->Arg@K 28.006148 28.0134 H(0)C(0)N(2)O(0)S(0) 0.0 AA substitution 599 0.0 +Lys->Xle@K -15.010899 -15.0146 H(-1)N(-1) 0.0 AA substitution 600 0.0 +Xle->Ser@I -26.052036 -26.0803 H(-6)C(-3)O(1) 0.0 AA substitution 601 0.0 +Xle->Ser@L -26.052036 -26.0803 H(-6)C(-3)O(1) 0.0 AA substitution 601 0.0 +Xle->Phe@I 33.98435 34.0162 H(-2)C(3) 0.0 AA substitution 602 0.0 +Xle->Phe@L 33.98435 34.0162 H(-2)C(3) 0.0 AA substitution 602 0.0 +Xle->Trp@I 72.995249 73.0523 H(-1)C(5)N(1) 0.0 AA substitution 603 0.0 +Xle->Trp@L 72.995249 73.0523 H(-1)C(5)N(1) 0.0 AA substitution 603 0.0 +Xle->Pro@I -16.0313 -16.0425 H(-4)C(-1) 0.0 AA substitution 604 0.0 +Xle->Pro@L -16.0313 -16.0425 H(-4)C(-1) 0.0 AA substitution 604 0.0 +Xle->Val@I -14.01565 -14.0266 H(-2)C(-1) 0.0 AA substitution 605 0.0 +Xle->Val@L -14.01565 -14.0266 H(-2)C(-1) 0.0 AA substitution 605 0.0 +Xle->His@I 23.974848 23.9816 H(-4)N(2) 0.0 AA substitution 606 0.0 +Xle->His@L 23.974848 23.9816 H(-4)N(2) 0.0 AA substitution 606 0.0 +Xle->Gln@I 14.974514 14.9716 H(-3)C(-1)N(1)O(1) 0.0 AA substitution 607 0.0 +Xle->Gln@L 14.974514 14.9716 H(-3)C(-1)N(1)O(1) 0.0 AA substitution 607 0.0 +Xle->Met@I 17.956421 18.0384 H(-2)C(-1)S(1) 0.0 AA substitution 608 0.0 +Xle->Met@L 17.956421 18.0384 H(-2)C(-1)S(1) 0.0 AA substitution 608 0.0 +Xle->Arg@I 43.017047 43.028 H(1)N(3) 0.0 AA substitution 609 0.0 +Xle->Arg@L 43.017047 43.028 H(1)N(3) 0.0 AA substitution 609 0.0 +Met->Thr@M -29.992806 -30.0922 H(-2)C(-1)N(0)O(1)S(-1) 0.0 AA substitution 610 0.0 +Met->Arg@M 25.060626 24.9896 H(3)C(1)N(3)O(0)S(-1) 0.0 AA substitution 611 0.0 +Met->Lys@M -2.945522 -3.0238 H(3)C(1)N(1)O(0)S(-1) 0.0 AA substitution 613 0.0 +Met->Xle@M -17.956421 -18.0384 H(2)C(1)S(-1) 0.0 AA substitution 614 0.0 +Met->Val@M -31.972071 -32.065 H(0)C(0)N(0)O(0)S(-1) 0.0 AA substitution 615 0.0 +Asn->Ser@N -27.010899 -27.0253 H(-1)C(-1)N(-1)O(0)S(0) 0.0 AA substitution 616 0.0 +Asn->Thr@N -12.995249 -12.9988 H(1)C(0)N(-1)O(0)S(0) 0.0 AA substitution 617 0.0 +Asn->Lys@N 14.052036 14.0696 H(6)C(2)N(0)O(-1)S(0) 0.0 AA substitution 618 0.0 +Asn->Tyr@N 49.020401 49.0706 H(3)C(5)N(-1)O(0)S(0) 0.0 AA substitution 619 0.0 +Asn->His@N 23.015984 23.0366 H(1)C(2)N(1)O(-1)S(0) 0.0 AA substitution 620 0.0 +Asn->Asp@N 0.984016 0.9848 H(-1)C(0)N(-1)O(1)S(0) 0.0 AA substitution 621 0.0 +Asn->Xle@N -0.958863 -0.945 H(5)C(2)N(-1)O(-1) 0.0 AA substitution 622 0.0 +Pro->Ser@P -10.020735 -10.0379 H(-2)C(-2)N(0)O(1)S(0) 0.0 AA substitution 623 0.0 +Pro->Ala@P -26.01565 -26.0373 H(-2)C(-2)N(0)O(0)S(0) 0.0 AA substitution 624 0.0 +Pro->His@P 40.006148 40.0241 H(0)C(1)N(2)O(0)S(0) 0.0 AA substitution 625 0.0 +Pro->Gln@P 31.005814 31.014 H(1)C(0)N(1)O(1)S(0) 0.0 AA substitution 626 0.0 +Pro->Thr@P 3.994915 3.9887 H(0)C(-1)N(0)O(1)S(0) 0.0 AA substitution 627 0.0 +Pro->Arg@P 59.048347 59.0705 H(5)C(1)N(3)O(0)S(0) 0.0 AA substitution 628 0.0 +Pro->Xle@P 16.0313 16.0425 H(4)C(1) 0.0 AA substitution 629 0.0 +Gln->Pro@Q -31.005814 -31.014 H(-1)C(0)N(-1)O(-1)S(0) 0.0 AA substitution 630 0.0 +Gln->Lys@Q 0.036386 0.0431 H(4)C(1)N(0)O(-1)S(0) 0.0 AA substitution 631 0.0 +Gln->Glu@Q 0.984016 0.9848 H(-1)C(0)N(-1)O(1)S(0) 0.0 AA substitution 632 0.0 +Gln->His@Q 9.000334 9.0101 H(-1)C(1)N(1)O(-1)S(0) 0.0 AA substitution 633 0.0 +Gln->Arg@Q 28.042534 28.0565 H(4)C(1)N(2)O(-1)S(0) 0.0 AA substitution 634 0.0 +Gln->Xle@Q -14.974514 -14.9716 H(3)C(1)N(-1)O(-1) 0.0 AA substitution 635 0.0 +Arg->Ser@R -69.069083 -69.1084 H(-7)C(-3)N(-3)O(1)S(0) 0.0 AA substitution 636 0.0 +Arg->Trp@R 29.978202 30.0242 H(-2)C(5)N(-2)O(0)S(0) 0.0 AA substitution 637 0.0 +Arg->Thr@R -55.053433 -55.0818 H(-5)C(-2)N(-3)O(1)S(0) 0.0 AA substitution 638 0.0 +Arg->Pro@R -59.048347 -59.0705 H(-5)C(-1)N(-3)O(0)S(0) 0.0 AA substitution 639 0.0 +Arg->Lys@R -28.006148 -28.0134 H(0)C(0)N(-2)O(0)S(0) 0.0 AA substitution 640 0.0 +Arg->His@R -19.042199 -19.0464 H(-5)C(0)N(-1)O(0)S(0) 0.0 AA substitution 641 0.0 +Arg->Gln@R -28.042534 -28.0565 H(-4)C(-1)N(-2)O(1)S(0) 0.0 AA substitution 642 0.0 +Arg->Met@R -25.060626 -24.9896 H(-3)C(-1)N(-3)O(0)S(1) 0.0 AA substitution 643 0.0 +Arg->Cys@R -53.091927 -53.0428 H(-7)C(-3)N(-3)O(0)S(1) 0.0 AA substitution 644 0.0 +Arg->Xle@R -43.017047 -43.028 H(-1)N(-3) 0.0 AA substitution 645 0.0 +Arg->Gly@R -99.079647 -99.1344 H(-9)C(-4)N(-3)O(0)S(0) 0.0 AA substitution 646 0.0 +Ser->Phe@S 60.036386 60.0966 H(4)C(6)N(0)O(-1)S(0) 0.0 AA substitution 647 0.0 +Ser->Ala@S -15.994915 -15.9994 H(0)C(0)N(0)O(-1)S(0) 0.0 AA substitution 648 0.0 +Ser->Trp@S 99.047285 99.1326 H(5)C(8)N(1)O(-1)S(0) 0.0 AA substitution 649 0.0 +Ser->Thr@S 14.01565 14.0266 H(2)C(1)N(0)O(0)S(0) 0.0 AA substitution 650 0.0 +Ser->Asn@S 27.010899 27.0253 H(1)C(1)N(1)O(0)S(0) 0.0 AA substitution 651 0.0 +Ser->Pro@S 10.020735 10.0379 H(2)C(2)N(0)O(-1)S(0) 0.0 AA substitution 652 0.0 +Ser->Tyr@S 76.0313 76.096 H(4)C(6)N(0)O(0)S(0) 0.0 AA substitution 653 0.0 +Ser->Cys@S 15.977156 16.0656 H(0)C(0)N(0)O(-1)S(1) 0.0 AA substitution 654 0.0 +Ser->Arg@S 69.069083 69.1084 H(7)C(3)N(3)O(-1)S(0) 0.0 AA substitution 655 0.0 +Ser->Xle@S 26.052036 26.0803 H(6)C(3)O(-1) 0.0 AA substitution 656 0.0 +Ser->Gly@S -30.010565 -30.026 H(-2)C(-1)N(0)O(-1)S(0) 0.0 AA substitution 657 0.0 +Thr->Ser@T -14.01565 -14.0266 H(-2)C(-1)N(0)O(0)S(0) 0.0 AA substitution 658 0.0 +Thr->Ala@T -30.010565 -30.026 H(-2)C(-1)N(0)O(-1)S(0) 0.0 AA substitution 659 0.0 +Thr->Asn@T 12.995249 12.9988 H(-1)C(0)N(1)O(0)S(0) 0.0 AA substitution 660 0.0 +Thr->Lys@T 27.047285 27.0684 H(5)C(2)N(1)O(-1)S(0) 0.0 AA substitution 661 0.0 +Thr->Pro@T -3.994915 -3.9887 H(0)C(1)N(0)O(-1)S(0) 0.0 AA substitution 662 0.0 +Thr->Met@T 29.992806 30.0922 H(2)C(1)N(0)O(-1)S(1) 0.0 AA substitution 663 0.0 +Thr->Xle@T 12.036386 12.0538 H(4)C(2)O(-1) 0.0 AA substitution 664 0.0 +Thr->Arg@T 55.053433 55.0818 H(5)C(2)N(3)O(-1)S(0) 0.0 AA substitution 665 0.0 +Val->Phe@V 48.0 48.0428 H(0)C(4)N(0)O(0)S(0) 0.0 AA substitution 666 0.0 +Val->Ala@V -28.0313 -28.0532 H(-4)C(-2)N(0)O(0)S(0) 0.0 AA substitution 667 0.0 +Val->Glu@V 29.974179 29.9829 H(-2)C(0)N(0)O(2)S(0) 0.0 AA substitution 668 0.0 +Val->Met@V 31.972071 32.065 H(0)C(0)N(0)O(0)S(1) 0.0 AA substitution 669 0.0 +Val->Asp@V 15.958529 15.9563 H(-4)C(-1)N(0)O(2)S(0) 0.0 AA substitution 670 0.0 +Val->Xle@V 14.01565 14.0266 H(2)C(1) 0.0 AA substitution 671 0.0 +Val->Gly@V -42.04695 -42.0797 H(-6)C(-3)N(0)O(0)S(0) 0.0 AA substitution 672 0.0 +Trp->Ser@W -99.047285 -99.1326 H(-5)C(-8)N(-1)O(1)S(0) 0.0 AA substitution 673 0.0 +Trp->Cys@W -83.070128 -83.067 H(-5)C(-8)N(-1)O(0)S(1) 0.0 AA substitution 674 0.0 +Trp->Arg@W -29.978202 -30.0242 H(2)C(-5)N(2)O(0)S(0) 0.0 AA substitution 675 0.0 +Trp->Gly@W -129.057849 -129.1586 H(-7)C(-9)N(-1)O(0)S(0) 0.0 AA substitution 676 0.0 +Trp->Xle@W -72.995249 -73.0523 H(1)C(-5)N(-1) 0.0 AA substitution 677 0.0 +Tyr->Phe@Y -15.994915 -15.9994 H(0)C(0)N(0)O(-1)S(0) 0.0 AA substitution 678 0.0 +Tyr->Ser@Y -76.0313 -76.096 H(-4)C(-6)N(0)O(0)S(0) 0.0 AA substitution 679 0.0 +Tyr->Asn@Y -49.020401 -49.0706 H(-3)C(-5)N(1)O(0)S(0) 0.0 AA substitution 680 0.0 +Tyr->His@Y -26.004417 -26.034 H(-2)C(-3)N(2)O(-1)S(0) 0.0 AA substitution 681 0.0 +Tyr->Asp@Y -48.036386 -48.0859 H(-4)C(-5)N(0)O(1)S(0) 0.0 AA substitution 682 0.0 +Tyr->Cys@Y -60.054144 -60.0304 H(-4)C(-6)N(0)O(-1)S(1) 0.0 AA substitution 683 0.0 +BDMAPP@W 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 +BDMAPP@Y 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 +BDMAPP@Protein_N-term 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Chemical derivative 684 0.0 +BDMAPP@K 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Chemical derivative 684 0.0 +BDMAPP@H 253.010225 254.1231 H(12)C(11)N(1)O(1)Br(1) 0.0 Artefact 684 0.0 +NA-LNO2@C 325.225309 325.443 H(31)C(18)N(1)O(4) 0.0 Post-translational 685 0.0 +NA-LNO2@H 325.225309 325.443 H(31)C(18)N(1)O(4) 0.0 Post-translational 685 0.0 +NA-OA-NO2@C 327.240959 327.4589 H(33)C(18)N(1)O(4) 0.0 Post-translational 686 0.0 +NA-OA-NO2@H 327.240959 327.4589 H(33)C(18)N(1)O(4) 0.0 Post-translational 686 0.0 +ICPL:2H(4)@Any_N-term 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 +ICPL:2H(4)@Protein_N-term 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 +ICPL:2H(4)@K 109.046571 109.1188 H(-1)2H(4)C(6)N(1)O(1) 0.0 Isotopic label 687 0.0 +CarboxymethylDTT@C 210.00205 210.2712 H(10)C(6)O(4)S(2) 0.0 Artefact 894 0.0 +iTRAQ8plex@Protein_N-term 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@T 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@S 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@H 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@Y 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@Any_N-term 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@K 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +iTRAQ8plex@C 304.20536 304.3074 H(24)C(7)13C(7)N(3)15N(1)O(3) 0.0 Isotopic label 730 0.0 +Label:13C(6)15N(1)@I 7.017164 6.9493 C(-6)13C(6)N(-1)15N(1) 0.0 Isotopic label 695 0.0 +Label:13C(6)15N(1)@L 7.017164 6.9493 C(-6)13C(6)N(-1)15N(1) 0.0 Isotopic label 695 0.0 +Label:2H(9)13C(6)15N(2)@K 17.07069 16.9982 H(-9)2H(9)C(-6)13C(6)N(-2)15N(2) 0.0 Isotopic label 696 0.0 +HNE-Delta:H(2)O@K 138.104465 138.2069 H(14)C(9)O(1) 0.0 Chemical derivative 720 0.0 +HNE-Delta:H(2)O@H 138.104465 138.2069 H(14)C(9)O(1) 0.0 Chemical derivative 720 0.0 +HNE-Delta:H(2)O@C 138.104465 138.2069 H(14)C(9)O(1) 0.0 Chemical derivative 720 0.0 +4-ONE@K 154.09938 154.2063 H(14)C(9)O(2) 0.0 Chemical derivative 721 0.0 +4-ONE@H 154.09938 154.2063 H(14)C(9)O(2) 0.0 Chemical derivative 721 0.0 +4-ONE@C 154.09938 154.2063 H(14)C(9)O(2) 0.0 Chemical derivative 721 0.0 +O-Dimethylphosphate@Y 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 723 0.0 +O-Dimethylphosphate@T 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 723 0.0 +O-Dimethylphosphate@S 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 723 0.0 +O-Methylphosphate@Y 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 +O-Methylphosphate@T 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 +O-Methylphosphate@S 93.981981 94.0065 H(3)C(1)O(3)P(1) 0.0 Chemical derivative 724 0.0 +Diethylphosphate@Any_N-term 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 +Diethylphosphate@H 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 +Diethylphosphate@C 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 +Diethylphosphate@K 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 +Diethylphosphate@Y 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 +Diethylphosphate@T 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 +Diethylphosphate@S 136.028931 136.0862 H(9)C(4)O(3)P(1) 0.0 Chemical derivative 725 0.0 +Ethylphosphate@Any_N-term 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 +Ethylphosphate@K 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 +Ethylphosphate@Y 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 +Ethylphosphate@T 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 +Ethylphosphate@S 107.997631 108.0331 H(5)C(2)O(3)P(1) 0.0 Chemical derivative 726 0.0 +O-pinacolylmethylphosphonate@T 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 +O-pinacolylmethylphosphonate@S 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 +O-pinacolylmethylphosphonate@K 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 +O-pinacolylmethylphosphonate@Y 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 +O-pinacolylmethylphosphonate@H 162.080967 162.1666 H(15)C(7)O(2)P(1) 0.0 Chemical derivative 727 0.0 +Methylphosphonate@Y 77.987066 78.0071 H(3)C(1)O(2)P(1) 0.0 Chemical derivative 728 0.0 +Methylphosphonate@T 77.987066 78.0071 H(3)C(1)O(2)P(1) 0.0 Chemical derivative 728 0.0 +Methylphosphonate@S 77.987066 78.0071 H(3)C(1)O(2)P(1) 0.0 Chemical derivative 728 0.0 +O-Isopropylmethylphosphonate@Y 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemical derivative 729 0.0 +O-Isopropylmethylphosphonate@T 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemical derivative 729 0.0 +O-Isopropylmethylphosphonate@S 120.034017 120.0868 H(9)C(4)O(2)P(1) 0.0 Chemical derivative 729 0.0 +iTRAQ8plex:13C(6)15N(2)@Y 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 +iTRAQ8plex:13C(6)15N(2)@Any_N-term 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 +iTRAQ8plex:13C(6)15N(2)@K 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 +iTRAQ8plex:13C(6)15N(2)@C 304.19904 304.3081 H(24)C(8)13C(6)N(2)15N(2)O(3) 0.0 Isotopic label 731 0.0 +BEMAD_ST@T 136.001656 136.2357 H(8)C(4)O(1)S(2) 0.0 Chemical derivative 735 0.0 +BEMAD_ST@S 136.001656 136.2357 H(8)C(4)O(1)S(2) 0.0 Chemical derivative 735 0.0 +Ethanolamine@D 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 +Ethanolamine@Any_C-term 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 +Ethanolamine@E 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 +Ethanolamine@C 43.042199 43.0678 H(5)C(2)N(1) 0.0 Chemical derivative 734 0.0 +TMT6plex@T 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 +TMT6plex@S 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 +TMT6plex@H 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 +TMT6plex@Protein_N-term 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 +TMT6plex@Any_N-term 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 +TMT6plex@K 229.162932 229.2634 H(20)C(8)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 737 0.0 +BEMAD_C@C 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 736 0.0 +TMT2plex@H 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 +TMT2plex@S 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 +TMT2plex@T 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 +TMT2plex@Protein_N-term 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 +TMT2plex@Any_N-term 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 +TMT2plex@K 225.155833 225.2921 H(20)C(11)13C(1)N(2)O(2) 0.0 Isotopic label 738 0.0 +TMT@Protein_N-term 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 +TMT@Any_N-term 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 +TMT@K 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Chemical derivative 739 0.0 +TMT@H 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Isotopic label 739 0.0 +TMT@S 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Isotopic label 739 0.0 +TMT@T 224.152478 224.2994 H(20)C(12)N(2)O(2) 0.0 Isotopic label 739 0.0 +ExacTagThiol@C 972.365219 972.7268 H(50)C(23)13C(12)N(8)15N(6)O(18) 0.0 Isotopic label 740 0.0 +ExacTagAmine@K 1046.347854 1046.8285 H(52)C(25)13C(12)N(8)15N(6)O(19)S(1) 0.0 Isotopic label 741 0.0 +NO_SMX_SEMD@C 251.036462 251.2618 H(9)C(10)N(3)O(3)S(1) 0.0 Chemical derivative 744 0.0 +4-ONE+Delta:H(-2)O(-1)@K 136.088815 136.191 H(12)C(9)O(1) 0.0 Chemical derivative 743 0.0 +4-ONE+Delta:H(-2)O(-1)@H 136.088815 136.191 H(12)C(9)O(1) 0.0 Chemical derivative 743 0.0 +4-ONE+Delta:H(-2)O(-1)@C 136.088815 136.191 H(12)C(9)O(1) 0.0 Chemical derivative 743 0.0 +Biotin:Aha-DADPS@M 922.465403 923.2022 H(70)C(42)N(8)O(11)S(1)Si(1) 0.0 Chemical derivative 2052 0.0 +NO_SMX_SIMD@C 267.031377 267.2612 H(9)C(10)N(3)O(4)S(1) 0.0 Chemical derivative 746 0.0 +Malonyl@C 86.000394 86.0462 H(2)C(3)O(3) 0.0 Chemical derivative 747 0.0 +Malonyl@S 86.000394 86.0462 H(2)C(3)O(3) 0.0 Chemical derivative 747 0.0 +Malonyl@K 86.000394 86.0462 H(2)C(3)O(3) 0.0 Post-translational 747 0.0 +3sulfo@Any_N-term 183.983029 184.1693 H(4)C(7)O(4)S(1) 0.0 Chemical derivative 748 0.0 +trifluoro@L 53.971735 53.9714 H(-3)F(3) 0.0 Non-standard residue 750 0.0 +TNBS@Any_N-term 210.986535 211.0886 H(1)C(6)N(3)O(6) 0.0 Chemical derivative 751 0.0 +TNBS@K 210.986535 211.0886 H(1)C(6)N(3)O(6) 0.0 Chemical derivative 751 0.0 +Biotin-phenacyl@C 626.263502 626.727 H(38)C(29)N(8)O(6)S(1) 0.0 Chemical derivative 774 0.0 +Biotin-phenacyl@H 626.263502 626.727 H(38)C(29)N(8)O(6)S(1) 0.0 Chemical derivative 774 0.0 +Biotin-phenacyl@S 626.263502 626.727 H(38)C(29)N(8)O(6)S(1) 0.0 Chemical derivative 774 0.0 +BEMAD_C:2H(6)@C 126.062161 126.2071 H(2)2H(6)C(4)O(2)S(1) 0.0 Isotopic label 764 0.0 +lapachenole@C 240.11503 240.297 H(16)C(16)O(2) 0.0 Chemical derivative 771 0.0 +Label:13C(5)@P 5.016774 4.9633 C(-5)13C(5) 0.0 Isotopic label 772 0.0 +maleimide@K 97.016378 97.0721 H(3)C(4)N(1)O(2) 0.0 Chemical derivative 773 0.0 +maleimide@C 97.016378 97.0721 H(3)C(4)N(1)O(2) 0.0 Chemical derivative 773 0.0 +IDEnT@C 214.990469 216.064 H(7)C(9)N(1)O(1)Cl(2) 0.0 Isotopic label 762 0.0 +BEMAD_ST:2H(6)@T 142.039317 142.2727 H(2)2H(6)C(4)O(1)S(2) 0.0 Isotopic label 763 0.0 +BEMAD_ST:2H(6)@S 142.039317 142.2727 H(2)2H(6)C(4)O(1)S(2) 0.0 Isotopic label 763 0.0 +Met-loss@M^Protein_N-term -131.040485 -131.1961 H(-9)C(-5)N(-1)O(-1)S(-1) 0.0 Co-translational 765 0.0 +Met-loss+Acetyl@M^Protein_N-term -89.02992 -89.1594 H(-7)C(-3)N(-1)S(-1) 0.0 Co-translational 766 0.0 +Menadione-HQ@K 172.05243 172.18 H(8)C(11)O(2) 0.0 Chemical derivative 767 0.0 +Menadione-HQ@C 172.05243 172.18 H(8)C(11)O(2) 0.0 Chemical derivative 767 0.0 +Carboxymethyl:13C(2)@C 60.012189 60.0214 H(2)13C(2)O(2) 0.0 Chemical derivative 775 0.0 +NEM:2H(5)@C 130.079062 130.1561 H(2)2H(5)C(6)N(1)O(2) 0.0 Chemical derivative 776 0.0 +Gly-loss+Amide@G^Any_C-term -58.005479 -58.0361 H(-2)C(-2)O(-2) 0.0 Post-translational 822 0.0 +TMPP-Ac@Any_N-term 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Chemical derivative 827 0.0 +TMPP-Ac@K 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Artefact 827 0.0 +TMPP-Ac@Y 572.181134 572.5401 H(33)C(29)O(10)P(1) 0.0 Artefact 827 0.0 +Label:13C(6)+GG@K 120.063056 120.0586 H(6)C(-2)13C(6)N(2)O(2) 0.0 Isotopic label 799 0.0 +Arg->Npo@R 80.985078 81.0297 H(-1)C(3)N(1)O(2) 0.0 Chemical derivative 837 0.0 +Label:2H(4)+Acetyl@K 46.035672 46.0613 H(-2)2H(4)C(2)O(1) 0.0 Isotopic label 834 0.0 +Pentylamine@Q 70.07825 70.1329 H(10)C(5) 0.0 Chemical derivative 801 0.0 +Biotin:Thermo-21345@Q 311.166748 311.4429 H(25)C(15)N(3)O(2)S(1) 0.0 Chemical derivative 800 0.0 +Dihydroxyimidazolidine@R 72.021129 72.0627 H(4)C(3)O(2) 0.0 Multiple 830 0.0 +Xlink:DFDNB@N 163.985807 164.0752 C(6)N(2)O(4) 0.0 Chemical derivative 825 0.0 +Xlink:DFDNB@Q 163.985807 164.0752 C(6)N(2)O(4) 0.0 Chemical derivative 825 0.0 +Xlink:DFDNB@R 163.985807 164.0752 C(6)N(2)O(4) 0.0 Chemical derivative 825 0.0 +Xlink:DFDNB@K 163.985807 164.0752 C(6)N(2)O(4) 0.0 Chemical derivative 825 0.0 +Cy3b-maleimide@C 682.24612 682.7852 H(38)C(37)N(4)O(7)S(1) 0.0 Chemical derivative 821 0.0 +Hex(1)HexNAc(1)@N 365.132196 365.3331 H(23)C(14)N(1)O(10) 365.132196 H(23)C(14)N(1)O(10) N-linked glycosylation 793 0.5 +Hex(1)HexNAc(1)@T 365.132196 365.3331 H(23)C(14)N(1)O(10) 365.132196 H(23)C(14)N(1)O(10) O-linked glycosylation 793 0.5 +Hex(1)HexNAc(1)@S 365.132196 365.3331 H(23)C(14)N(1)O(10) 365.132196 H(23)C(14)N(1)O(10) O-linked glycosylation 793 0.5 +AEC-MAEC:2H(4)@S 63.044462 63.158 H(1)2H(4)C(2)N(1)O(-1)S(1) 0.0 Isotopic label 792 0.0 +AEC-MAEC:2H(4)@T 63.044462 63.158 H(1)2H(4)C(2)N(1)O(-1)S(1) 0.0 Isotopic label 792 0.0 +Xlink:BMOE@C 220.048407 220.1815 H(8)C(10)N(2)O(4) 0.0 Chemical derivative 824 0.0 +Biotin:Thermo-21360@Anywhere 487.246455 487.6134 H(37)C(21)N(5)O(6)S(1) 0.0 Chemical derivative 811 0.0 +Label:13C(6)+Acetyl@K 48.030694 47.9926 H(2)C(-4)13C(6)O(1) 0.0 Isotopic label 835 0.0 +Label:13C(6)15N(2)+Acetyl@K 50.024764 49.9794 H(2)C(-4)13C(6)N(-2)15N(2)O(1) 0.0 Isotopic label 836 0.0 +EQIGG@K 484.228162 484.5035 H(32)C(20)N(6)O(8) 0.0 Other 846 0.0 +cGMP@S 343.031785 343.1895 H(10)C(10)N(5)O(7)P(1) 0.0 Post-translational 849 0.0 +cGMP@C 343.031785 343.1895 H(10)C(10)N(5)O(7)P(1) 0.0 Post-translational 849 0.0 +cGMP+RMP-loss@C 150.041585 150.1182 H(4)C(5)N(5)O(1) 0.0 Post-translational 851 0.0 +cGMP+RMP-loss@S 150.041585 150.1182 H(4)C(5)N(5)O(1) 0.0 Post-translational 851 0.0 +mTRAQ@Y 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 +mTRAQ@Any_N-term 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 +mTRAQ@K 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 +mTRAQ@H 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 +mTRAQ@S 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 +mTRAQ@T 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Isotopic label 888 0.0 +Arg2PG@R 266.057909 266.2482 H(10)C(16)O(4) 0.0 Chemical derivative 848 0.0 +Label:2H(4)+GG@K 118.068034 118.1273 H(2)2H(4)C(4)N(2)O(2) 0.0 Post-translational 853 0.0 +spermine@Q 185.189198 185.3097 H(23)C(10)N(3) 0.0 Chemical derivative 1420 0.0 +Label:13C(1)2H(3)@M 4.022185 4.0111 H(-3)2H(3)C(-1)13C(1) 0.0 Isotopic label 862 0.0 +ZGB@K 758.380841 758.7261 H(53)C(37)N(6)O(6)F(2)S(1)B(1) 0.0 Other 861 0.0 +ZGB@Any_N-term 758.380841 758.7261 H(53)C(37)N(6)O(6)F(2)S(1)B(1) 0.0 Other 861 0.0 +MG-H1@R 54.010565 54.0474 H(2)C(3)O(1) 0.0 Other 859 0.0 +G-H1@R 39.994915 40.0208 C(2)O(1) 0.0 Other 860 0.0 +Label:13C(6)15N(2)+GG@K 122.057126 122.0454 H(6)C(-2)13C(6)15N(2)O(2) 0.0 Isotopic label 864 0.0 +ICPL:13C(6)2H(4)@Any_N-term 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 +ICPL:13C(6)2H(4)@K 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 +ICPL:13C(6)2H(4)@Protein_N-term 115.0667 115.0747 H(-1)2H(4)13C(6)N(1)O(1) 0.0 Isotopic label 866 0.0 +DyLight-maleimide@C 940.1999 941.0762 H(48)C(39)N(4)O(15)S(4) 0.0 Chemical derivative 890 0.0 +mTRAQ:13C(3)15N(1)@S 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 +mTRAQ:13C(3)15N(1)@T 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 +mTRAQ:13C(3)15N(1)@H 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 +mTRAQ:13C(3)15N(1)@Y 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 +mTRAQ:13C(3)15N(1)@Any_N-term 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 +mTRAQ:13C(3)15N(1)@K 144.102063 144.1544 H(12)C(4)13C(3)N(1)15N(1)O(1) 0.0 Isotopic label 889 0.0 +Methyl-PEO12-Maleimide@C 710.383719 710.8073 H(58)C(32)N(2)O(15) 0.0 Chemical derivative 891 0.0 +MDCC@C 383.148121 383.3978 H(21)C(20)N(3)O(5) 0.0 Chemical derivative 887 0.0 +QQQTGG@K 599.266339 599.5942 H(37)C(23)N(9)O(10) 0.0 Other 877 0.0 +QEQTGG@K 600.250354 600.5789 H(36)C(23)N(8)O(11) 0.0 Other 876 0.0 +HydroxymethylOP@K 108.021129 108.0948 H(4)C(6)O(2) 0.0 Other 886 0.0 +Biotin:Thermo-21325@K 695.310118 695.8288 H(45)C(34)N(7)O(7)S(1) 0.0 Chemical derivative 884 0.0 +Label:13C(1)2H(3)+Oxidation@M 20.0171 20.0105 H(-3)2H(3)C(-1)13C(1)O(1) 0.0 Multiple 885 0.0 +shTMTpro@K 313.231019 313.2473 H(25)13C(15)15N(3)O(3) 0.0 Chemical derivative 2050 0.0 +shTMTpro@Protein_N-term 313.231019 313.2473 H(25)13C(15)15N(3)O(3) 0.0 Chemical derivative 2050 0.0 +shTMTpro@Any_N-term 313.231019 313.2473 H(25)13C(15)15N(3)O(3) 0.0 Chemical derivative 2050 0.0 +Biotin-PEG-PRA@M 578.317646 578.6611 H(42)C(26)N(8)O(7) 0.0 Chemical derivative 895 0.0 +Met->Aha@M -4.986324 -5.0794 H(-3)C(-1)N(3)S(-1) 0.0 Non-standard residue 896 0.0 +Label:15N(4)@R 3.98814 3.9736 N(-4)15N(4) 0.0 Isotopic label 897 0.0 +pyrophospho@T 159.932662 159.9598 H(2)O(6)P(2) 176.935402 H(3)O(7)P(2) Post-translational 898 0.5 +pyrophospho@S 159.932662 159.9598 H(2)O(6)P(2) 176.935402 H(3)O(7)P(2) Post-translational 898 0.5 +Met->Hpg@M -21.987721 -22.0702 H(-2)C(1)S(-1) 0.0 Non-standard residue 899 0.0 +4AcAllylGal@C 372.142033 372.3671 H(24)C(17)O(9) 0.0 Chemical derivative 901 0.0 +DimethylArsino@C 103.960719 103.9827 H(5)C(2)As(1) 0.0 Post-translational 902 0.0 +Lys->CamCys@K 31.935685 32.0219 H(-4)C(-1)O(1)S(1) 0.0 Pre-translational 903 0.0 +Phe->CamCys@F 12.962234 13.0204 H(-1)C(-4)N(1)O(1)S(1) 0.0 Pre-translational 904 0.0 +Leu->MetOx@L 33.951335 34.0378 H(-2)C(-1)O(1)S(1) 0.0 Pre-translational 905 0.0 +Lys->MetOx@K 18.940436 19.0232 H(-3)C(-1)N(-1)O(1)S(1) 0.0 Pre-translational 906 0.0 +Galactosyl@Any_N-term 178.047738 178.14 H(10)C(6)O(6) 0.0 Other glycosylation 907 0.0 +Galactosyl@K 178.047738 178.14 H(10)C(6)O(6) 0.0 Other glycosylation 907 0.0 +Xlink:SMCC[321]@C 321.205242 321.4146 H(27)C(17)N(3)O(3) 0.0 Chemical derivative 908 0.0 +Bacillosamine@N 228.111007 228.245 H(16)C(10)N(2)O(4) 228.111007 H(16)C(10)N(2)O(4) N-linked glycosylation 910 0.5 +MTSL@C 184.07961 184.2786 H(14)C(9)N(1)O(1)S(1) 0.0 Chemical derivative 911 0.0 +HNE-BAHAH@H 511.319226 511.7209 H(45)C(25)N(5)O(4)S(1) 0.0 Chemical derivative 912 0.0 +HNE-BAHAH@C 511.319226 511.7209 H(45)C(25)N(5)O(4)S(1) 0.0 Chemical derivative 912 0.0 +HNE-BAHAH@K 511.319226 511.7209 H(45)C(25)N(5)O(4)S(1) 0.0 Chemical derivative 912 0.0 +LTX+Lophotoxin@Y 416.147118 416.4212 H(24)C(22)O(8) 0.0 Post-translational 2039 0.0 +Methylmalonylation@S 100.016044 100.0728 H(4)C(4)O(3) 0.0 Chemical derivative 914 0.0 +AROD@C 820.336015 820.979 H(52)C(35)N(10)O(9)S(2) 0.0 Chemical derivative 938 0.0 +Cys->methylaminoAla@C -2.945522 -3.0238 H(3)C(1)N(1)S(-1) 0.0 Chemical derivative 939 0.0 +Cys->ethylaminoAla@C 11.070128 11.0028 H(5)C(2)N(1)S(-1) 0.0 Chemical derivative 940 0.0 +Label:13C(4)15N(2)+GG@K 120.050417 120.0601 H(6)13C(4)15N(2)O(2) 0.0 Isotopic label 923 0.0 +ethylamino@S 27.047285 27.0684 H(5)C(2)N(1)O(-1) 0.0 Chemical derivative 926 0.0 +ethylamino@T 27.047285 27.0684 H(5)C(2)N(1)O(-1) 0.0 Chemical derivative 926 0.0 +MercaptoEthanol@S 60.003371 60.1182 H(4)C(2)S(1) 0.0 Chemical derivative 928 0.0 +MercaptoEthanol@T 60.003371 60.1182 H(4)C(2)S(1) 0.0 Chemical derivative 928 0.0 +Atto495Maleimide@C 474.250515 474.5747 H(32)C(27)N(5)O(3) 0.0 Chemical derivative 935 0.0 +AMTzHexNAc2@T 502.202341 502.4757 H(30)C(19)N(6)O(10) 0.0 Chemical derivative 934 0.0 +AMTzHexNAc2@S 502.202341 502.4757 H(30)C(19)N(6)O(10) 0.0 Chemical derivative 934 0.0 +AMTzHexNAc2@N 502.202341 502.4757 H(30)C(19)N(6)O(10) 0.0 Chemical derivative 934 0.0 +Ethyl+Deamidated@Q 29.015316 29.0379 H(3)C(2)N(-1)O(1) 0.0 Chemical derivative 931 0.0 +Ethyl+Deamidated@N 29.015316 29.0379 H(3)C(2)N(-1)O(1) 0.0 Chemical derivative 931 0.0 +VFQQQTGG@K 845.403166 845.8991 H(55)C(37)N(11)O(12) 0.0 Other 932 0.0 +VIEVYQEQTGG@K 1203.577168 1204.2859 H(81)C(53)N(13)O(19) 0.0 Other 933 0.0 +Chlorination@W 33.961028 34.4451 H(-1)Cl(1) 0.0 Artefact 936 0.0 +Chlorination@Y 33.961028 34.4451 H(-1)Cl(1) 0.0 Artefact 936 0.0 +dichlorination@C 67.922055 68.8901 H(-2)Cl(2) 0.0 Chemical derivative 937 0.0 +dichlorination@Y 67.922055 68.8901 H(-2)Cl(2) 0.0 Artefact 937 0.0 +DNPS@C 198.981352 199.164 H(3)C(6)N(2)O(4)S(1) 0.0 Chemical derivative 941 0.0 +DNPS@W 198.981352 199.164 H(3)C(6)N(2)O(4)S(1) 0.0 Chemical derivative 941 0.0 +SulfoGMBS@C 458.162391 458.5306 H(26)C(22)N(4)O(5)S(1) 0.0 Other 942 0.0 +DimethylamineGMBS@C 267.158292 267.3241 H(21)C(13)N(3)O(3) 0.0 Chemical derivative 943 0.0 +Label:15N(2)2H(9)@K 11.050561 11.0423 H(-9)2H(9)N(-2)15N(2) 0.0 Isotopic label 944 0.0 +LG-anhydrolactam@Any_N-term 314.188195 314.4186 H(26)C(20)O(3) 0.0 Post-translational 946 0.0 +LG-anhydrolactam@K 314.188195 314.4186 H(26)C(20)O(3) 0.0 Post-translational 946 0.0 +LG-pyrrole@C 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 +LG-pyrrole@Any_N-term 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 +LG-pyrrole@K 316.203845 316.4345 H(28)C(20)O(3) 0.0 Post-translational 947 0.0 +LG-anhyropyrrole@Any_N-term 298.19328 298.4192 H(26)C(20)O(2) 0.0 Post-translational 948 0.0 +LG-anhyropyrrole@K 298.19328 298.4192 H(26)C(20)O(2) 0.0 Post-translational 948 0.0 +3-deoxyglucosone@R 144.042259 144.1253 H(8)C(6)O(4) 0.0 Multiple 949 0.0 +Cation:Li@D 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 +Cation:Li@E 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 +Cation:Li@Any_C-term 6.008178 5.9331 H(-1)Li(1) 0.0 Artefact 950 0.0 +Cation:Ca[II]@Any_C-term 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 +Cation:Ca[II]@E 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 +Cation:Ca[II]@D 37.946941 38.0621 H(-2)Ca(1) 0.0 Artefact 951 0.0 +Cation:Fe[II]@D 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 +Cation:Fe[II]@E 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 +Cation:Fe[II]@Any_C-term 53.919289 53.8291 H(-2)Fe(1) 0.0 Artefact 952 0.0 +Cation:Ni[II]@D 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 +Cation:Ni[II]@E 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 +Cation:Ni[II]@Any_C-term 55.919696 56.6775 H(-2)Ni(1) 0.0 Artefact 953 0.0 +Cation:Zn[II]@Any_C-term 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 +Cation:Zn[II]@E 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 +Cation:Zn[II]@D 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 +Cation:Zn[II]@H 61.913495 63.3931 H(-2)Zn(1) 0.0 Artefact 954 0.0 +Cation:Ag@D 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 +Cation:Ag@E 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 +Cation:Ag@Any_C-term 105.897267 106.8603 H(-1)Ag(1) 0.0 Artefact 955 0.0 +Cation:Mg[II]@D 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 +Cation:Mg[II]@E 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 +Cation:Mg[II]@Any_C-term 21.969392 22.2891 H(-2)Mg(1) 0.0 Artefact 956 0.0 +2-succinyl@C 116.010959 116.0722 H(4)C(4)O(4) 0.0 Chemical derivative 957 0.0 +Propargylamine@D 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 +Propargylamine@Any_C-term 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 +Propargylamine@E 37.031634 37.0632 H(3)C(3)N(1)O(-1) 0.0 Chemical derivative 958 0.0 +Phosphopropargyl@T 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0.0 +Phosphopropargyl@Y 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0.0 +Phosphopropargyl@S 116.997965 117.0431 H(4)C(3)N(1)O(2)P(1) 0.0 Multiple 959 0.0 +SUMO2135@K 2135.920496 2137.2343 H(137)C(90)N(21)O(37)S(1) 0.0 Other 960 0.0 +SUMO3549@K 3549.536568 3551.6672 H(224)C(150)N(38)O(60)S(1) 0.0 Other 961 0.0 +serotonylation@Q 159.068414 159.1846 H(9)C(10)N(1)O(1) 0.0 Post-translational 1992 0.0 +BITC@Any_N-term 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 +BITC@K 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 +BITC@C 149.02992 149.2129 H(7)C(8)N(1)S(1) 0.0 Chemical derivative 978 0.0 +Carbofuran@S 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 977 0.0 +PEITC@Any_N-term 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 +PEITC@K 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 +PEITC@C 163.04557 163.2395 H(9)C(9)N(1)S(1) 0.0 Chemical derivative 979 0.0 +thioacylPA@K 159.035399 159.2062 H(9)C(6)N(1)O(2)S(1) 0.0 Chemical derivative 967 0.0 +maleimide3@K 969.366232 969.8975 H(59)C(37)N(7)O(23) 0.0 Post-translational 971 0.0 +maleimide3@C 969.366232 969.8975 H(59)C(37)N(7)O(23) 0.0 Post-translational 971 0.0 +maleimide5@K 1293.471879 1294.1787 H(79)C(49)N(7)O(33) 0.0 Post-translational 972 0.0 +maleimide5@C 1293.471879 1294.1787 H(79)C(49)N(7)O(33) 0.0 Post-translational 972 0.0 +Puromycin@Any_C-term 453.212452 453.4943 H(27)C(22)N(7)O(4) 0.0 Co-translational 973 0.0 +glucosone@R 160.037173 160.1247 H(8)C(6)O(5) 0.0 Other 981 0.0 +Label:13C(6)+Dimethyl@K 34.051429 34.0091 H(4)C(-4)13C(6) 0.0 Isotopic label 986 0.0 +cysTMT@C 299.166748 299.4322 H(25)C(14)N(3)O(2)S(1) 0.0 Chemical derivative 984 0.0 +cysTMT6plex@C 304.177202 304.3962 H(25)C(10)13C(4)N(2)15N(1)O(2)S(1) 0.0 Isotopic label 985 0.0 +ISD_z+2_ion@Any_N-term -15.010899 -15.0146 H(-1)N(-1) 0.0 Artefact 991 0.0 +Ammonium@E 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 +Ammonium@D 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 +Ammonium@Any_C-term 17.026549 17.0305 H(3)N(1) 0.0 Artefact 989 0.0 +Biotin:Sigma-B1267@C 449.17329 449.5239 H(27)C(20)N(5)O(5)S(1) 0.0 Chemical derivative 993 0.0 +Label:15N(1)@M 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@E 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@D 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@L 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@I 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@C 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@T 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@V 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@P 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@S 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@A 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@G 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@Y 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(1)@F 0.997035 0.9934 N(-1)15N(1) 0.0 Isotopic label 994 0.0 +Label:15N(2)@W 1.99407 1.9868 N(-2)15N(2) 0.0 Isotopic label 995 0.0 +Label:15N(2)@K 1.99407 1.9868 N(-2)15N(2) 0.0 Isotopic label 995 0.0 +Label:15N(2)@Q 1.99407 1.9868 N(-2)15N(2) 0.0 Isotopic label 995 0.0 +Label:15N(2)@N 1.99407 1.9868 N(-2)15N(2) 0.0 Isotopic label 995 0.0 +Label:15N(3)@H 2.991105 2.9802 N(-3)15N(3) 0.0 Isotopic label 996 0.0 +sulfo+amino@Y 94.967714 95.0778 H(1)N(1)O(3)S(1) 0.0 Chemical derivative 997 0.0 +AHA-Alkyne@M 107.077339 107.0504 H(5)C(4)N(5)O(1)S(-1) 0.0 Chemical derivative 1000 0.0 +AHA-Alkyne-KDDDD@M 695.280074 695.5723 H(37)C(26)N(11)O(14)S(-1) 0.0 Chemical derivative 1001 0.0 +EGCG1@C 456.069261 456.3558 H(16)C(22)O(11) 0.0 Post-translational 1002 0.0 +EGCG2@C 287.055563 287.2442 H(11)C(15)O(6) 0.0 Post-translational 1003 0.0 +Label:13C(6)15N(4)+Methyl@R 24.023919 23.9561 H(2)C(-5)13C(6)N(-4)15N(4) 0.0 Isotopic label 1004 0.0 +Label:13C(6)15N(4)+Dimethyl@R 38.039569 37.9827 H(4)C(-4)13C(6)N(-4)15N(4) 0.0 Isotopic label 1005 0.0 +Label:13C(6)15N(4)+Methyl:2H(3)13C(1)@R 28.046104 27.9673 H(-1)2H(3)C(-6)13C(7)N(-4)15N(4) 0.0 Isotopic label 1006 0.0 +Label:13C(6)15N(4)+Dimethyl:2H(6)13C(2)@R 46.083939 46.005 H(-2)2H(6)C(-6)13C(8)N(-4)15N(4) 0.0 Isotopic label 1007 0.0 +Cys->CamSec@C 104.965913 103.9463 H(3)C(2)N(1)O(1)S(-1)Se(1) 0.0 Non-standard residue 1008 0.0 +Thiazolidine@W 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +Thiazolidine@Y 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +Thiazolidine@H 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +Thiazolidine@R 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +Thiazolidine@K 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +Thiazolidine@Protein_N-term 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +Thiazolidine@C 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +Thiazolidine@F 12.0 12.0107 C(1) 0.0 Chemical derivative 1009 0.0 +DEDGFLYMVYASQETFG@K 1970.824411 1972.088 H(122)C(89)N(18)O(31)S(1) 18.010565 H(2)O(1) Post-translational 1010 0.5 +Biotin:Invitrogen-M1602@C 523.210069 523.6024 H(33)C(23)N(5)O(7)S(1) 0.0 Chemical derivative 1012 0.0 +Xlink:DSS[156]@K 156.078644 156.1791 H(12)C(8)O(3) 0.0 Chemical derivative 1020 0.0 +Xlink:DSS[156]@Protein_N-term 156.078644 156.1791 H(12)C(8)O(3) 0.0 Chemical derivative 1020 0.0 +DMPO@H 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 +DMPO@Y 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 +DMPO@C 111.068414 111.1418 H(9)C(6)N(1)O(1) 0.0 Post-translational 1017 0.0 +glycidamide@K 87.032028 87.0773 H(5)C(3)N(1)O(2) 0.0 Chemical derivative 1014 0.0 +glycidamide@Any_N-term 87.032028 87.0773 H(5)C(3)N(1)O(2) 0.0 Chemical derivative 1014 0.0 +Ahx2+Hsl@Any_C-term 309.205242 309.4039 H(27)C(16)N(3)O(3) 0.0 Non-standard residue 1015 0.0 +ICDID@C 138.06808 138.1638 H(10)C(8)O(2) 0.0 Isotopic label 1018 0.0 +ICDID:2H(6)@C 144.10574 144.2008 H(4)2H(6)C(8)O(2) 0.0 Isotopic label 1019 0.0 +Xlink:EGS[244]@Protein_N-term 244.058303 244.1981 H(12)C(10)O(7) 0.0 Chemical derivative 1021 0.0 +Xlink:EGS[244]@K 244.058303 244.1981 H(12)C(10)O(7) 0.0 Chemical derivative 1021 0.0 +Xlink:DST[132]@Protein_N-term 132.005873 132.0716 H(4)C(4)O(5) 0.0 Chemical derivative 1022 0.0 +Xlink:DST[132]@K 132.005873 132.0716 H(4)C(4)O(5) 0.0 Chemical derivative 1022 0.0 +Xlink:DTSSP[192]@Protein_N-term 191.991486 192.2559 H(8)C(6)O(3)S(2) 0.0 Chemical derivative 1023 0.0 +Xlink:DTSSP[192]@K 191.991486 192.2559 H(8)C(6)O(3)S(2) 0.0 Chemical derivative 1023 0.0 +Xlink:SMCC[237]@C 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 +Xlink:SMCC[237]@K 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 +Xlink:SMCC[237]@Protein_N-term 237.100108 237.2518 H(15)C(12)N(1)O(4) 0.0 Chemical derivative 1024 0.0 +2-nitrobenzyl@Y 135.032028 135.1201 H(5)C(7)N(1)O(2) 0.0 Chemical derivative 1032 0.0 +Xlink:DMP[140]@Protein_N-term 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Chemical derivative 1027 0.0 +Xlink:DMP[140]@K 140.094963 140.183 H(12)C(7)N(2)O(1) 0.0 Chemical derivative 1027 0.0 +Xlink:EGS[115]@Protein_N-term 115.026943 115.0874 H(5)C(4)N(1)O(3) 0.0 Chemical derivative 1028 0.0 +Xlink:EGS[115]@K 115.026943 115.0874 H(5)C(4)N(1)O(3) 0.0 Chemical derivative 1028 0.0 +Cys->SecNEM@C 172.992127 172.0203 H(7)C(6)N(1)O(2)S(-1)Se(1) 0.0 Non-standard residue 1033 0.0 +Cys->SecNEM:2H(5)@C 178.023511 177.0511 H(2)2H(5)C(6)N(1)O(2)S(-1)Se(1) 0.0 Chemical derivative 1034 0.0 +Thiadiazole@C 174.025169 174.2223 H(6)C(9)N(2)S(1) 0.0 Chemical derivative 1035 0.0 +Biotin:Thermo-88310@K 196.121178 196.2462 H(16)C(10)N(2)O(2) 0.0 Chemical derivative 1031 0.0 +TAMRA-FP@Y 659.312423 659.7514 H(46)C(37)N(3)O(6)P(1) 0.0 Chemical derivative 1038 0.0 +TAMRA-FP@S 659.312423 659.7514 H(46)C(37)N(3)O(6)P(1) 0.0 Chemical derivative 1038 0.0 +Biotin:Thermo-21901+H2O@C 543.236284 543.6336 H(37)C(23)N(5)O(8)S(1) 0.0 Chemical derivative 1039 0.0 +Deoxyhypusine@Q 71.073499 71.121 H(9)C(4)N(1) 0.0 Chemical derivative 1041 0.0 +Deoxyhypusine@K 71.073499 71.121 H(9)C(4)N(1) 0.0 Post-translational 1041 0.0 +Acetyldeoxyhypusine@K 113.084064 113.1576 H(11)C(6)N(1)O(1) 0.0 Post-translational 1042 0.0 +Acetylhypusine@K 129.078979 129.157 H(11)C(6)N(1)O(2) 0.0 Post-translational 1043 0.0 +Ala->Cys@A 31.972071 32.065 H(0)C(0)N(0)O(0)S(1) 0.0 AA substitution 1044 0.0 +Ala->Phe@A 76.0313 76.096 H(4)C(6)N(0)O(0)S(0) 0.0 AA substitution 1045 0.0 +Ala->His@A 66.021798 66.0614 H(2)C(3)N(2)O(0)S(0) 0.0 AA substitution 1046 0.0 +Ala->Xle@A 42.04695 42.0797 H(6)C(3) 0.0 AA substitution 1047 0.0 +Ala->Lys@A 57.057849 57.0944 H(7)C(3)N(1)O(0)S(0) 0.0 AA substitution 1048 0.0 +Ala->Met@A 60.003371 60.1182 H(4)C(2)N(0)O(0)S(1) 0.0 AA substitution 1049 0.0 +Ala->Asn@A 43.005814 43.0247 H(1)C(1)N(1)O(1)S(0) 0.0 AA substitution 1050 0.0 +Ala->Gln@A 57.021464 57.0513 H(3)C(2)N(1)O(1)S(0) 0.0 AA substitution 1051 0.0 +Ala->Arg@A 85.063997 85.1078 H(7)C(3)N(3)O(0)S(0) 0.0 AA substitution 1052 0.0 +Ala->Trp@A 115.042199 115.132 H(5)C(8)N(1)O(0)S(0) 0.0 AA substitution 1053 0.0 +Ala->Tyr@A 92.026215 92.0954 H(4)C(6)N(0)O(1)S(0) 0.0 AA substitution 1054 0.0 +Cys->Ala@C -31.972071 -32.065 H(0)C(0)N(0)O(0)S(-1) 0.0 AA substitution 1055 0.0 +Cys->Asp@C 12.017759 11.9445 H(0)C(1)N(0)O(2)S(-1) 0.0 AA substitution 1056 0.0 +Cys->Glu@C 26.033409 25.9711 H(2)C(2)N(0)O(2)S(-1) 0.0 AA substitution 1057 0.0 +Cys->His@C 34.049727 33.9964 H(2)C(3)N(2)O(0)S(-1) 0.0 AA substitution 1058 0.0 +Cys->Xle@C 10.07488 10.0147 H(6)C(3)S(-1) 0.0 AA substitution 1059 0.0 +Cys->Lys@C 25.085779 25.0294 H(7)C(3)N(1)O(0)S(-1) 0.0 AA substitution 1060 0.0 +Cys->Met@C 28.0313 28.0532 H(4)C(2)N(0)O(0)S(0) 0.0 AA substitution 1061 0.0 +Cys->Asn@C 11.033743 10.9597 H(1)C(1)N(1)O(1)S(-1) 0.0 AA substitution 1062 0.0 +Cys->Pro@C -5.956421 -6.0277 H(2)C(2)N(0)O(0)S(-1) 0.0 AA substitution 1063 0.0 +Cys->Gln@C 25.049393 24.9863 H(3)C(2)N(1)O(1)S(-1) 0.0 AA substitution 1064 0.0 +Cys->Thr@C -1.961506 -2.039 H(2)C(1)N(0)O(1)S(-1) 0.0 AA substitution 1065 0.0 +Cys->Val@C -3.940771 -4.0118 H(4)C(2)N(0)O(0)S(-1) 0.0 AA substitution 1066 0.0 +Asp->Cys@D -12.017759 -11.9445 H(0)C(-1)N(0)O(-2)S(1) 0.0 AA substitution 1067 0.0 +Asp->Phe@D 32.041471 32.0865 H(4)C(5)N(0)O(-2)S(0) 0.0 AA substitution 1068 0.0 +Asp->Xle@D -1.942879 -1.9298 H(6)C(2)O(-2) 0.0 AA substitution 1069 0.0 +Asp->Lys@D 13.06802 13.0849 H(7)C(2)N(1)O(-2)S(0) 0.0 AA substitution 1070 0.0 +Asp->Met@D 16.013542 16.1087 H(4)C(1)N(0)O(-2)S(1) 0.0 AA substitution 1071 0.0 +Asp->Pro@D -17.974179 -17.9722 H(2)C(1)N(0)O(-2)S(0) 0.0 AA substitution 1072 0.0 +Asp->Gln@D 13.031634 13.0418 H(3)C(1)N(1)O(-1)S(0) 0.0 AA substitution 1073 0.0 +Asp->Arg@D 41.074168 41.0983 H(7)C(2)N(3)O(-2)S(0) 0.0 AA substitution 1074 0.0 +Asp->Ser@D -27.994915 -28.0101 H(0)C(-1)N(0)O(-1)S(0) 0.0 AA substitution 1075 0.0 +Asp->Thr@D -13.979265 -13.9835 H(2)C(0)N(0)O(-1)S(0) 0.0 AA substitution 1076 0.0 +Asp->Trp@D 71.05237 71.1225 H(5)C(7)N(1)O(-2)S(0) 0.0 AA substitution 1077 0.0 +Glu->Cys@E -26.033409 -25.9711 H(-2)C(-2)N(0)O(-2)S(1) 0.0 AA substitution 1078 0.0 +Glu->Phe@E 18.025821 18.0599 H(2)C(4)N(0)O(-2)S(0) 0.0 AA substitution 1079 0.0 +Glu->His@E 8.016319 8.0253 H(0)C(1)N(2)O(-2)S(0) 0.0 AA substitution 1080 0.0 +Glu->Xle@E -15.958529 -15.9563 H(4)C(1)O(-2) 0.0 AA substitution 1081 0.0 +Glu->Met@E 1.997892 2.0821 H(2)C(0)N(0)O(-2)S(1) 0.0 AA substitution 1082 0.0 +Glu->Asn@E -14.999666 -15.0113 H(-1)C(-1)N(1)O(-1)S(0) 0.0 AA substitution 1083 0.0 +Glu->Pro@E -31.989829 -31.9988 H(0)C(0)N(0)O(-2)S(0) 0.0 AA substitution 1084 0.0 +Glu->Arg@E 27.058518 27.0717 H(5)C(1)N(3)O(-2)S(0) 0.0 AA substitution 1085 0.0 +Glu->Ser@E -42.010565 -42.0367 H(-2)C(-2)N(0)O(-1)S(0) 0.0 AA substitution 1086 0.0 +Glu->Thr@E -27.994915 -28.0101 H(0)C(-1)N(0)O(-1)S(0) 0.0 AA substitution 1087 0.0 +Glu->Trp@E 57.03672 57.0959 H(3)C(6)N(1)O(-2)S(0) 0.0 AA substitution 1088 0.0 +Glu->Tyr@E 34.020735 34.0593 H(2)C(4)N(0)O(-1)S(0) 0.0 AA substitution 1089 0.0 +Phe->Ala@F -76.0313 -76.096 H(-4)C(-6)N(0)O(0)S(0) 0.0 AA substitution 1090 0.0 +Phe->Asp@F -32.041471 -32.0865 H(-4)C(-5)N(0)O(2)S(0) 0.0 AA substitution 1091 0.0 +Phe->Glu@F -18.025821 -18.0599 H(-2)C(-4)N(0)O(2)S(0) 0.0 AA substitution 1092 0.0 +Phe->Gly@F -90.04695 -90.1225 H(-6)C(-7)N(0)O(0)S(0) 0.0 AA substitution 1093 0.0 +Phe->His@F -10.009502 -10.0346 H(-2)C(-3)N(2)O(0)S(0) 0.0 AA substitution 1094 0.0 +Phe->Lys@F -18.973451 -19.0016 H(3)C(-3)N(1)O(0)S(0) 0.0 AA substitution 1095 0.0 +Phe->Met@F -16.027929 -15.9778 H(0)C(-4)N(0)O(0)S(1) 0.0 AA substitution 1096 0.0 +Phe->Asn@F -33.025486 -33.0712 H(-3)C(-5)N(1)O(1)S(0) 0.0 AA substitution 1097 0.0 +Phe->Pro@F -50.01565 -50.0587 H(-2)C(-4)N(0)O(0)S(0) 0.0 AA substitution 1098 0.0 +Phe->Gln@F -19.009836 -19.0446 H(-1)C(-4)N(1)O(1)S(0) 0.0 AA substitution 1099 0.0 +Phe->Arg@F 9.032697 9.0118 H(3)C(-3)N(3)O(0)S(0) 0.0 AA substitution 1100 0.0 +Phe->Thr@F -46.020735 -46.07 H(-2)C(-5)N(0)O(1)S(0) 0.0 AA substitution 1101 0.0 +Phe->Trp@F 39.010899 39.036 H(1)C(2)N(1)O(0)S(0) 0.0 AA substitution 1102 0.0 +Gly->Phe@G 90.04695 90.1225 H(6)C(7)N(0)O(0)S(0) 0.0 AA substitution 1103 0.0 +Gly->His@G 80.037448 80.088 H(4)C(4)N(2)O(0)S(0) 0.0 AA substitution 1104 0.0 +Gly->Xle@G 56.0626 56.1063 H(8)C(4) 0.0 AA substitution 1105 0.0 +Gly->Lys@G 71.073499 71.121 H(9)C(4)N(1)O(0)S(0) 0.0 AA substitution 1106 0.0 +Gly->Met@G 74.019021 74.1447 H(6)C(3)N(0)O(0)S(1) 0.0 AA substitution 1107 0.0 +Gly->Asn@G 57.021464 57.0513 H(3)C(2)N(1)O(1)S(0) 0.0 AA substitution 1108 0.0 +Gly->Pro@G 40.0313 40.0639 H(4)C(3)N(0)O(0)S(0) 0.0 AA substitution 1109 0.0 +Gly->Gln@G 71.037114 71.0779 H(5)C(3)N(1)O(1)S(0) 0.0 AA substitution 1110 0.0 +Gly->Thr@G 44.026215 44.0526 H(4)C(2)N(0)O(1)S(0) 0.0 AA substitution 1111 0.0 +Gly->Tyr@G 106.041865 106.1219 H(6)C(7)N(0)O(1)S(0) 0.0 AA substitution 1112 0.0 +His->Ala@H -66.021798 -66.0614 H(-2)C(-3)N(-2)O(0)S(0) 0.0 AA substitution 1113 0.0 +His->Cys@H -34.049727 -33.9964 H(-2)C(-3)N(-2)O(0)S(1) 0.0 AA substitution 1114 0.0 +His->Glu@H -8.016319 -8.0253 H(0)C(-1)N(-2)O(2)S(0) 0.0 AA substitution 1115 0.0 +His->Phe@H 10.009502 10.0346 H(2)C(3)N(-2)O(0)S(0) 0.0 AA substitution 1116 0.0 +His->Gly@H -80.037448 -80.088 H(-4)C(-4)N(-2)O(0)S(0) 0.0 AA substitution 1117 0.0 +His->Lys@H -8.963949 -8.967 H(5)C(0)N(-1)O(0)S(0) 0.0 AA substitution 1119 0.0 +His->Met@H -6.018427 -5.9432 H(2)C(-1)N(-2)O(0)S(1) 0.0 AA substitution 1120 0.0 +His->Ser@H -50.026883 -50.062 H(-2)C(-3)N(-2)O(1)S(0) 0.0 AA substitution 1121 0.0 +His->Thr@H -36.011233 -36.0354 H(0)C(-2)N(-2)O(1)S(0) 0.0 AA substitution 1122 0.0 +His->Val@H -37.990498 -38.0082 H(2)C(-1)N(-2)O(0)S(0) 0.0 AA substitution 1123 0.0 +His->Trp@H 49.020401 49.0706 H(3)C(5)N(-1)O(0)S(0) 0.0 AA substitution 1124 0.0 +Xle->Cys@L -10.07488 -10.0147 H(-6)C(-3)N(0)O(0)S(1) 0.0 AA substitution 1126 0.0 +Xle->Cys@I -10.07488 -10.0147 H(-6)C(-3)N(0)O(0)S(1) 0.0 AA substitution 1126 0.0 +Xle->Asp@L 1.942879 1.9298 H(-6)C(-2)N(0)O(2)S(0) 0.0 AA substitution 1127 0.0 +Xle->Asp@I 1.942879 1.9298 H(-6)C(-2)N(0)O(2)S(0) 0.0 AA substitution 1127 0.0 +Xle->Glu@L 15.958529 15.9563 H(-4)C(-1)N(0)O(2)S(0) 0.0 AA substitution 1128 0.0 +Xle->Glu@I 15.958529 15.9563 H(-4)C(-1)N(0)O(2)S(0) 0.0 AA substitution 1128 0.0 +Xle->Gly@L -56.0626 -56.1063 H(-8)C(-4)N(0)O(0)S(0) 0.0 AA substitution 1129 0.0 +Xle->Gly@I -56.0626 -56.1063 H(-8)C(-4)N(0)O(0)S(0) 0.0 AA substitution 1129 0.0 +Xle->Tyr@L 49.979265 50.0156 H(-2)C(3)N(0)O(1)S(0) 0.0 AA substitution 1130 0.0 +Xle->Tyr@I 49.979265 50.0156 H(-2)C(3)N(0)O(1)S(0) 0.0 AA substitution 1130 0.0 +Lys->Ala@K -57.057849 -57.0944 H(-7)C(-3)N(-1)O(0)S(0) 0.0 AA substitution 1131 0.0 +Lys->Cys@K -25.085779 -25.0294 H(-7)C(-3)N(-1)O(0)S(1) 0.0 AA substitution 1132 0.0 +Lys->Asp@K -13.06802 -13.0849 H(-7)C(-2)N(-1)O(2)S(0) 0.0 AA substitution 1133 0.0 +Lys->Phe@K 18.973451 19.0016 H(-3)C(3)N(-1)O(0)S(0) 0.0 AA substitution 1134 0.0 +Lys->Gly@K -71.073499 -71.121 H(-9)C(-4)N(-1)O(0)S(0) 0.0 AA substitution 1135 0.0 +Lys->His@K 8.963949 8.967 H(-5)C(0)N(1)O(0)S(0) 0.0 AA substitution 1136 0.0 +Lys->Pro@K -31.042199 -31.0571 H(-5)C(-1)N(-1)O(0)S(0) 0.0 AA substitution 1137 0.0 +Lys->Ser@K -41.062935 -41.095 H(-7)C(-3)N(-1)O(1)S(0) 0.0 AA substitution 1138 0.0 +Lys->Val@K -29.026549 -29.0412 H(-3)C(-1)N(-1)O(0)S(0) 0.0 AA substitution 1139 0.0 +Lys->Trp@K 57.98435 58.0376 H(-2)C(5)N(0)O(0)S(0) 0.0 AA substitution 1140 0.0 +Lys->Tyr@K 34.968366 35.001 H(-3)C(3)N(-1)O(1)S(0) 0.0 AA substitution 1141 0.0 +Met->Ala@M -60.003371 -60.1182 H(-4)C(-2)N(0)O(0)S(-1) 0.0 AA substitution 1142 0.0 +Met->Cys@M -28.0313 -28.0532 H(-4)C(-2)N(0)O(0)S(0) 0.0 AA substitution 1143 0.0 +Met->Asp@M -16.013542 -16.1087 H(-4)C(-1)N(0)O(2)S(-1) 0.0 AA substitution 1144 0.0 +Met->Glu@M -1.997892 -2.0821 H(-2)C(0)N(0)O(2)S(-1) 0.0 AA substitution 1145 0.0 +Met->Phe@M 16.027929 15.9778 H(0)C(4)N(0)O(0)S(-1) 0.0 AA substitution 1146 0.0 +Met->Gly@M -74.019021 -74.1447 H(-6)C(-3)N(0)O(0)S(-1) 0.0 AA substitution 1147 0.0 +Met->His@M 6.018427 5.9432 H(-2)C(1)N(2)O(0)S(-1) 0.0 AA substitution 1148 0.0 +Met->Asn@M -16.997557 -17.0934 H(-3)C(-1)N(1)O(1)S(-1) 0.0 AA substitution 1149 0.0 +Met->Pro@M -33.987721 -34.0809 H(-2)C(0)N(0)O(0)S(-1) 0.0 AA substitution 1150 0.0 +Met->Gln@M -2.981907 -3.0668 H(-1)C(0)N(1)O(1)S(-1) 0.0 AA substitution 1151 0.0 +Met->Ser@M -44.008456 -44.1188 H(-4)C(-2)N(0)O(1)S(-1) 0.0 AA substitution 1152 0.0 +Met->Trp@M 55.038828 55.0138 H(1)C(6)N(1)O(0)S(-1) 0.0 AA substitution 1153 0.0 +Met->Tyr@M 32.022844 31.9772 H(0)C(4)N(0)O(1)S(-1) 0.0 AA substitution 1154 0.0 +Asn->Ala@N -43.005814 -43.0247 H(-1)C(-1)N(-1)O(-1)S(0) 0.0 AA substitution 1155 0.0 +Asn->Cys@N -11.033743 -10.9597 H(-1)C(-1)N(-1)O(-1)S(1) 0.0 AA substitution 1156 0.0 +Asn->Glu@N 14.999666 15.0113 H(1)C(1)N(-1)O(1)S(0) 0.0 AA substitution 1157 0.0 +Asn->Phe@N 33.025486 33.0712 H(3)C(5)N(-1)O(-1)S(0) 0.0 AA substitution 1158 0.0 +Asn->Gly@N -57.021464 -57.0513 H(-3)C(-2)N(-1)O(-1)S(0) 0.0 AA substitution 1159 0.0 +Asn->Met@N 16.997557 17.0934 H(3)C(1)N(-1)O(-1)S(1) 0.0 AA substitution 1160 0.0 +Asn->Pro@N -16.990164 -16.9875 H(1)C(1)N(-1)O(-1)S(0) 0.0 AA substitution 1161 0.0 +Asn->Gln@N 14.01565 14.0266 H(2)C(1)N(0)O(0)S(0) 0.0 AA substitution 1162 0.0 +Asn->Arg@N 42.058184 42.083 H(6)C(2)N(2)O(-1)S(0) 0.0 AA substitution 1163 0.0 +Asn->Val@N -14.974514 -14.9716 H(3)C(1)N(-1)O(-1)S(0) 0.0 AA substitution 1164 0.0 +Asn->Trp@N 72.036386 72.1073 H(4)C(7)N(0)O(-1)S(0) 0.0 AA substitution 1165 0.0 +Pro->Cys@P 5.956421 6.0277 H(-2)C(-2)N(0)O(0)S(1) 0.0 AA substitution 1166 0.0 +Pro->Asp@P 17.974179 17.9722 H(-2)C(-1)N(0)O(2)S(0) 0.0 AA substitution 1167 0.0 +Pro->Glu@P 31.989829 31.9988 H(0)C(0)N(0)O(2)S(0) 0.0 AA substitution 1168 0.0 +Pro->Phe@P 50.01565 50.0587 H(2)C(4)N(0)O(0)S(0) 0.0 AA substitution 1169 0.0 +Pro->Gly@P -40.0313 -40.0639 H(-4)C(-3)N(0)O(0)S(0) 0.0 AA substitution 1170 0.0 +Pro->Lys@P 31.042199 31.0571 H(5)C(1)N(1)O(0)S(0) 0.0 AA substitution 1171 0.0 +Pro->Met@P 33.987721 34.0809 H(2)C(0)N(0)O(0)S(1) 0.0 AA substitution 1172 0.0 +Pro->Asn@P 16.990164 16.9875 H(-1)C(-1)N(1)O(1)S(0) 0.0 AA substitution 1173 0.0 +Pro->Val@P 2.01565 2.0159 H(2)C(0)N(0)O(0)S(0) 0.0 AA substitution 1174 0.0 +Pro->Trp@P 89.026549 89.0947 H(3)C(6)N(1)O(0)S(0) 0.0 AA substitution 1175 0.0 +Pro->Tyr@P 66.010565 66.0581 H(2)C(4)N(0)O(1)S(0) 0.0 AA substitution 1176 0.0 +Gln->Ala@Q -57.021464 -57.0513 H(-3)C(-2)N(-1)O(-1)S(0) 0.0 AA substitution 1177 0.0 +Gln->Cys@Q -25.049393 -24.9863 H(-3)C(-2)N(-1)O(-1)S(1) 0.0 AA substitution 1178 0.0 +Gln->Asp@Q -13.031634 -13.0418 H(-3)C(-1)N(-1)O(1)S(0) 0.0 AA substitution 1179 0.0 +Gln->Phe@Q 19.009836 19.0446 H(1)C(4)N(-1)O(-1)S(0) 0.0 AA substitution 1180 0.0 +Gln->Gly@Q -71.037114 -71.0779 H(-5)C(-3)N(-1)O(-1)S(0) 0.0 AA substitution 1181 0.0 +Gln->Met@Q 2.981907 3.0668 H(1)C(0)N(-1)O(-1)S(1) 0.0 AA substitution 1182 0.0 +Gln->Asn@Q -14.01565 -14.0266 H(-2)C(-1)N(0)O(0)S(0) 0.0 AA substitution 1183 0.0 +Gln->Ser@Q -41.026549 -41.0519 H(-3)C(-2)N(-1)O(0)S(0) 0.0 AA substitution 1184 0.0 +Gln->Thr@Q -27.010899 -27.0253 H(-1)C(-1)N(-1)O(0)S(0) 0.0 AA substitution 1185 0.0 +Gln->Val@Q -28.990164 -28.9982 H(1)C(0)N(-1)O(-1)S(0) 0.0 AA substitution 1186 0.0 +Gln->Trp@Q 58.020735 58.0807 H(2)C(6)N(0)O(-1)S(0) 0.0 AA substitution 1187 0.0 +Gln->Tyr@Q 35.004751 35.044 H(1)C(4)N(-1)O(0)S(0) 0.0 AA substitution 1188 0.0 +Arg->Ala@R -85.063997 -85.1078 H(-7)C(-3)N(-3)O(0)S(0) 0.0 AA substitution 1189 0.0 +Arg->Asp@R -41.074168 -41.0983 H(-7)C(-2)N(-3)O(2)S(0) 0.0 AA substitution 1190 0.0 +Arg->Glu@R -27.058518 -27.0717 H(-5)C(-1)N(-3)O(2)S(0) 0.0 AA substitution 1191 0.0 +Arg->Asn@R -42.058184 -42.083 H(-6)C(-2)N(-2)O(1)S(0) 0.0 AA substitution 1192 0.0 +Arg->Val@R -57.032697 -57.0546 H(-3)C(-1)N(-3)O(0)S(0) 0.0 AA substitution 1193 0.0 +Arg->Tyr@R 6.962218 6.9876 H(-3)C(3)N(-3)O(1)S(0) 0.0 AA substitution 1194 0.0 +Arg->Phe@R -9.032697 -9.0118 H(-3)C(3)N(-3) 0.0 AA substitution 1195 0.0 +Ser->Asp@S 27.994915 28.0101 H(0)C(1)N(0)O(1)S(0) 0.0 AA substitution 1196 0.0 +Ser->Glu@S 42.010565 42.0367 H(2)C(2)N(0)O(1)S(0) 0.0 AA substitution 1197 0.0 +Ser->His@S 50.026883 50.062 H(2)C(3)N(2)O(-1)S(0) 0.0 AA substitution 1198 0.0 +Ser->Lys@S 41.062935 41.095 H(7)C(3)N(1)O(-1)S(0) 0.0 AA substitution 1199 0.0 +Ser->Met@S 44.008456 44.1188 H(4)C(2)N(0)O(-1)S(1) 0.0 AA substitution 1200 0.0 +Ser->Gln@S 41.026549 41.0519 H(3)C(2)N(1)O(0)S(0) 0.0 AA substitution 1201 0.0 +Ser->Val@S 12.036386 12.0538 H(4)C(2)N(0)O(-1)S(0) 0.0 AA substitution 1202 0.0 +Thr->Cys@T 1.961506 2.039 H(-2)C(-1)N(0)O(-1)S(1) 0.0 AA substitution 1203 0.0 +Thr->Asp@T 13.979265 13.9835 H(-2)C(0)N(0)O(1)S(0) 0.0 AA substitution 1204 0.0 +Thr->Glu@T 27.994915 28.0101 H(0)C(1)N(0)O(1)S(0) 0.0 AA substitution 1205 0.0 +Thr->Phe@T 46.020735 46.07 H(2)C(5)N(0)O(-1)S(0) 0.0 AA substitution 1206 0.0 +Thr->Gly@T -44.026215 -44.0526 H(-4)C(-2)N(0)O(-1)S(0) 0.0 AA substitution 1207 0.0 +Thr->His@T 36.011233 36.0354 H(0)C(2)N(2)O(-1)S(0) 0.0 AA substitution 1208 0.0 +Thr->Gln@T 27.010899 27.0253 H(1)C(1)N(1)O(0)S(0) 0.0 AA substitution 1209 0.0 +Thr->Val@T -1.979265 -1.9728 H(2)C(1)N(0)O(-1)S(0) 0.0 AA substitution 1210 0.0 +Thr->Trp@T 85.031634 85.106 H(3)C(7)N(1)O(-1)S(0) 0.0 AA substitution 1211 0.0 +Thr->Tyr@T 62.01565 62.0694 H(2)C(5)N(0)O(0)S(0) 0.0 AA substitution 1212 0.0 +Val->Cys@V 3.940771 4.0118 H(-4)C(-2)N(0)O(0)S(1) 0.0 AA substitution 1213 0.0 +Val->His@V 37.990498 38.0082 H(-2)C(1)N(2)O(0)S(0) 0.0 AA substitution 1214 0.0 +Val->Lys@V 29.026549 29.0412 H(3)C(1)N(1)O(0)S(0) 0.0 AA substitution 1215 0.0 +Val->Asn@V 14.974514 14.9716 H(-3)C(-1)N(1)O(1)S(0) 0.0 AA substitution 1216 0.0 +Val->Pro@V -2.01565 -2.0159 H(-2)C(0)N(0)O(0)S(0) 0.0 AA substitution 1217 0.0 +Val->Gln@V 28.990164 28.9982 H(-1)C(0)N(1)O(1)S(0) 0.0 AA substitution 1218 0.0 +Val->Arg@V 57.032697 57.0546 H(3)C(1)N(3)O(0)S(0) 0.0 AA substitution 1219 0.0 +Val->Ser@V -12.036386 -12.0538 H(-4)C(-2)N(0)O(1)S(0) 0.0 AA substitution 1220 0.0 +Val->Thr@V 1.979265 1.9728 H(-2)C(-1)N(0)O(1)S(0) 0.0 AA substitution 1221 0.0 +Val->Trp@V 87.010899 87.0788 H(1)C(6)N(1)O(0)S(0) 0.0 AA substitution 1222 0.0 +Val->Tyr@V 63.994915 64.0422 H(0)C(4)N(0)O(1)S(0) 0.0 AA substitution 1223 0.0 +Trp->Ala@W -115.042199 -115.132 H(-5)C(-8)N(-1)O(0)S(0) 0.0 AA substitution 1224 0.0 +Trp->Asp@W -71.05237 -71.1225 H(-5)C(-7)N(-1)O(2)S(0) 0.0 AA substitution 1225 0.0 +Trp->Glu@W -57.03672 -57.0959 H(-3)C(-6)N(-1)O(2)S(0) 0.0 AA substitution 1226 0.0 +Trp->Phe@W -39.010899 -39.036 H(-1)C(-2)N(-1)O(0)S(0) 0.0 AA substitution 1227 0.0 +Trp->His@W -49.020401 -49.0706 H(-3)C(-5)N(1)O(0)S(0) 0.0 AA substitution 1228 0.0 +Trp->Lys@W -57.98435 -58.0376 H(2)C(-5)N(0)O(0)S(0) 0.0 AA substitution 1229 0.0 +Trp->Met@W -55.038828 -55.0138 H(-1)C(-6)N(-1)O(0)S(1) 0.0 AA substitution 1230 0.0 +Trp->Asn@W -72.036386 -72.1073 H(-4)C(-7)N(0)O(1)S(0) 0.0 AA substitution 1231 0.0 +Trp->Pro@W -89.026549 -89.0947 H(-3)C(-6)N(-1)O(0)S(0) 0.0 AA substitution 1232 0.0 +Trp->Gln@W -58.020735 -58.0807 H(-2)C(-6)N(0)O(1)S(0) 0.0 AA substitution 1233 0.0 +Trp->Thr@W -85.031634 -85.106 H(-3)C(-7)N(-1)O(1)S(0) 0.0 AA substitution 1234 0.0 +Trp->Val@W -87.010899 -87.0788 H(-1)C(-6)N(-1)O(0)S(0) 0.0 AA substitution 1235 0.0 +Trp->Tyr@W -23.015984 -23.0366 H(-1)C(-2)N(-1)O(1)S(0) 0.0 AA substitution 1236 0.0 +Tyr->Ala@Y -92.026215 -92.0954 H(-4)C(-6)N(0)O(-1)S(0) 0.0 AA substitution 1237 0.0 +Tyr->Glu@Y -34.020735 -34.0593 H(-2)C(-4)N(0)O(1)S(0) 0.0 AA substitution 1238 0.0 +Tyr->Gly@Y -106.041865 -106.1219 H(-6)C(-7)N(0)O(-1)S(0) 0.0 AA substitution 1239 0.0 +Tyr->Lys@Y -34.968366 -35.001 H(3)C(-3)N(1)O(-1)S(0) 0.0 AA substitution 1240 0.0 +Tyr->Met@Y -32.022844 -31.9772 H(0)C(-4)N(0)O(-1)S(1) 0.0 AA substitution 1241 0.0 +Tyr->Pro@Y -66.010565 -66.0581 H(-2)C(-4)N(0)O(-1)S(0) 0.0 AA substitution 1242 0.0 +Tyr->Gln@Y -35.004751 -35.044 H(-1)C(-4)N(1)O(0)S(0) 0.0 AA substitution 1243 0.0 +Tyr->Arg@Y -6.962218 -6.9876 H(3)C(-3)N(3)O(-1)S(0) 0.0 AA substitution 1244 0.0 +Tyr->Thr@Y -62.01565 -62.0694 H(-2)C(-5)N(0)O(0)S(0) 0.0 AA substitution 1245 0.0 +Tyr->Val@Y -63.994915 -64.0422 H(0)C(-4)N(0)O(-1)S(0) 0.0 AA substitution 1246 0.0 +Tyr->Trp@Y 23.015984 23.0366 H(1)C(2)N(1)O(-1)S(0) 0.0 AA substitution 1247 0.0 +Tyr->Xle@Y -49.979265 -50.0156 H(2)C(-3)O(-1) 0.0 AA substitution 1248 0.0 +AHA-SS@M 195.075625 195.1787 H(9)C(7)N(5)O(2) 0.0 Multiple 1249 0.0 +AHA-SS_CAM@M 252.097088 252.23 H(12)C(9)N(6)O(3) 0.0 Multiple 1250 0.0 +Biotin:Thermo-33033@Anywhere 548.223945 548.7211 H(36)C(25)N(6)O(4)S(2) 0.0 Chemical derivative 1251 0.0 +Biotin:Thermo-33033-H@Anywhere 546.208295 546.7053 H(34)C(25)N(6)O(4)S(2) 0.0 Chemical derivative 1252 0.0 +2-monomethylsuccinyl@C 130.026609 130.0987 H(6)C(5)O(4) 0.0 Chemical derivative 1253 0.0 +Saligenin@H 106.041865 106.1219 H(6)C(7)O(1) 0.0 Chemical derivative 1254 0.0 +Saligenin@K 106.041865 106.1219 H(6)C(7)O(1) 0.0 Chemical derivative 1254 0.0 +Cresylphosphate@R 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 +Cresylphosphate@S 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 +Cresylphosphate@T 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 +Cresylphosphate@Y 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 +Cresylphosphate@K 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 +Cresylphosphate@H 170.013281 170.1024 H(7)C(7)O(3)P(1) 0.0 Chemical derivative 1255 0.0 +CresylSaligeninPhosphate@R 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 +CresylSaligeninPhosphate@S 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 +CresylSaligeninPhosphate@T 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 +CresylSaligeninPhosphate@Y 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 +CresylSaligeninPhosphate@K 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 +CresylSaligeninPhosphate@H 276.055146 276.2244 H(13)C(14)O(4)P(1) 0.0 Chemical derivative 1256 0.0 +Ub-Br2@C 100.063663 100.1191 H(8)C(4)N(2)O(1) 0.0 Chemical derivative 1257 0.0 +Ub-VME@C 172.084792 172.1818 H(12)C(7)N(2)O(3) 0.0 Chemical derivative 1258 0.0 +Ub-fluorescein@C 597.209772 597.598 H(29)C(31)N(6)O(7) 0.0 Chemical derivative 1261 0.0 +2-dimethylsuccinyl@C 144.042259 144.1253 H(8)C(6)O(4) 0.0 Chemical derivative 1262 0.0 +Gly@T 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 1263 0.0 +Gly@S 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 1263 0.0 +Gly@K 57.021464 57.0513 H(3)C(2)N(1)O(1) 0.0 Chemical derivative 1263 0.0 +pupylation@K 243.085521 243.2166 H(13)C(9)N(3)O(5) 0.0 Post-translational 1264 0.0 +Label:13C(4)@M 4.013419 3.9706 C(-4)13C(4) 0.0 Isotopic label 1266 0.0 +HCysteinyl@C 133.019749 133.1689 H(7)C(4)N(1)O(2)S(1) 0.0 Post-translational 1271 0.0 +Label:13C(4)+Oxidation@M 20.008334 19.97 C(-4)13C(4)O(1) 0.0 Isotopic label 1267 0.0 +UgiJoullie@E 1106.48935 1107.1274 H(60)C(47)N(23)O(10) 0.0 Chemical derivative 1276 0.0 +UgiJoullie@D 1106.48935 1107.1274 H(60)C(47)N(23)O(10) 0.0 Chemical derivative 1276 0.0 +HCysThiolactone@K 117.024835 117.1695 H(7)C(4)N(1)O(1)S(1) 0.0 Post-translational 1270 0.0 +UgiJoullieProGly@D 154.074228 154.1665 H(10)C(7)N(2)O(2) 0.0 Chemical derivative 1282 0.0 +UgiJoullieProGly@E 154.074228 154.1665 H(10)C(7)N(2)O(2) 0.0 Chemical derivative 1282 0.0 +Dipyridyl@C 225.090212 225.2459 H(11)C(13)N(3)O(1) 0.0 Chemical derivative 1277 0.0 +Furan@Y 66.010565 66.0581 H(2)C(4)O(1) 0.0 Chemical derivative 1278 0.0 +Difuran@Y 132.021129 132.1162 H(4)C(8)O(2) 0.0 Chemical derivative 1279 0.0 +BMP-piperidinol@C 263.131014 263.3337 H(17)C(18)N(1)O(1) 0.0 Chemical derivative 1281 0.0 +BMP-piperidinol@M 263.131014 263.3337 H(17)C(18)N(1)O(1) 0.0 Chemical derivative 1281 0.0 +UgiJoullieProGlyProGly@D 308.148455 308.333 H(20)C(14)N(4)O(4) 0.0 Chemical derivative 1283 0.0 +UgiJoullieProGlyProGly@E 308.148455 308.333 H(20)C(14)N(4)O(4) 0.0 Chemical derivative 1283 0.0 +Arg-loss@R^Any_C-term -156.101111 -156.1857 H(-12)C(-6)N(-4)O(-1) 0.0 Other 1287 0.0 +Arg@Any_N-term 156.101111 156.1857 H(12)C(6)N(4)O(1) 0.0 Other 1288 0.0 +IMEHex(2)NeuAc(1)@K 688.199683 688.6527 H(40)C(25)N(2)O(18)S(1) 0.0 Other glycosylation 1286 0.0 +Butyryl@K 70.041865 70.0898 H(6)C(4)O(1) 0.0 Post-translational 1289 0.0 +Dicarbamidomethyl@K 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 +Dicarbamidomethyl@H 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 +Dicarbamidomethyl@C 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 +Dicarbamidomethyl@R 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 +Dicarbamidomethyl@Any_N-term 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Artefact 1290 0.0 +Dimethyl:2H(6)@K 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 +Dimethyl:2H(6)@Any_N-term 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 +Dimethyl:2H(6)@R 34.068961 34.0901 H(-2)2H(6)C(2) 0.0 Isotopic label 1291 0.0 +GGQ@K 242.101505 242.2319 H(14)C(9)N(4)O(4) 0.0 Other 1292 0.0 +QTGG@K 343.149184 343.3357 H(21)C(13)N(5)O(6) 0.0 Other 1293 0.0 +Label:13C(3)15N(1)@A 4.007099 3.9714 C(-3)13C(3)N(-1)15N(1) 0.0 Isotopic label 1297 0.0 +Label:13C(3)15N(1)@S 4.007099 3.9714 C(-3)13C(3)N(-1)15N(1) 0.0 Isotopic label 1297 0.0 +Label:13C(3)@A 3.010064 2.978 C(-3)13C(3) 0.0 Isotopic label 1296 0.0 +Label:13C(4)15N(1)@D 5.010454 4.964 C(-4)13C(4)N(-1)15N(1) 0.0 Isotopic label 1298 0.0 +Label:2H(10)@L 10.062767 10.0616 H(-10)2H(10) 0.0 Isotopic label 1299 0.0 +Label:2H(4)13C(1)@R 5.028462 5.0173 H(-4)2H(4)C(-1)13C(1) 0.0 Isotopic label 1300 0.0 +Lys@Any_N-term 128.094963 128.1723 H(12)C(6)N(2)O(1) 0.0 Other 1301 0.0 +mTRAQ:13C(6)15N(2)@K 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 +mTRAQ:13C(6)15N(2)@Any_N-term 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 +mTRAQ:13C(6)15N(2)@Y 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 +mTRAQ:13C(6)15N(2)@H 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 +mTRAQ:13C(6)15N(2)@S 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 +mTRAQ:13C(6)15N(2)@T 148.109162 148.1257 H(12)C(1)13C(6)15N(2)O(1) 0.0 Isotopic label 1302 0.0 +NeuAc@T 291.095417 291.2546 H(17)C(11)N(1)O(8) 291.095417 H(17)C(11)N(1)O(8) O-linked glycosylation 1303 0.5 +NeuAc@S 291.095417 291.2546 H(17)C(11)N(1)O(8) 291.095417 H(17)C(11)N(1)O(8) O-linked glycosylation 1303 0.5 +NeuAc@N 291.095417 291.2546 H(17)C(11)N(1)O(8) 291.095417 H(17)C(11)N(1)O(8) N-linked glycosylation 1303 0.5 +NeuGc@T 307.090331 307.254 H(17)C(11)N(1)O(9) 307.090331 H(17)C(11)N(1)O(9) O-linked glycosylation 1304 0.5 +NeuGc@S 307.090331 307.254 H(17)C(11)N(1)O(9) 307.090331 H(17)C(11)N(1)O(9) O-linked glycosylation 1304 0.5 +NeuGc@N 307.090331 307.254 H(17)C(11)N(1)O(9) 307.090331 H(17)C(11)N(1)O(9) N-linked glycosylation 1304 0.5 +Propyl@D 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 +Propyl@K 42.04695 42.0797 H(6)C(3) 0.0 Isotopic label 1305 0.0 +Propyl@Any_N-term 42.04695 42.0797 H(6)C(3) 0.0 Isotopic label 1305 0.0 +Propyl@E 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 +Propyl@Any_C-term 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 +Propyl@Protein_C-term 42.04695 42.0797 H(6)C(3) 0.0 Chemical derivative 1305 0.0 +Propyl:2H(6)@Any_N-term 48.084611 48.1167 2H(6)C(3) 0.0 Isotopic label 1306 0.0 +Propyl:2H(6)@K 48.084611 48.1167 2H(6)C(3) 0.0 Isotopic label 1306 0.0 +Propiophenone@C 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 +Propiophenone@W 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 +Propiophenone@T 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 +Propiophenone@S 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 +Propiophenone@R 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 +Propiophenone@K 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 +Propiophenone@H 132.057515 132.1592 H(8)C(9)O(1) 0.0 Chemical derivative 1310 0.0 +PS_Hapten@H 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 +PS_Hapten@C 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 +PS_Hapten@K 120.021129 120.1055 H(4)C(7)O(2) 0.0 Chemical derivative 1345 0.0 +Cy3-maleimide@C 753.262796 753.9046 H(45)C(37)N(4)O(9)S(2) 0.0 Chemical derivative 1348 0.0 +Delta:H(6)C(3)O(1)@Protein_N-term 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 +Delta:H(6)C(3)O(1)@K 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 +Delta:H(6)C(3)O(1)@H 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 +Delta:H(6)C(3)O(1)@C 58.041865 58.0791 H(6)C(3)O(1) 0.0 Chemical derivative 1312 0.0 +Delta:H(8)C(6)O(1)@Protein_N-term 96.057515 96.1271 H(8)C(6)O(1) 0.0 Chemical derivative 1313 0.0 +Delta:H(8)C(6)O(1)@K 96.057515 96.1271 H(8)C(6)O(1) 0.0 Chemical derivative 1313 0.0 +biotinAcrolein298@H 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 +biotinAcrolein298@K 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 +biotinAcrolein298@Protein_N-term 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 +biotinAcrolein298@C 298.146347 298.4044 H(22)C(13)N(4)O(2)S(1) 0.0 Chemical derivative 1314 0.0 +MM-diphenylpentanone@C 265.146664 265.3496 H(19)C(18)N(1)O(1) 0.0 Chemical derivative 1315 0.0 +EHD-diphenylpentanone@M 266.13068 266.3343 H(18)C(18)O(2) 0.0 Chemical derivative 1317 0.0 +EHD-diphenylpentanone@C 266.13068 266.3343 H(18)C(18)O(2) 0.0 Chemical derivative 1317 0.0 +benzylguanidine@K 132.068748 132.1625 H(8)C(8)N(2) 0.0 Chemical derivative 1349 0.0 +CarboxymethylDMAP@Any_N-term 162.079313 162.1885 H(10)C(9)N(2)O(1) 0.0 Chemical derivative 1350 0.0 +Biotin:Thermo-21901+2H2O@C 561.246849 561.6489 H(39)C(23)N(5)O(9)S(1) 0.0 Chemical derivative 1320 0.0 +DiLeu4plex115@K 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 +DiLeu4plex115@Any_N-term 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 +DiLeu4plex115@Y 145.12 145.1966 H(15)C(7)13C(1)15N(1)18O(1) 0.0 Isotopic label 1321 0.0 +DiLeu4plex@Any_N-term 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 +DiLeu4plex@K 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 +DiLeu4plex@Y 145.132163 145.2229 H(13)2H(2)C(8)N(1)18O(1) 0.0 Isotopic label 1322 0.0 +DiLeu4plex117@K 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 +DiLeu4plex117@Any_N-term 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 +DiLeu4plex117@Y 145.128307 145.2092 H(13)2H(2)C(7)13C(1)15N(1)O(1) 0.0 Isotopic label 1323 0.0 +DiLeu4plex118@K 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 +DiLeu4plex118@Any_N-term 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 +DiLeu4plex118@Y 145.140471 145.2354 H(11)2H(4)C(8)N(1)O(1) 0.0 Isotopic label 1324 0.0 +Xlink:BuUrBu[213]@Protein_N-term 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 +Xlink:BuUrBu[213]@S 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 +Xlink:BuUrBu[213]@K 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 +Xlink:BuUrBu[213]@T 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 +Xlink:BuUrBu[213]@Y 213.111341 213.2337 H(15)C(9)N(3)O(3) 0.0 Chemical derivative 1887 0.0 +bisANS-sulfonates@T 434.178299 434.5305 H(22)C(32)N(2) 0.0 Chemical derivative 1330 0.0 +bisANS-sulfonates@S 434.178299 434.5305 H(22)C(32)N(2) 0.0 Chemical derivative 1330 0.0 +bisANS-sulfonates@K 434.178299 434.5305 H(22)C(32)N(2) 0.0 Chemical derivative 1330 0.0 +DNCB_hapten@Y 166.001457 166.0911 H(2)C(6)N(2)O(4) 0.0 Chemical derivative 1331 0.0 +DNCB_hapten@H 166.001457 166.0911 H(2)C(6)N(2)O(4) 0.0 Chemical derivative 1331 0.0 +DNCB_hapten@K 166.001457 166.0911 H(2)C(6)N(2)O(4) 0.0 Chemical derivative 1331 0.0 +DNCB_hapten@C 166.001457 166.0911 H(2)C(6)N(2)O(4) 0.0 Chemical derivative 1331 0.0 +NEMsulfur@C 157.019749 157.1903 H(7)C(6)N(1)O(2)S(1) 0.0 Chemical derivative 1326 0.0 +SulfurDioxide@C 63.9619 64.0638 O(2)S(1) 0.0 Post-translational 1327 0.0 +NEMsulfurWater@C 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1328 0.0 +HN3_mustard@C 131.094629 131.1729 H(13)C(6)N(1)O(2) 0.0 Post-translational 1389 0.0 +HN3_mustard@H 131.094629 131.1729 H(13)C(6)N(1)O(2) 0.0 Post-translational 1389 0.0 +HN3_mustard@K 131.094629 131.1729 H(13)C(6)N(1)O(2) 0.0 Post-translational 1389 0.0 +3-phosphoglyceryl@K 167.982375 168.042 H(5)C(3)O(6)P(1) 0.0 Post-translational 1387 0.0 +HN2_mustard@H 101.084064 101.1469 H(11)C(5)N(1)O(1) 0.0 Post-translational 1388 0.0 +HN2_mustard@K 101.084064 101.1469 H(11)C(5)N(1)O(1) 0.0 Post-translational 1388 0.0 +HN2_mustard@C 101.084064 101.1469 H(11)C(5)N(1)O(1) 0.0 Post-translational 1388 0.0 +NEM:2H(5)+H2O@C 148.089627 148.1714 H(4)2H(5)C(6)N(1)O(3) 0.0 Chemical derivative 1358 0.0 +Crotonyl@K 68.026215 68.074 H(4)C(4)O(1) 0.0 Post-translational 1363 0.0 +O-Et-N-diMePhospho@S 135.044916 135.1015 H(10)C(4)N(1)O(2)P(1) 0.0 Chemical derivative 1364 0.0 +N-dimethylphosphate@S 107.013615 107.0483 H(6)C(2)N(1)O(2)P(1) 0.0 Chemical derivative 1365 0.0 +phosphoRibosyl@E 212.00859 212.0945 H(9)C(5)O(7)P(1) 0.0 Post-translational 1356 0.0 +phosphoRibosyl@R 212.00859 212.0945 H(9)C(5)O(7)P(1) 0.0 Post-translational 1356 0.0 +phosphoRibosyl@D 212.00859 212.0945 H(9)C(5)O(7)P(1) 0.0 Post-translational 1356 0.0 +azole@C -20.026215 -20.0312 H(-4)O(-1) 0.0 Post-translational 1355 0.0 +azole@S -20.026215 -20.0312 H(-4)O(-1) 0.0 Post-translational 1355 0.0 +Biotin:Thermo-21911@C 921.461652 922.0913 H(71)C(41)N(5)O(16)S(1) 0.0 Chemical derivative 1340 0.0 +iodoTMT@K 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 +iodoTMT@H 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 +iodoTMT@E 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 +iodoTMT@D 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 +iodoTMT@C 324.216141 324.4185 H(28)C(16)N(4)O(3) 0.0 Chemical derivative 1341 0.0 +iodoTMT6plex@K 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 +iodoTMT6plex@H 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 +iodoTMT6plex@E 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 +iodoTMT6plex@D 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 +iodoTMT6plex@C 329.226595 329.3825 H(28)C(12)13C(4)N(3)15N(1)O(3) 0.0 Chemical derivative 1342 0.0 +Label:13C(2)15N(2)@K 4.00078 3.9721 C(-2)13C(2)N(-2)15N(2) 0.0 Isotopic label 1787 0.0 +Phosphogluconoylation@Any_N-term 258.014069 258.1199 H(11)C(6)O(9)P(1) 0.0 Post-translational 1344 0.0 +Phosphogluconoylation@K 258.014069 258.1199 H(11)C(6)O(9)P(1) 0.0 Post-translational 1344 0.0 +Methyl:2H(3)+Acetyl:2H(3)@K 62.063875 62.1002 H(-2)2H(6)C(3)O(1) 0.0 Isotopic label 1368 0.0 +dHex(1)Hex(1)@T 308.110732 308.2818 H(20)C(12)O(9) 308.110732 H(20)C(12)O(9) O-linked glycosylation 1367 0.5 +dHex(1)Hex(1)@S 308.110732 308.2818 H(20)C(12)O(9) 308.110732 H(20)C(12)O(9) O-linked glycosylation 1367 0.5 +methylsulfonylethyl@K 106.00885 106.1435 H(6)C(3)O(2)S(1) 0.0 Chemical derivative 1380 0.0 +methylsulfonylethyl@H 106.00885 106.1435 H(6)C(3)O(2)S(1) 0.0 Chemical derivative 1380 0.0 +methylsulfonylethyl@C 106.00885 106.1435 H(6)C(3)O(2)S(1) 0.0 Chemical derivative 1380 0.0 +Label:2H(3)+Oxidation@M 19.013745 19.0179 H(-3)2H(3)O(1) 0.0 Isotopic label 1370 0.0 +Trimethyl:2H(9)@R 51.103441 51.1352 H(-3)2H(9)C(3) 0.0 Isotopic label 1371 0.0 +Trimethyl:2H(9)@K 51.103441 51.1352 H(-3)2H(9)C(3) 0.0 Isotopic label 1371 0.0 +Acetyl:13C(2)@K 44.017274 44.022 H(2)13C(2)O(1) 0.0 Isotopic label 1372 0.0 +Acetyl:13C(2)@Protein_N-term 44.017274 44.022 H(2)13C(2)O(1) 0.0 Isotopic label 1372 0.0 +dHex(1)Hex(2)@T 470.163556 470.4224 H(30)C(18)O(14) 470.163556 H(30)C(18)O(14) O-linked glycosylation 1375 0.5 +dHex(1)Hex(2)@S 470.163556 470.4224 H(30)C(18)O(14) 470.163556 H(30)C(18)O(14) O-linked glycosylation 1375 0.5 +dHex(1)Hex(3)@T 632.216379 632.563 H(40)C(24)O(19) 632.216379 H(40)C(24)O(19) O-linked glycosylation 1376 0.5 +dHex(1)Hex(3)@S 632.216379 632.563 H(40)C(24)O(19) 632.216379 H(40)C(24)O(19) O-linked glycosylation 1376 0.5 +dHex(1)Hex(4)@T 794.269203 794.7036 H(50)C(30)O(24) 794.269203 H(50)C(30)O(24) O-linked glycosylation 1377 0.5 +dHex(1)Hex(4)@S 794.269203 794.7036 H(50)C(30)O(24) 794.269203 H(50)C(30)O(24) O-linked glycosylation 1377 0.5 +dHex(1)Hex(5)@T 956.322026 956.8442 H(60)C(36)O(29) 956.322026 H(60)C(36)O(29) O-linked glycosylation 1378 0.5 +dHex(1)Hex(5)@S 956.322026 956.8442 H(60)C(36)O(29) 956.322026 H(60)C(36)O(29) O-linked glycosylation 1378 0.5 +dHex(1)Hex(6)@T 1118.37485 1118.9848 H(70)C(42)O(34) 1118.37485 H(70)C(42)O(34) O-linked glycosylation 1379 0.5 +dHex(1)Hex(6)@S 1118.37485 1118.9848 H(70)C(42)O(34) 1118.37485 H(70)C(42)O(34) O-linked glycosylation 1379 0.5 +ethylsulfonylethyl@H 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 1381 0.0 +ethylsulfonylethyl@C 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 1381 0.0 +ethylsulfonylethyl@K 120.0245 120.1701 H(8)C(4)O(2)S(1) 0.0 Chemical derivative 1381 0.0 +phenylsulfonylethyl@C 168.0245 168.2129 H(8)C(8)O(2)S(1) 0.0 Chemical derivative 1382 0.0 +PyridoxalPhosphateH2@K 231.02966 231.1425 H(10)C(8)N(1)O(5)P(1) 0.0 Chemical derivative 1383 0.0 +Homocysteic_acid@M 33.969094 33.9716 H(-2)C(-1)O(3) 0.0 Artefact 1384 0.0 +Hydroxamic_acid@E 15.010899 15.0146 H(1)N(1) 0.0 Artefact 1385 0.0 +Hydroxamic_acid@D 15.010899 15.0146 H(1)N(1) 0.0 Artefact 1385 0.0 +Oxidation+NEM@C 141.042593 141.1247 H(7)C(6)N(1)O(3) 0.0 Chemical derivative 1390 0.0 +NHS-fluorescein@K 471.131802 471.4581 H(21)C(27)N(1)O(7) 0.0 Chemical derivative 1391 0.0 +DiART6plex@Y 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 +DiART6plex@Protein_N-term 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 +DiART6plex@Any_N-term 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 +DiART6plex@K 217.162932 217.2527 H(20)C(7)13C(4)N(1)15N(1)O(2) 0.0 Isotopic label 1392 0.0 +DiART6plex115@K 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 +DiART6plex115@Any_N-term 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 +DiART6plex115@Protein_N-term 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 +DiART6plex115@Y 217.156612 217.2535 H(20)C(8)13C(3)15N(2)O(2) 0.0 Isotopic label 1393 0.0 +DiART6plex116/119@Y 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 +DiART6plex116/119@Protein_N-term 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 +DiART6plex116/119@K 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 +DiART6plex116/119@Any_N-term 217.168776 217.2797 H(18)2H(2)C(9)13C(2)N(1)15N(1)O(2) 0.0 Isotopic label 1394 0.0 +DiART6plex117@K 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 +DiART6plex117@Any_N-term 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 +DiART6plex117@Protein_N-term 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 +DiART6plex117@Y 217.162456 217.2805 H(18)2H(2)C(10)13C(1)15N(2)O(2) 0.0 Isotopic label 1395 0.0 +DiART6plex118@K 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 +DiART6plex118@Any_N-term 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 +DiART6plex118@Protein_N-term 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 +DiART6plex118@Y 217.175096 217.279 H(18)2H(2)C(8)13C(3)N(2)O(2) 0.0 Isotopic label 1396 0.0 +Iodoacetanilide@K 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Artefact 1397 0.0 +Iodoacetanilide@C 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Chemical derivative 1397 0.0 +Iodoacetanilide@Any_N-term 133.052764 133.1473 H(7)C(8)N(1)O(1) 0.0 Artefact 1397 0.0 +Iodoacetanilide:13C(6)@K 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Artefact 1398 0.0 +Iodoacetanilide:13C(6)@C 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Chemical derivative 1398 0.0 +Iodoacetanilide:13C(6)@Any_N-term 139.072893 139.1032 H(7)C(2)13C(6)N(1)O(1) 0.0 Artefact 1398 0.0 +Dap-DSP@K 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Chemical derivative 1399 0.0 +Dap-DSP@E 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Non-standard residue 1399 0.0 +Dap-DSP@A 364.076278 364.4377 H(20)C(13)N(2)O(6)S(2) 0.0 Non-standard residue 1399 0.0 +MurNAc@A 275.100502 275.2552 H(17)C(11)N(1)O(7) 0.0 Other glycosylation 1400 0.0 +EEEDVIEVYQEQTGG@K 1705.73189 1706.7153 H(107)C(72)N(17)O(31) 0.0 Chemical derivative 1405 0.0 +Label:2H(7)15N(4)@R 11.032077 11.0168 H(-7)2H(7)N(-4)15N(4) 0.0 Isotopic label 1402 0.0 +Label:2H(6)15N(1)@P 7.034695 7.0304 H(-6)2H(6)N(-1)15N(1) 0.0 Isotopic label 1403 0.0 +EDEDTIDVFQQQTGG@K 1662.700924 1663.6508 H(102)C(69)N(18)O(30) 0.0 Chemical derivative 1406 0.0 +Hex(5)HexNAc(4)NeuAc(2)@N 2204.772441 2205.9822 H(136)C(84)N(6)O(61) 2204.772441 H(136)C(84)N(6)O(61) N-linked glycosylation 1408 0.5 +Hex(5)HexNAc(4)NeuAc(1)@N 1913.677025 1914.7277 H(119)C(73)N(5)O(53) 1913.677025 H(119)C(73)N(5)O(53) N-linked glycosylation 1409 0.5 +dHex(1)Hex(5)HexNAc(4)NeuAc(1)@N 2059.734933 2060.8689 H(129)C(79)N(5)O(57) 2059.734933 H(129)C(79)N(5)O(57) N-linked glycosylation 1410 0.5 +dHex(1)Hex(5)HexNAc(4)NeuAc(2)@N 2350.83035 2352.1234 H(146)C(90)N(6)O(65) 2350.83035 H(146)C(90)N(6)O(65) N-linked glycosylation 1411 0.5 +s-GlcNAc@T 283.036187 283.2557 H(13)C(8)N(1)O(8)S(1) 283.036187 H(13)C(8)N(1)O(8)S(1) O-linked glycosylation 1412 0.5 +s-GlcNAc@S 283.036187 283.2557 H(13)C(8)N(1)O(8)S(1) 283.036187 H(13)C(8)N(1)O(8)S(1) O-linked glycosylation 1412 0.5 +PhosphoHex(2)@N 404.071978 404.2611 H(21)C(12)O(13)P(1) 404.071978 H(21)C(12)O(13)P(1) N-linked glycosylation 1413 0.5 +PhosphoHex(2)@T 404.071978 404.2611 H(21)C(12)O(13)P(1) 404.071978 H(21)C(12)O(13)P(1) O-linked glycosylation 1413 0.5 +PhosphoHex(2)@S 404.071978 404.2611 H(21)C(12)O(13)P(1) 404.071978 H(21)C(12)O(13)P(1) O-linked glycosylation 1413 0.5 +Trimethyl:13C(3)2H(9)@K 54.113505 54.1132 H(-3)2H(9)13C(3) 0.0 Isotopic label 1414 0.0 +Trimethyl:13C(3)2H(9)@R 54.113505 54.1132 H(-3)2H(9)13C(3) 0.0 Isotopic label 1414 0.0 +15N-oxobutanoic@S^Protein_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Post-translational 1419 0.0 +15N-oxobutanoic@C^Any_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Artefact 1419 0.0 +15N-oxobutanoic@T^Protein_N-term -18.023584 -18.0239 H(-3)15N(-1) 0.0 Post-translational 1419 0.0 +spermidine@Q 128.131349 128.2153 H(16)C(7)N(2) 0.0 Chemical derivative 1421 0.0 +Biotin:Thermo-21330@Any_N-term 473.219571 473.5835 H(35)C(21)N(3)O(7)S(1) 0.0 Chemical derivative 1423 0.0 +Biotin:Thermo-21330@K 473.219571 473.5835 H(35)C(21)N(3)O(7)S(1) 0.0 Chemical derivative 1423 0.0 +Hex(1)Pent(2)@T 426.137341 426.3698 H(26)C(16)O(13) 426.137341 H(26)C(16)O(13) O-linked glycosylation 1428 0.5 +Hex(1)Pent(2)@S 426.137341 426.3698 H(26)C(16)O(13) 426.137341 H(26)C(16)O(13) O-linked glycosylation 1428 0.5 +Pentose@T 132.042259 132.1146 H(8)C(5)O(4) 132.042259 H(8)C(5)O(4) O-linked glycosylation 1425 0.5 +Pentose@S 132.042259 132.1146 H(8)C(5)O(4) 132.042259 H(8)C(5)O(4) O-linked glycosylation 1425 0.5 +Hex(1)Pent(1)@T 294.095082 294.2552 H(18)C(11)O(9) 294.095082 H(18)C(11)O(9) O-linked glycosylation 1426 0.5 +Hex(1)Pent(1)@S 294.095082 294.2552 H(18)C(11)O(9) 294.095082 H(18)C(11)O(9) O-linked glycosylation 1426 0.5 +Hex(1)HexA(1)@T 338.084912 338.2647 H(18)C(12)O(11) 338.084912 H(18)C(12)O(11) O-linked glycosylation 1427 0.5 +Hex(1)HexA(1)@S 338.084912 338.2647 H(18)C(12)O(11) 338.084912 H(18)C(12)O(11) O-linked glycosylation 1427 0.5 +Hex(1)HexNAc(1)Phos(1)@T 445.098527 445.313 H(24)C(14)N(1)O(13)P(1) 445.098527 H(24)C(14)N(1)O(13)P(1) O-linked glycosylation 1429 0.5 +Hex(1)HexNAc(1)Phos(1)@S 445.098527 445.313 H(24)C(14)N(1)O(13)P(1) 445.098527 H(24)C(14)N(1)O(13)P(1) O-linked glycosylation 1429 0.5 +Hex(1)HexNAc(1)Sulf(1)@T 445.089011 445.3963 H(23)C(14)N(1)O(13)S(1) 445.089011 H(23)C(14)N(1)O(13)S(1) O-linked glycosylation 1430 0.5 +Hex(1)HexNAc(1)Sulf(1)@S 445.089011 445.3963 H(23)C(14)N(1)O(13)S(1) 445.089011 H(23)C(14)N(1)O(13)S(1) O-linked glycosylation 1430 0.5 +Hex(1)NeuAc(1)@T 453.14824 453.3952 H(27)C(17)N(1)O(13) 453.14824 H(27)C(17)N(1)O(13) O-linked glycosylation 1431 0.5 +Hex(1)NeuAc(1)@S 453.14824 453.3952 H(27)C(17)N(1)O(13) 453.14824 H(27)C(17)N(1)O(13) O-linked glycosylation 1431 0.5 +Hex(1)NeuGc(1)@T 469.143155 469.3946 H(27)C(17)N(1)O(14) 469.143155 H(27)C(17)N(1)O(14) O-linked glycosylation 1432 0.5 +Hex(1)NeuGc(1)@S 469.143155 469.3946 H(27)C(17)N(1)O(14) 469.143155 H(27)C(17)N(1)O(14) O-linked glycosylation 1432 0.5 +HexNAc(3)@T 609.238118 609.5776 H(39)C(24)N(3)O(15) 609.238118 H(39)C(24)N(3)O(15) O-linked glycosylation 1433 0.5 +HexNAc(3)@S 609.238118 609.5776 H(39)C(24)N(3)O(15) 609.238118 H(39)C(24)N(3)O(15) O-linked glycosylation 1433 0.5 +HexNAc(1)NeuAc(1)@T 494.174789 494.4471 H(30)C(19)N(2)O(13) 494.174789 H(30)C(19)N(2)O(13) O-linked glycosylation 1434 0.5 +HexNAc(1)NeuAc(1)@S 494.174789 494.4471 H(30)C(19)N(2)O(13) 494.174789 H(30)C(19)N(2)O(13) O-linked glycosylation 1434 0.5 +HexNAc(1)NeuGc(1)@T 510.169704 510.4465 H(30)C(19)N(2)O(14) 510.169704 H(30)C(19)N(2)O(14) O-linked glycosylation 1435 0.5 +HexNAc(1)NeuGc(1)@S 510.169704 510.4465 H(30)C(19)N(2)O(14) 510.169704 H(30)C(19)N(2)O(14) O-linked glycosylation 1435 0.5 +Hex(2)NeuAc(1)@T 615.201064 615.5358 H(37)C(23)N(1)O(18) 615.201064 H(37)C(23)N(1)O(18) O-linked glycosylation 1444 0.5 +Hex(2)NeuAc(1)@S 615.201064 615.5358 H(37)C(23)N(1)O(18) 615.201064 H(37)C(23)N(1)O(18) O-linked glycosylation 1444 0.5 +Hex(1)HexNAc(1)dHex(1)Me(1)@T 525.205755 525.5009 H(35)C(21)N(1)O(14) 525.205755 H(35)C(21)N(1)O(14) O-linked glycosylation 1436 0.5 +Hex(1)HexNAc(1)dHex(1)Me(1)@S 525.205755 525.5009 H(35)C(21)N(1)O(14) 525.205755 H(35)C(21)N(1)O(14) O-linked glycosylation 1436 0.5 +Hex(1)HexNAc(1)dHex(1)Me(2)@T 539.221405 539.5275 H(37)C(22)N(1)O(14) 539.221405 H(37)C(22)N(1)O(14) O-linked glycosylation 1437 0.5 +Hex(1)HexNAc(1)dHex(1)Me(2)@S 539.221405 539.5275 H(37)C(22)N(1)O(14) 539.221405 H(37)C(22)N(1)O(14) O-linked glycosylation 1437 0.5 +Xlink:DSS[155]@Protein_N-term 155.094629 155.1943 H(13)C(8)N(1)O(2) 0.0 Chemical derivative 1789 0.0 +Xlink:DSS[155]@K 155.094629 155.1943 H(13)C(8)N(1)O(2) 0.0 Chemical derivative 1789 0.0 +Hex(2)HexNAc(1)@N 527.18502 527.4737 H(33)C(20)N(1)O(15) 527.18502 H(33)C(20)N(1)O(15) N-linked glycosylation 1438 0.5 +Hex(2)HexNAc(1)@T 527.18502 527.4737 H(33)C(20)N(1)O(15) 527.18502 H(33)C(20)N(1)O(15) O-linked glycosylation 1438 0.5 +Hex(2)HexNAc(1)@S 527.18502 527.4737 H(33)C(20)N(1)O(15) 527.18502 H(33)C(20)N(1)O(15) O-linked glycosylation 1438 0.5 +Hex(1)HexA(1)HexNAc(1)@T 541.164284 541.4572 H(31)C(20)N(1)O(16) 541.164284 H(31)C(20)N(1)O(16) O-linked glycosylation 1439 0.5 +Hex(1)HexA(1)HexNAc(1)@S 541.164284 541.4572 H(31)C(20)N(1)O(16) 541.164284 H(31)C(20)N(1)O(16) O-linked glycosylation 1439 0.5 +Hex(2)HexNAc(1)Me(1)@T 541.20067 541.5003 H(35)C(21)N(1)O(15) 541.20067 H(35)C(21)N(1)O(15) O-linked glycosylation 1440 0.5 +Hex(2)HexNAc(1)Me(1)@S 541.20067 541.5003 H(35)C(21)N(1)O(15) 541.20067 H(35)C(21)N(1)O(15) O-linked glycosylation 1440 0.5 +Hex(1)Pent(3)@T 558.1796 558.4845 H(34)C(21)O(17) 558.1796 H(34)C(21)O(17) O-linked glycosylation 1441 0.5 +Hex(1)Pent(3)@S 558.1796 558.4845 H(34)C(21)O(17) 558.1796 H(34)C(21)O(17) O-linked glycosylation 1441 0.5 +Hex(1)NeuAc(1)Pent(1)@S 585.190499 585.5098 H(35)C(22)N(1)O(17) 585.190499 H(35)C(22)N(1)O(17) O-linked glycosylation 1442 0.5 +Hex(1)NeuAc(1)Pent(1)@T 585.190499 585.5098 H(35)C(22)N(1)O(17) 585.190499 H(35)C(22)N(1)O(17) O-linked glycosylation 1442 0.5 +Hex(2)HexNAc(1)Sulf(1)@T 607.141834 607.5369 H(33)C(20)N(1)O(18)S(1) 607.141834 H(33)C(20)N(1)O(18)S(1) O-linked glycosylation 1443 0.5 +Hex(2)HexNAc(1)Sulf(1)@S 607.141834 607.5369 H(33)C(20)N(1)O(18)S(1) 607.141834 H(33)C(20)N(1)O(18)S(1) O-linked glycosylation 1443 0.5 +dHex(2)Hex(2)@S 616.221465 616.5636 H(40)C(24)O(18) 616.221465 H(40)C(24)O(18) O-linked glycosylation 1445 0.5 +dHex(2)Hex(2)@T 616.221465 616.5636 H(40)C(24)O(18) 616.221465 H(40)C(24)O(18) O-linked glycosylation 1445 0.5 +dHex(1)Hex(2)HexA(1)@S 646.195644 646.5465 H(38)C(24)O(20) 646.195644 H(38)C(24)O(20) O-linked glycosylation 1446 0.5 +dHex(1)Hex(2)HexA(1)@T 646.195644 646.5465 H(38)C(24)O(20) 646.195644 H(38)C(24)O(20) O-linked glycosylation 1446 0.5 +Hex(1)HexNAc(2)Sulf(1)@T 648.168383 648.5888 H(36)C(22)N(2)O(18)S(1) 648.168383 H(36)C(22)N(2)O(18)S(1) O-linked glycosylation 1447 0.5 +Hex(1)HexNAc(2)Sulf(1)@S 648.168383 648.5888 H(36)C(22)N(2)O(18)S(1) 648.168383 H(36)C(22)N(2)O(18)S(1) O-linked glycosylation 1447 0.5 +Hex(4)@S 648.211294 648.5624 H(40)C(24)O(20) 648.211294 H(40)C(24)O(20) O-linked glycosylation 1448 0.5 +Hex(4)@T 648.211294 648.5624 H(40)C(24)O(20) 648.211294 H(40)C(24)O(20) O-linked glycosylation 1448 0.5 +dHex(1)Hex(2)HexNAc(2)Pent(1)@N 1008.36456 1008.9221 H(64)C(39)N(2)O(28) 1008.36456 H(64)C(39)N(2)O(28) N-linked glycosylation 1449 0.5 +Hex(2)HexNAc(2)NeuAc(1)@N 1021.359809 1021.9208 H(63)C(39)N(3)O(28) 1021.359809 H(63)C(39)N(3)O(28) N-linked glycosylation 1450 0.5 +Hex(2)HexNAc(2)NeuAc(1)@S 1021.359809 1021.9208 H(63)C(39)N(3)O(28) 1021.359809 H(63)C(39)N(3)O(28) O-linked glycosylation 1450 0.5 +Hex(2)HexNAc(2)NeuAc(1)@T 1021.359809 1021.9208 H(63)C(39)N(3)O(28) 1021.359809 H(63)C(39)N(3)O(28) O-linked glycosylation 1450 0.5 +Hex(3)HexNAc(2)Pent(1)@N 1024.359475 1024.9215 H(64)C(39)N(2)O(29) 1024.359475 H(64)C(39)N(2)O(29) N-linked glycosylation 1451 0.5 +Hex(4)HexNAc(2)@N 1054.370039 1054.9474 H(66)C(40)N(2)O(30) 1054.370039 H(66)C(40)N(2)O(30) N-linked glycosylation 1452 0.5 +dHex(1)Hex(4)HexNAc(1)Pent(1)@N 1129.390834 1130.0107 H(71)C(43)N(1)O(33) 1129.390834 H(71)C(43)N(1)O(33) N-linked glycosylation 1453 0.5 +dHex(1)Hex(3)HexNAc(2)Pent(1)@N 1170.417383 1171.0627 H(74)C(45)N(2)O(33) 1170.417383 H(74)C(45)N(2)O(33) N-linked glycosylation 1454 0.5 +Hex(3)HexNAc(2)NeuAc(1)@N 1183.412632 1184.0614 H(73)C(45)N(3)O(33) 1183.412632 H(73)C(45)N(3)O(33) N-linked glycosylation 1455 0.5 +Hex(4)HexNAc(2)Pent(1)@N 1186.412298 1187.0621 H(74)C(45)N(2)O(34) 1186.412298 H(74)C(45)N(2)O(34) N-linked glycosylation 1456 0.5 +Hex(3)HexNAc(3)Pent(1)@N 1227.438847 1228.114 H(77)C(47)N(3)O(34) 1227.438847 H(77)C(47)N(3)O(34) N-linked glycosylation 1457 0.5 +Hex(5)HexNAc(2)Phos(1)@N 1296.389194 1297.0679 H(77)C(46)N(2)O(38)P(1) 1296.389194 H(77)C(46)N(2)O(38)P(1) N-linked glycosylation 1458 0.5 +dHex(1)Hex(4)HexNAc(2)Pent(1)@N 1332.470207 1333.2033 H(84)C(51)N(2)O(38) 1332.470207 H(84)C(51)N(2)O(38) N-linked glycosylation 1459 0.5 +Hex(7)HexNAc(1)@N 1337.449137 1338.1767 H(83)C(50)N(1)O(40) 1337.449137 H(83)C(50)N(1)O(40) N-linked glycosylation 1460 0.5 +Hex(4)HexNAc(2)NeuAc(1)@N 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) N-linked glycosylation 1461 0.5 +Hex(4)HexNAc(2)NeuAc(1)@S 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) O-linked glycosylation 1461 0.5 +Hex(4)HexNAc(2)NeuAc(1)@T 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) O-linked glycosylation 1461 0.5 +dHex(1)Hex(5)HexNAc(2)@N 1362.480772 1363.2292 H(86)C(52)N(2)O(39) 1362.480772 H(86)C(52)N(2)O(39) N-linked glycosylation 1462 0.5 +dHex(1)Hex(3)HexNAc(3)Pent(1)@N 1373.496756 1374.2552 H(87)C(53)N(3)O(38) 1373.496756 H(87)C(53)N(3)O(38) N-linked glycosylation 1463 0.5 +Hex(3)HexNAc(4)Sulf(1)@N 1378.432776 1379.2551 H(82)C(50)N(4)O(38)S(1) 1378.432776 H(82)C(50)N(4)O(38)S(1) N-linked glycosylation 1464 0.5 +Hex(6)HexNAc(2)@N 1378.475686 1379.2286 H(86)C(52)N(2)O(40) 1378.475686 H(86)C(52)N(2)O(40) N-linked glycosylation 1465 0.5 +Hex(4)HexNAc(3)Pent(1)@N 1389.491671 1390.2546 H(87)C(53)N(3)O(39) 1389.491671 H(87)C(53)N(3)O(39) N-linked glycosylation 1466 0.5 +dHex(1)Hex(4)HexNAc(3)@N 1403.507321 1404.2812 H(89)C(54)N(3)O(39) 1403.507321 H(89)C(54)N(3)O(39) N-linked glycosylation 1467 0.5 +Hex(5)HexNAc(3)@N 1419.502235 1420.2806 H(89)C(54)N(3)O(40) 1419.502235 H(89)C(54)N(3)O(40) N-linked glycosylation 1468 0.5 +Hex(3)HexNAc(4)Pent(1)@N 1430.51822 1431.3065 H(90)C(55)N(4)O(39) 1430.51822 H(90)C(55)N(4)O(39) N-linked glycosylation 1469 0.5 +Hex(6)HexNAc(2)Phos(1)@N 1458.442017 1459.2085 H(87)C(52)N(2)O(43)P(1) 1458.442017 H(87)C(52)N(2)O(43)P(1) N-linked glycosylation 1470 0.5 +dHex(1)Hex(4)HexNAc(3)Sulf(1)@N 1483.464135 1484.3444 H(89)C(54)N(3)O(42)S(1) 1483.464135 H(89)C(54)N(3)O(42)S(1) N-linked glycosylation 1471 0.5 +dHex(1)Hex(5)HexNAc(2)Pent(1)@N 1494.52303 1495.3439 H(94)C(57)N(2)O(43) 1494.52303 H(94)C(57)N(2)O(43) N-linked glycosylation 1472 0.5 +Hex(8)HexNAc(1)@N 1499.501961 1500.3173 H(93)C(56)N(1)O(45) 1499.501961 H(93)C(56)N(1)O(45) N-linked glycosylation 1473 0.5 +dHex(1)Hex(3)HexNAc(3)Pent(2)@N 1505.539015 1506.3698 H(95)C(58)N(3)O(42) 1505.539015 H(95)C(58)N(3)O(42) N-linked glycosylation 1474 0.5 +dHex(2)Hex(3)HexNAc(3)Pent(1)@N 1519.554665 1520.3964 H(97)C(59)N(3)O(42) 1519.554665 H(97)C(59)N(3)O(42) N-linked glycosylation 1475 0.5 +dHex(1)Hex(3)HexNAc(4)Sulf(1)@N 1524.490684 1525.3963 H(92)C(56)N(4)O(42)S(1) 1524.490684 H(92)C(56)N(4)O(42)S(1) N-linked glycosylation 1476 0.5 +dHex(1)Hex(6)HexNAc(2)@N 1524.533595 1525.3698 H(96)C(58)N(2)O(44) 1524.533595 H(96)C(58)N(2)O(44) N-linked glycosylation 1477 0.5 +dHex(1)Hex(4)HexNAc(3)Pent(1)@N 1535.549579 1536.3958 H(97)C(59)N(3)O(43) 1535.549579 H(97)C(59)N(3)O(43) N-linked glycosylation 1478 0.5 +Hex(4)HexNAc(4)Sulf(1)@N 1540.485599 1541.3957 H(92)C(56)N(4)O(43)S(1) 1540.485599 H(92)C(56)N(4)O(43)S(1) N-linked glycosylation 1479 0.5 +Hex(7)HexNAc(2)@N 1540.52851 1541.3692 H(96)C(58)N(2)O(45) 1540.52851 H(96)C(58)N(2)O(45) N-linked glycosylation 1480 0.5 +dHex(2)Hex(4)HexNAc(3)@N 1549.56523 1550.4224 H(99)C(60)N(3)O(43) 1549.56523 H(99)C(60)N(3)O(43) N-linked glycosylation 1481 0.5 +Hex(5)HexNAc(3)Pent(1)@N 1551.544494 1552.3952 H(97)C(59)N(3)O(44) 1551.544494 H(97)C(59)N(3)O(44) N-linked glycosylation 1482 0.5 +Hex(4)HexNAc(3)NeuGc(1)@N 1564.539743 1565.3939 H(96)C(59)N(4)O(44) 1564.539743 H(96)C(59)N(4)O(44) N-linked glycosylation 1483 0.5 +dHex(1)Hex(5)HexNAc(3)@N 1565.560144 1566.4218 H(99)C(60)N(3)O(44) 1565.560144 H(99)C(60)N(3)O(44) N-linked glycosylation 1484 0.5 +dHex(1)Hex(3)HexNAc(4)Pent(1)@N 1576.576129 1577.4477 H(100)C(61)N(4)O(43) 1576.576129 H(100)C(61)N(4)O(43) N-linked glycosylation 1485 0.5 +Hex(3)HexNAc(5)Sulf(1)@N 1581.512148 1582.4476 H(95)C(58)N(5)O(43)S(1) 1581.512148 H(95)C(58)N(5)O(43)S(1) N-linked glycosylation 1486 0.5 +Hex(6)HexNAc(3)@N 1581.555059 1582.4212 H(99)C(60)N(3)O(45) 1581.555059 H(99)C(60)N(3)O(45) N-linked glycosylation 1487 0.5 +Hex(3)HexNAc(4)NeuAc(1)@N 1589.571378 1590.4465 H(99)C(61)N(5)O(43) 1589.571378 H(99)C(61)N(5)O(43) N-linked glycosylation 1488 0.5 +Hex(4)HexNAc(4)Pent(1)@N 1592.571043 1593.4471 H(100)C(61)N(4)O(44) 1592.571043 H(100)C(61)N(4)O(44) N-linked glycosylation 1489 0.5 +Hex(7)HexNAc(2)Phos(1)@N 1620.494841 1621.3491 H(97)C(58)N(2)O(48)P(1) 1620.494841 H(97)C(58)N(2)O(48)P(1) N-linked glycosylation 1490 0.5 +Hex(4)HexNAc(4)Me(2)Pent(1)@N 1620.602343 1621.5003 H(104)C(63)N(4)O(44) 1620.602343 H(104)C(63)N(4)O(44) N-linked glycosylation 1491 0.5 +dHex(1)Hex(3)HexNAc(3)Pent(3)@N 1637.581274 1638.4844 H(103)C(63)N(3)O(46) 1637.581274 H(103)C(63)N(3)O(46) N-linked glycosylation 1492 0.5 +dHex(1)Hex(5)HexNAc(3)Sulf(1)@N 1645.516959 1646.485 H(99)C(60)N(3)O(47)S(1) 1645.516959 H(99)C(60)N(3)O(47)S(1) N-linked glycosylation 1493 0.5 +dHex(2)Hex(3)HexNAc(3)Pent(2)@N 1651.596924 1652.511 H(105)C(64)N(3)O(46) 1651.596924 H(105)C(64)N(3)O(46) N-linked glycosylation 1494 0.5 +Hex(6)HexNAc(3)Phos(1)@N 1661.52139 1662.4011 H(100)C(60)N(3)O(48)P(1) 1661.52139 H(100)C(60)N(3)O(48)P(1) N-linked glycosylation 1495 0.5 +Hex(4)HexNAc(5)@N 1663.608157 1664.525 H(105)C(64)N(5)O(45) 1663.608157 H(105)C(64)N(5)O(45) N-linked glycosylation 1496 0.5 +dHex(3)Hex(3)HexNAc(3)Pent(1)@N 1665.612574 1666.5376 H(107)C(65)N(3)O(46) 1665.612574 H(107)C(65)N(3)O(46) N-linked glycosylation 1497 0.5 +dHex(2)Hex(4)HexNAc(3)Pent(1)@N 1681.607488 1682.537 H(107)C(65)N(3)O(47) 1681.607488 H(107)C(65)N(3)O(47) N-linked glycosylation 1498 0.5 +dHex(1)Hex(4)HexNAc(4)Sulf(1)@N 1686.543508 1687.5369 H(102)C(62)N(4)O(47)S(1) 1686.543508 H(102)C(62)N(4)O(47)S(1) N-linked glycosylation 1499 0.5 +dHex(1)Hex(7)HexNAc(2)@N 1686.586419 1687.5104 H(106)C(64)N(2)O(49) 1686.586419 H(106)C(64)N(2)O(49) N-linked glycosylation 1500 0.5 +dHex(1)Hex(4)HexNAc(3)NeuAc(1)@N 1694.602737 1695.5357 H(106)C(65)N(4)O(47) 1694.602737 H(106)C(65)N(4)O(47) N-linked glycosylation 1501 0.5 +dHex(1)Hex(4)HexNAc(3)NeuAc(1)@S 1694.602737 1695.5357 H(106)C(65)N(4)O(47) 1694.602737 H(106)C(65)N(4)O(47) O-linked glycosylation 1501 0.5 +dHex(1)Hex(4)HexNAc(3)NeuAc(1)@T 1694.602737 1695.5357 H(106)C(65)N(4)O(47) 1694.602737 H(106)C(65)N(4)O(47) O-linked glycosylation 1501 0.5 +Hex(7)HexNAc(2)Phos(2)@N 1700.461172 1701.329 H(98)C(58)N(2)O(51)P(2) 1700.461172 H(98)C(58)N(2)O(51)P(2) N-linked glycosylation 1502 0.5 +Hex(5)HexNAc(4)Sulf(1)@N 1702.538423 1703.5363 H(102)C(62)N(4)O(48)S(1) 1702.538423 H(102)C(62)N(4)O(48)S(1) N-linked glycosylation 1503 0.5 +Hex(8)HexNAc(2)@N 1702.581333 1703.5098 H(106)C(64)N(2)O(50) 1702.581333 H(106)C(64)N(2)O(50) N-linked glycosylation 1504 0.5 +dHex(1)Hex(3)HexNAc(4)Pent(2)@N 1708.618387 1709.5623 H(108)C(66)N(4)O(47) 1708.618387 H(108)C(66)N(4)O(47) N-linked glycosylation 1505 0.5 +dHex(1)Hex(4)HexNAc(3)NeuGc(1)@N 1710.597652 1711.5351 H(106)C(65)N(4)O(48) 1710.597652 H(106)C(65)N(4)O(48) N-linked glycosylation 1506 0.5 +dHex(2)Hex(3)HexNAc(4)Pent(1)@N 1722.634037 1723.5889 H(110)C(67)N(4)O(47) 1722.634037 H(110)C(67)N(4)O(47) N-linked glycosylation 1507 0.5 +dHex(1)Hex(3)HexNAc(5)Sulf(1)@N 1727.570057 1728.5888 H(105)C(64)N(5)O(47)S(1) 1727.570057 H(105)C(64)N(5)O(47)S(1) N-linked glycosylation 1508 0.5 +dHex(1)Hex(6)HexNAc(3)@N 1727.612968 1728.5624 H(109)C(66)N(3)O(49) 1727.612968 H(109)C(66)N(3)O(49) N-linked glycosylation 1509 0.5 +dHex(1)Hex(3)HexNAc(4)NeuAc(1)@N 1735.629286 1736.5877 H(109)C(67)N(5)O(47) 1735.629286 H(109)C(67)N(5)O(47) N-linked glycosylation 1510 0.5 +dHex(3)Hex(3)HexNAc(4)@N 1736.649688 1737.6155 H(112)C(68)N(4)O(47) 1736.649688 H(112)C(68)N(4)O(47) N-linked glycosylation 1511 0.5 +dHex(1)Hex(4)HexNAc(4)Pent(1)@N 1738.628952 1739.5883 H(110)C(67)N(4)O(48) 1738.628952 H(110)C(67)N(4)O(48) N-linked glycosylation 1512 0.5 +Hex(4)HexNAc(5)Sulf(1)@N 1743.564972 1744.5882 H(105)C(64)N(5)O(48)S(1) 1743.564972 H(105)C(64)N(5)O(48)S(1) N-linked glycosylation 1513 0.5 +Hex(7)HexNAc(3)@N 1743.607882 1744.5618 H(109)C(66)N(3)O(50) 1743.607882 H(109)C(66)N(3)O(50) N-linked glycosylation 1514 0.5 +dHex(1)Hex(4)HexNAc(3)NeuAc(1)Sulf(1)@N 1774.559552 1775.5989 H(106)C(65)N(4)O(50)S(1) 1774.559552 H(106)C(65)N(4)O(50)S(1) N-linked glycosylation 1515 0.5 +Hex(5)HexNAc(4)Me(2)Pent(1)@N 1782.655167 1783.6409 H(114)C(69)N(4)O(49) 1782.655167 H(114)C(69)N(4)O(49) N-linked glycosylation 1516 0.5 +Hex(3)HexNAc(6)Sulf(1)@N 1784.591521 1785.6401 H(108)C(66)N(6)O(48)S(1) 1784.591521 H(108)C(66)N(6)O(48)S(1) N-linked glycosylation 1517 0.5 +dHex(1)Hex(6)HexNAc(3)Sulf(1)@N 1807.569782 1808.6256 H(109)C(66)N(3)O(52)S(1) 1807.569782 H(109)C(66)N(3)O(52)S(1) N-linked glycosylation 1518 0.5 +dHex(1)Hex(4)HexNAc(5)@N 1809.666066 1810.6662 H(115)C(70)N(5)O(49) 1809.666066 H(115)C(70)N(5)O(49) N-linked glycosylation 1519 0.5 +dHex(1)Hex(5)HexA(1)HexNAc(3)Sulf(1)@N 1821.549047 1822.6091 H(107)C(66)N(3)O(53)S(1) 1821.549047 H(107)C(66)N(3)O(53)S(1) N-linked glycosylation 1520 0.5 +Hex(7)HexNAc(3)Phos(1)@N 1823.574213 1824.5417 H(110)C(66)N(3)O(53)P(1) 1823.574213 H(110)C(66)N(3)O(53)P(1) N-linked glycosylation 1521 0.5 +Hex(6)HexNAc(4)Me(3)@N 1826.681382 1827.6934 H(118)C(71)N(4)O(50) 1826.681382 H(118)C(71)N(4)O(50) N-linked glycosylation 1522 0.5 +dHex(2)Hex(4)HexNAc(4)Sulf(1)@N 1832.601417 1833.6781 H(112)C(68)N(4)O(51)S(1) 1832.601417 H(112)C(68)N(4)O(51)S(1) N-linked glycosylation 1523 0.5 +Hex(4)HexNAc(3)NeuAc(2)@N 1839.640245 1840.6491 H(113)C(70)N(5)O(51) 1839.640245 H(113)C(70)N(5)O(51) N-linked glycosylation 1524 0.5 +dHex(1)Hex(3)HexNAc(4)Pent(3)@N 1840.660646 1841.6769 H(116)C(71)N(4)O(51) 1840.660646 H(116)C(71)N(4)O(51) N-linked glycosylation 1525 0.5 +dHex(2)Hex(5)HexNAc(3)Pent(1)@N 1843.660312 1844.6776 H(117)C(71)N(3)O(52) 1843.660312 H(117)C(71)N(3)O(52) N-linked glycosylation 1526 0.5 +dHex(1)Hex(5)HexNAc(4)Sulf(1)@N 1848.596331 1849.6775 H(112)C(68)N(4)O(52)S(1) 1848.596331 H(112)C(68)N(4)O(52)S(1) N-linked glycosylation 1527 0.5 +dHex(2)Hex(3)HexNAc(4)Pent(2)@N 1854.676296 1855.7035 H(118)C(72)N(4)O(51) 1854.676296 H(118)C(72)N(4)O(51) N-linked glycosylation 1528 0.5 +dHex(1)Hex(5)HexNAc(3)NeuAc(1)@N 1856.655561 1857.6763 H(116)C(71)N(4)O(52) 1856.655561 H(116)C(71)N(4)O(52) N-linked glycosylation 1529 0.5 +Hex(3)HexNAc(6)Sulf(2)@N 1864.548335 1865.7033 H(108)C(66)N(6)O(51)S(2) 1864.548335 H(108)C(66)N(6)O(51)S(2) N-linked glycosylation 1530 0.5 +Hex(9)HexNAc(2)@N 1864.634157 1865.6504 H(116)C(70)N(2)O(55) 1864.634157 H(116)C(70)N(2)O(55) N-linked glycosylation 1531 0.5 +Hex(4)HexNAc(6)@N 1866.68753 1867.7175 H(118)C(72)N(6)O(50) 1866.68753 H(118)C(72)N(6)O(50) N-linked glycosylation 1532 0.5 +dHex(3)Hex(3)HexNAc(4)Pent(1)@N 1868.691946 1869.7301 H(120)C(73)N(4)O(51) 1868.691946 H(120)C(73)N(4)O(51) N-linked glycosylation 1533 0.5 +dHex(1)Hex(5)HexNAc(3)NeuGc(1)@N 1872.650475 1873.6757 H(116)C(71)N(4)O(53) 1872.650475 H(116)C(71)N(4)O(53) N-linked glycosylation 1534 0.5 +dHex(2)Hex(4)HexNAc(4)Pent(1)@N 1884.686861 1885.7295 H(120)C(73)N(4)O(52) 1884.686861 H(120)C(73)N(4)O(52) N-linked glycosylation 1535 0.5 +dHex(1)Hex(4)HexNAc(5)Sulf(1)@N 1889.62288 1890.7294 H(115)C(70)N(5)O(52)S(1) 1889.62288 H(115)C(70)N(5)O(52)S(1) N-linked glycosylation 1536 0.5 +dHex(1)Hex(7)HexNAc(3)@N 1889.665791 1890.703 H(119)C(72)N(3)O(54) 1889.665791 H(119)C(72)N(3)O(54) N-linked glycosylation 1537 0.5 +dHex(1)Hex(5)HexNAc(4)Pent(1)@N 1900.681776 1901.7289 H(120)C(73)N(4)O(53) 1900.681776 H(120)C(73)N(4)O(53) N-linked glycosylation 1538 0.5 +dHex(1)Hex(5)HexA(1)HexNAc(3)Sulf(2)@N 1901.505861 1902.6723 H(107)C(66)N(3)O(56)S(2) 1901.505861 H(107)C(66)N(3)O(56)S(2) N-linked glycosylation 1539 0.5 +Hex(3)HexNAc(7)@N 1907.714079 1908.7694 H(121)C(74)N(7)O(50) 1907.714079 H(121)C(74)N(7)O(50) N-linked glycosylation 1540 0.5 +dHex(2)Hex(5)HexNAc(4)@N 1914.697426 1915.7555 H(122)C(74)N(4)O(53) 1914.697426 H(122)C(74)N(4)O(53) N-linked glycosylation 1541 0.5 +dHex(2)Hex(4)HexNAc(3)NeuAc(1)Sulf(1)@N 1920.617461 1921.7401 H(116)C(71)N(4)O(54)S(1) 1920.617461 H(116)C(71)N(4)O(54)S(1) N-linked glycosylation 1542 0.5 +dHex(1)Hex(5)HexNAc(4)Sulf(2)@N 1928.553146 1929.7407 H(112)C(68)N(4)O(55)S(2) 1928.553146 H(112)C(68)N(4)O(55)S(2) N-linked glycosylation 1543 0.5 +dHex(1)Hex(5)HexNAc(4)Me(2)Pent(1)@N 1928.713076 1929.7821 H(124)C(75)N(4)O(53) 1928.713076 H(124)C(75)N(4)O(53) N-linked glycosylation 1544 0.5 +Hex(5)HexNAc(4)NeuGc(1)@N 1929.671939 1930.7271 H(119)C(73)N(5)O(54) 1929.671939 H(119)C(73)N(5)O(54) N-linked glycosylation 1545 0.5 +dHex(1)Hex(3)HexNAc(6)Sulf(1)@N 1930.64943 1931.7813 H(118)C(72)N(6)O(52)S(1) 1930.64943 H(118)C(72)N(6)O(52)S(1) N-linked glycosylation 1546 0.5 +dHex(1)Hex(6)HexNAc(4)@N 1930.69234 1931.7549 H(122)C(74)N(4)O(54) 1930.69234 H(122)C(74)N(4)O(54) N-linked glycosylation 1547 0.5 +dHex(1)Hex(5)HexNAc(3)NeuAc(1)Sulf(1)@N 1936.612375 1937.7395 H(116)C(71)N(4)O(55)S(1) 1936.612375 H(116)C(71)N(4)O(55)S(1) N-linked glycosylation 1548 0.5 +Hex(7)HexNAc(4)@N 1946.687255 1947.7543 H(122)C(74)N(4)O(55) 1946.687255 H(122)C(74)N(4)O(55) N-linked glycosylation 1549 0.5 +dHex(1)Hex(5)HexNAc(3)NeuGc(1)Sulf(1)@N 1952.60729 1953.7389 H(116)C(71)N(4)O(56)S(1) 1952.60729 H(116)C(71)N(4)O(56)S(1) N-linked glycosylation 1550 0.5 +Hex(4)HexNAc(5)NeuAc(1)@N 1954.703574 1955.7796 H(122)C(75)N(6)O(53) 1954.703574 H(122)C(75)N(6)O(53) N-linked glycosylation 1551 0.5 +Hex(6)HexNAc(4)Me(3)Pent(1)@N 1958.72364 1959.808 H(126)C(76)N(4)O(54) 1958.72364 H(126)C(76)N(4)O(54) N-linked glycosylation 1552 0.5 +dHex(1)Hex(7)HexNAc(3)Sulf(1)@N 1969.622606 1970.7662 H(119)C(72)N(3)O(57)S(1) 1969.622606 H(119)C(72)N(3)O(57)S(1) N-linked glycosylation 1553 0.5 +dHex(1)Hex(7)HexNAc(3)Phos(1)@N 1969.632122 1970.6829 H(120)C(72)N(3)O(57)P(1) 1969.632122 H(120)C(72)N(3)O(57)P(1) N-linked glycosylation 1554 0.5 +dHex(1)Hex(5)HexNAc(5)@N 1971.718889 1972.8068 H(125)C(76)N(5)O(54) 1971.718889 H(125)C(76)N(5)O(54) N-linked glycosylation 1555 0.5 +dHex(1)Hex(4)HexNAc(4)NeuAc(1)Sulf(1)@N 1977.638925 1978.7915 H(119)C(73)N(5)O(55)S(1) 1977.638925 H(119)C(73)N(5)O(55)S(1) N-linked glycosylation 1556 0.5 +dHex(3)Hex(4)HexNAc(4)Sulf(1)@N 1978.659326 1979.8193 H(122)C(74)N(4)O(55)S(1) 1978.659326 H(122)C(74)N(4)O(55)S(1) N-linked glycosylation 1557 0.5 +Hex(3)HexNAc(7)Sulf(1)@N 1987.670893 1988.8326 H(121)C(74)N(7)O(53)S(1) 1987.670893 H(121)C(74)N(7)O(53)S(1) N-linked glycosylation 1558 0.5 +Hex(6)HexNAc(5)@N 1987.713804 1988.8062 H(125)C(76)N(5)O(55) 1987.713804 H(125)C(76)N(5)O(55) N-linked glycosylation 1559 0.5 +Hex(5)HexNAc(4)NeuAc(1)Sulf(1)@N 1993.633839 1994.7909 H(119)C(73)N(5)O(56)S(1) 1993.633839 H(119)C(73)N(5)O(56)S(1) N-linked glycosylation 1560 0.5 +Hex(3)HexNAc(6)NeuAc(1)@N 1995.730123 1996.8315 H(125)C(77)N(7)O(53) 1995.730123 H(125)C(77)N(7)O(53) N-linked glycosylation 1561 0.5 +dHex(2)Hex(3)HexNAc(6)@N 1996.750524 1997.8593 H(128)C(78)N(6)O(53) 1996.750524 H(128)C(78)N(6)O(53) N-linked glycosylation 1562 0.5 +Hex(1)HexNAc(1)NeuGc(1)@S 672.222527 672.5871 H(40)C(25)N(2)O(19) 672.222527 H(40)C(25)N(2)O(19) O-linked glycosylation 1563 0.5 +Hex(1)HexNAc(1)NeuGc(1)@T 672.222527 672.5871 H(40)C(25)N(2)O(19) 672.222527 H(40)C(25)N(2)O(19) O-linked glycosylation 1563 0.5 +dHex(1)Hex(2)HexNAc(1)@S 673.242928 673.6149 H(43)C(26)N(1)O(19) 673.242928 H(43)C(26)N(1)O(19) O-linked glycosylation 1564 0.5 +dHex(1)Hex(2)HexNAc(1)@T 673.242928 673.6149 H(43)C(26)N(1)O(19) 673.242928 H(43)C(26)N(1)O(19) O-linked glycosylation 1564 0.5 +HexNAc(3)Sulf(1)@T 689.194932 689.6408 H(39)C(24)N(3)O(18)S(1) 689.194932 H(39)C(24)N(3)O(18)S(1) O-linked glycosylation 1565 0.5 +HexNAc(3)Sulf(1)@S 689.194932 689.6408 H(39)C(24)N(3)O(18)S(1) 689.194932 H(39)C(24)N(3)O(18)S(1) O-linked glycosylation 1565 0.5 +Hex(3)HexNAc(1)@T 689.237843 689.6143 H(43)C(26)N(1)O(20) 689.237843 H(43)C(26)N(1)O(20) O-linked glycosylation 1566 0.5 +Hex(3)HexNAc(1)@S 689.237843 689.6143 H(43)C(26)N(1)O(20) 689.237843 H(43)C(26)N(1)O(20) O-linked glycosylation 1566 0.5 +Hex(3)HexNAc(1)@N 689.237843 689.6143 H(43)C(26)N(1)O(20) 689.237843 H(43)C(26)N(1)O(20) N-linked glycosylation 1566 0.5 +Hex(1)HexNAc(1)Kdn(1)Sulf(1)@T 695.157878 695.599 H(37)C(23)N(1)O(21)S(1) 695.157878 H(37)C(23)N(1)O(21)S(1) O-linked glycosylation 1567 0.5 +Hex(1)HexNAc(1)Kdn(1)Sulf(1)@S 695.157878 695.599 H(37)C(23)N(1)O(21)S(1) 695.157878 H(37)C(23)N(1)O(21)S(1) O-linked glycosylation 1567 0.5 +HexNAc(2)NeuAc(1)@S 697.254162 697.6396 H(43)C(27)N(3)O(18) 697.254162 H(43)C(27)N(3)O(18) O-linked glycosylation 1568 0.5 +HexNAc(2)NeuAc(1)@T 697.254162 697.6396 H(43)C(27)N(3)O(18) 697.254162 H(43)C(27)N(3)O(18) O-linked glycosylation 1568 0.5 +HexNAc(1)Kdn(2)@T 703.217108 703.5978 H(41)C(26)N(1)O(21) 703.217108 H(41)C(26)N(1)O(21) O-linked glycosylation 1570 0.5 +HexNAc(1)Kdn(2)@S 703.217108 703.5978 H(41)C(26)N(1)O(21) 703.217108 H(41)C(26)N(1)O(21) O-linked glycosylation 1570 0.5 +Hex(3)HexNAc(1)Me(1)@S 703.253493 703.6409 H(45)C(27)N(1)O(20) 703.253493 H(45)C(27)N(1)O(20) O-linked glycosylation 1571 0.5 +Hex(3)HexNAc(1)Me(1)@T 703.253493 703.6409 H(45)C(27)N(1)O(20) 703.253493 H(45)C(27)N(1)O(20) O-linked glycosylation 1571 0.5 +Hex(2)HexA(1)Pent(1)Sulf(1)@T 712.136808 712.5831 H(36)C(23)O(23)S(1) 712.136808 H(36)C(23)O(23)S(1) O-linked glycosylation 1572 0.5 +Hex(2)HexA(1)Pent(1)Sulf(1)@S 712.136808 712.5831 H(36)C(23)O(23)S(1) 712.136808 H(36)C(23)O(23)S(1) O-linked glycosylation 1572 0.5 +HexNAc(2)NeuGc(1)@S 713.249076 713.639 H(43)C(27)N(3)O(19) 713.249076 H(43)C(27)N(3)O(19) O-linked glycosylation 1573 0.5 +HexNAc(2)NeuGc(1)@T 713.249076 713.639 H(43)C(27)N(3)O(19) 713.249076 H(43)C(27)N(3)O(19) O-linked glycosylation 1573 0.5 +Hex(4)Phos(1)@T 728.177625 728.5423 H(41)C(24)O(23)P(1) 728.177625 H(41)C(24)O(23)P(1) O-linked glycosylation 1575 0.5 +Hex(4)Phos(1)@S 728.177625 728.5423 H(41)C(24)O(23)P(1) 728.177625 H(41)C(24)O(23)P(1) O-linked glycosylation 1575 0.5 +Hex(1)HexNAc(1)NeuAc(1)Sulf(1)@T 736.184427 736.6509 H(40)C(25)N(2)O(21)S(1) 736.184427 H(40)C(25)N(2)O(21)S(1) O-linked glycosylation 1577 0.5 +Hex(1)HexNAc(1)NeuAc(1)Sulf(1)@S 736.184427 736.6509 H(40)C(25)N(2)O(21)S(1) 736.184427 H(40)C(25)N(2)O(21)S(1) O-linked glycosylation 1577 0.5 +Hex(1)HexA(1)HexNAc(2)@S 744.243657 744.6498 H(44)C(28)N(2)O(21) 744.243657 H(44)C(28)N(2)O(21) O-linked glycosylation 1578 0.5 +Hex(1)HexA(1)HexNAc(2)@T 744.243657 744.6498 H(44)C(28)N(2)O(21) 744.243657 H(44)C(28)N(2)O(21) O-linked glycosylation 1578 0.5 +dHex(1)Hex(2)HexNAc(1)Sulf(1)@T 753.199743 753.6781 H(43)C(26)N(1)O(22)S(1) 753.199743 H(43)C(26)N(1)O(22)S(1) O-linked glycosylation 1579 0.5 +dHex(1)Hex(2)HexNAc(1)Sulf(1)@S 753.199743 753.6781 H(43)C(26)N(1)O(22)S(1) 753.199743 H(43)C(26)N(1)O(22)S(1) O-linked glycosylation 1579 0.5 +dHex(1)HexNAc(3)@S 755.296027 755.7188 H(49)C(30)N(3)O(19) 755.296027 H(49)C(30)N(3)O(19) O-linked glycosylation 1580 0.5 +dHex(1)HexNAc(3)@T 755.296027 755.7188 H(49)C(30)N(3)O(19) 755.296027 H(49)C(30)N(3)O(19) O-linked glycosylation 1580 0.5 +dHex(1)Hex(1)HexNAc(1)Kdn(1)@T 761.258973 761.677 H(47)C(29)N(1)O(22) 761.258973 H(47)C(29)N(1)O(22) O-linked glycosylation 1581 0.5 +dHex(1)Hex(1)HexNAc(1)Kdn(1)@S 761.258973 761.677 H(47)C(29)N(1)O(22) 761.258973 H(47)C(29)N(1)O(22) O-linked glycosylation 1581 0.5 +Hex(1)HexNAc(3)@S 771.290941 771.7182 H(49)C(30)N(3)O(20) 771.290941 H(49)C(30)N(3)O(20) O-linked glycosylation 1582 0.5 +Hex(1)HexNAc(3)@T 771.290941 771.7182 H(49)C(30)N(3)O(20) 771.290941 H(49)C(30)N(3)O(20) O-linked glycosylation 1582 0.5 +HexNAc(2)NeuAc(1)Sulf(1)@T 777.210976 777.7028 H(43)C(27)N(3)O(21)S(1) 777.210976 H(43)C(27)N(3)O(21)S(1) O-linked glycosylation 1583 0.5 +HexNAc(2)NeuAc(1)Sulf(1)@S 777.210976 777.7028 H(43)C(27)N(3)O(21)S(1) 777.210976 H(43)C(27)N(3)O(21)S(1) O-linked glycosylation 1583 0.5 +dHex(2)Hex(3)@S 778.274288 778.7042 H(50)C(30)O(23) 778.274288 H(50)C(30)O(23) O-linked glycosylation 1584 0.5 +dHex(2)Hex(3)@T 778.274288 778.7042 H(50)C(30)O(23) 778.274288 H(50)C(30)O(23) O-linked glycosylation 1584 0.5 +Hex(2)HexA(1)HexNAc(1)Sulf(1)@T 783.173922 783.661 H(41)C(26)N(1)O(24)S(1) 783.173922 H(41)C(26)N(1)O(24)S(1) O-linked glycosylation 1585 0.5 +Hex(2)HexA(1)HexNAc(1)Sulf(1)@S 783.173922 783.661 H(41)C(26)N(1)O(24)S(1) 783.173922 H(41)C(26)N(1)O(24)S(1) O-linked glycosylation 1585 0.5 +dHex(2)Hex(2)HexA(1)@S 792.253553 792.6877 H(48)C(30)O(24) 792.253553 H(48)C(30)O(24) O-linked glycosylation 1586 0.5 +dHex(2)Hex(2)HexA(1)@T 792.253553 792.6877 H(48)C(30)O(24) 792.253553 H(48)C(30)O(24) O-linked glycosylation 1586 0.5 +dHex(1)Hex(1)HexNAc(2)Sulf(1)@T 794.226292 794.73 H(46)C(28)N(2)O(22)S(1) 794.226292 H(46)C(28)N(2)O(22)S(1) O-linked glycosylation 1587 0.5 +dHex(1)Hex(1)HexNAc(2)Sulf(1)@S 794.226292 794.73 H(46)C(28)N(2)O(22)S(1) 794.226292 H(46)C(28)N(2)O(22)S(1) O-linked glycosylation 1587 0.5 +dHex(1)Hex(1)HexNAc(1)NeuAc(1)@S 802.285522 802.7289 H(50)C(31)N(2)O(22) 802.285522 H(50)C(31)N(2)O(22) O-linked glycosylation 1588 0.5 +dHex(1)Hex(1)HexNAc(1)NeuAc(1)@T 802.285522 802.7289 H(50)C(31)N(2)O(22) 802.285522 H(50)C(31)N(2)O(22) O-linked glycosylation 1588 0.5 +Hex(2)HexNAc(2)Sulf(1)@T 810.221207 810.7294 H(46)C(28)N(2)O(23)S(1) 810.221207 H(46)C(28)N(2)O(23)S(1) O-linked glycosylation 1589 0.5 +Hex(2)HexNAc(2)Sulf(1)@S 810.221207 810.7294 H(46)C(28)N(2)O(23)S(1) 810.221207 H(46)C(28)N(2)O(23)S(1) O-linked glycosylation 1589 0.5 +Hex(5)@S 810.264117 810.703 H(50)C(30)O(25) 810.264117 H(50)C(30)O(25) O-linked glycosylation 1590 0.5 +Hex(5)@T 810.264117 810.703 H(50)C(30)O(25) 810.264117 H(50)C(30)O(25) O-linked glycosylation 1590 0.5 +HexNAc(4)@S 812.31749 812.7701 H(52)C(32)N(4)O(20) 812.31749 H(52)C(32)N(4)O(20) O-linked glycosylation 1591 0.5 +HexNAc(4)@T 812.31749 812.7701 H(52)C(32)N(4)O(20) 812.31749 H(52)C(32)N(4)O(20) O-linked glycosylation 1591 0.5 +HexNAc(1)NeuGc(2)@S 817.260035 817.7005 H(47)C(30)N(3)O(23) 817.260035 H(47)C(30)N(3)O(23) O-linked glycosylation 1592 0.5 +HexNAc(1)NeuGc(2)@T 817.260035 817.7005 H(47)C(30)N(3)O(23) 817.260035 H(47)C(30)N(3)O(23) O-linked glycosylation 1592 0.5 +dHex(1)Hex(1)HexNAc(1)NeuGc(1)@T 818.280436 818.7283 H(50)C(31)N(2)O(23) 818.280436 H(50)C(31)N(2)O(23) O-linked glycosylation 1593 0.5 +dHex(1)Hex(1)HexNAc(1)NeuGc(1)@S 818.280436 818.7283 H(50)C(31)N(2)O(23) 818.280436 H(50)C(31)N(2)O(23) O-linked glycosylation 1593 0.5 +dHex(2)Hex(2)HexNAc(1)@S 819.300837 819.7561 H(53)C(32)N(1)O(23) 819.300837 H(53)C(32)N(1)O(23) O-linked glycosylation 1594 0.5 +dHex(2)Hex(2)HexNAc(1)@T 819.300837 819.7561 H(53)C(32)N(1)O(23) 819.300837 H(53)C(32)N(1)O(23) O-linked glycosylation 1594 0.5 +Hex(2)HexNAc(1)NeuGc(1)@S 834.275351 834.7277 H(50)C(31)N(2)O(24) 834.275351 H(50)C(31)N(2)O(24) O-linked glycosylation 1595 0.5 +Hex(2)HexNAc(1)NeuGc(1)@T 834.275351 834.7277 H(50)C(31)N(2)O(24) 834.275351 H(50)C(31)N(2)O(24) O-linked glycosylation 1595 0.5 +dHex(1)Hex(3)HexNAc(1)@S 835.295752 835.7555 H(53)C(32)N(1)O(24) 835.295752 H(53)C(32)N(1)O(24) O-linked glycosylation 1596 0.5 +dHex(1)Hex(3)HexNAc(1)@T 835.295752 835.7555 H(53)C(32)N(1)O(24) 835.295752 H(53)C(32)N(1)O(24) O-linked glycosylation 1596 0.5 +dHex(1)Hex(2)HexA(1)HexNAc(1)@S 849.275017 849.739 H(51)C(32)N(1)O(25) 849.275017 H(51)C(32)N(1)O(25) O-linked glycosylation 1597 0.5 +dHex(1)Hex(2)HexA(1)HexNAc(1)@T 849.275017 849.739 H(51)C(32)N(1)O(25) 849.275017 H(51)C(32)N(1)O(25) O-linked glycosylation 1597 0.5 +Hex(1)HexNAc(3)Sulf(1)@T 851.247756 851.7814 H(49)C(30)N(3)O(23)S(1) 851.247756 H(49)C(30)N(3)O(23)S(1) O-linked glycosylation 1598 0.5 +Hex(1)HexNAc(3)Sulf(1)@S 851.247756 851.7814 H(49)C(30)N(3)O(23)S(1) 851.247756 H(49)C(30)N(3)O(23)S(1) O-linked glycosylation 1598 0.5 +Hex(4)HexNAc(1)@T 851.290667 851.7549 H(53)C(32)N(1)O(25) 851.290667 H(53)C(32)N(1)O(25) O-linked glycosylation 1599 0.5 +Hex(4)HexNAc(1)@S 851.290667 851.7549 H(53)C(32)N(1)O(25) 851.290667 H(53)C(32)N(1)O(25) O-linked glycosylation 1599 0.5 +Hex(4)HexNAc(1)@N 851.290667 851.7549 H(53)C(32)N(1)O(25) 851.290667 H(53)C(32)N(1)O(25) N-linked glycosylation 1599 0.5 +Hex(1)HexNAc(2)NeuAc(1)@S 859.306985 859.7802 H(53)C(33)N(3)O(23) 859.306985 H(53)C(33)N(3)O(23) O-linked glycosylation 1600 0.5 +Hex(1)HexNAc(2)NeuAc(1)@T 859.306985 859.7802 H(53)C(33)N(3)O(23) 859.306985 H(53)C(33)N(3)O(23) O-linked glycosylation 1600 0.5 +Hex(1)HexNAc(2)NeuGc(1)@S 875.3019 875.7796 H(53)C(33)N(3)O(24) 875.3019 H(53)C(33)N(3)O(24) O-linked glycosylation 1602 0.5 +Hex(1)HexNAc(2)NeuGc(1)@T 875.3019 875.7796 H(53)C(33)N(3)O(24) 875.3019 H(53)C(33)N(3)O(24) O-linked glycosylation 1602 0.5 +Hex(5)Phos(1)@T 890.230448 890.6829 H(51)C(30)O(28)P(1) 890.230448 H(51)C(30)O(28)P(1) O-linked glycosylation 1604 0.5 +Hex(5)Phos(1)@S 890.230448 890.6829 H(51)C(30)O(28)P(1) 890.230448 H(51)C(30)O(28)P(1) O-linked glycosylation 1604 0.5 +dHex(2)Hex(1)HexNAc(1)Kdn(1)@T 907.316881 907.8182 H(57)C(35)N(1)O(26) 907.316881 H(57)C(35)N(1)O(26) O-linked glycosylation 1606 0.5 +dHex(2)Hex(1)HexNAc(1)Kdn(1)@S 907.316881 907.8182 H(57)C(35)N(1)O(26) 907.316881 H(57)C(35)N(1)O(26) O-linked glycosylation 1606 0.5 +dHex(1)Hex(3)HexNAc(1)Sulf(1)@T 915.252567 915.8187 H(53)C(32)N(1)O(27)S(1) 915.252567 H(53)C(32)N(1)O(27)S(1) O-linked glycosylation 1607 0.5 +dHex(1)Hex(3)HexNAc(1)Sulf(1)@S 915.252567 915.8187 H(53)C(32)N(1)O(27)S(1) 915.252567 H(53)C(32)N(1)O(27)S(1) O-linked glycosylation 1607 0.5 +dHex(1)Hex(1)HexNAc(3)@S 917.34885 917.8594 H(59)C(36)N(3)O(24) 917.34885 H(59)C(36)N(3)O(24) O-linked glycosylation 1608 0.5 +dHex(1)Hex(1)HexNAc(3)@T 917.34885 917.8594 H(59)C(36)N(3)O(24) 917.34885 H(59)C(36)N(3)O(24) O-linked glycosylation 1608 0.5 +dHex(1)Hex(2)HexA(1)HexNAc(1)Sulf(1)@T 929.231831 929.8022 H(51)C(32)N(1)O(28)S(1) 929.231831 H(51)C(32)N(1)O(28)S(1) O-linked glycosylation 1609 0.5 +dHex(1)Hex(2)HexA(1)HexNAc(1)Sulf(1)@S 929.231831 929.8022 H(51)C(32)N(1)O(28)S(1) 929.231831 H(51)C(32)N(1)O(28)S(1) O-linked glycosylation 1609 0.5 +Hex(2)HexNAc(3)@S 933.343765 933.8588 H(59)C(36)N(3)O(25) 933.343765 H(59)C(36)N(3)O(25) O-linked glycosylation 1610 0.5 +Hex(2)HexNAc(3)@N 933.343765 933.8588 H(59)C(36)N(3)O(25) 933.343765 H(59)C(36)N(3)O(25) N-linked glycosylation 1610 0.5 +Hex(2)HexNAc(3)@T 933.343765 933.8588 H(59)C(36)N(3)O(25) 933.343765 H(59)C(36)N(3)O(25) O-linked glycosylation 1610 0.5 +Hex(1)HexNAc(2)NeuAc(1)Sulf(1)@T 939.2638 939.8434 H(53)C(33)N(3)O(26)S(1) 939.2638 H(53)C(33)N(3)O(26)S(1) O-linked glycosylation 1611 0.5 +Hex(1)HexNAc(2)NeuAc(1)Sulf(1)@S 939.2638 939.8434 H(53)C(33)N(3)O(26)S(1) 939.2638 H(53)C(33)N(3)O(26)S(1) O-linked glycosylation 1611 0.5 +dHex(2)Hex(4)@S 940.327112 940.8448 H(60)C(36)O(28) 940.327112 H(60)C(36)O(28) O-linked glycosylation 1612 0.5 +dHex(2)Hex(4)@T 940.327112 940.8448 H(60)C(36)O(28) 940.327112 H(60)C(36)O(28) O-linked glycosylation 1612 0.5 +Hex(1)HexNAc(1)NeuAc(1)Ac(1)@T 698.238177 698.6244 H(42)C(27)N(2)O(19) 698.238177 H(42)C(27)N(2)O(19) O-linked glycosylation 1786 0.5 +Hex(1)HexNAc(1)NeuAc(1)Ac(1)@S 698.238177 698.6244 H(42)C(27)N(2)O(19) 698.238177 H(42)C(27)N(2)O(19) O-linked glycosylation 1786 0.5 +dHex(2)HexNAc(2)Kdn(1)@T 948.34343 948.8701 H(60)C(37)N(2)O(26) 948.34343 H(60)C(37)N(2)O(26) O-linked glycosylation 1614 0.5 +dHex(2)HexNAc(2)Kdn(1)@S 948.34343 948.8701 H(60)C(37)N(2)O(26) 948.34343 H(60)C(37)N(2)O(26) O-linked glycosylation 1614 0.5 +dHex(1)Hex(2)HexNAc(2)Sulf(1)@T 956.279116 956.8706 H(56)C(34)N(2)O(27)S(1) 956.279116 H(56)C(34)N(2)O(27)S(1) O-linked glycosylation 1615 0.5 +dHex(1)Hex(2)HexNAc(2)Sulf(1)@S 956.279116 956.8706 H(56)C(34)N(2)O(27)S(1) 956.279116 H(56)C(34)N(2)O(27)S(1) O-linked glycosylation 1615 0.5 +dHex(1)HexNAc(4)@S 958.375399 958.9113 H(62)C(38)N(4)O(24) 958.375399 H(62)C(38)N(4)O(24) O-linked glycosylation 1616 0.5 +dHex(1)HexNAc(4)@T 958.375399 958.9113 H(62)C(38)N(4)O(24) 958.375399 H(62)C(38)N(4)O(24) O-linked glycosylation 1616 0.5 +Hex(1)HexNAc(1)NeuAc(1)NeuGc(1)@S 963.317944 963.8417 H(57)C(36)N(3)O(27) 963.317944 H(57)C(36)N(3)O(27) O-linked glycosylation 1617 0.5 +Hex(1)HexNAc(1)NeuAc(1)NeuGc(1)@T 963.317944 963.8417 H(57)C(36)N(3)O(27) 963.317944 H(57)C(36)N(3)O(27) O-linked glycosylation 1617 0.5 +dHex(1)Hex(1)HexNAc(2)Kdn(1)@T 964.338345 964.8695 H(60)C(37)N(2)O(27) 964.338345 H(60)C(37)N(2)O(27) O-linked glycosylation 1618 0.5 +dHex(1)Hex(1)HexNAc(2)Kdn(1)@S 964.338345 964.8695 H(60)C(37)N(2)O(27) 964.338345 H(60)C(37)N(2)O(27) O-linked glycosylation 1618 0.5 +Hex(1)HexNAc(1)NeuGc(2)@S 979.312859 979.8411 H(57)C(36)N(3)O(28) 979.312859 H(57)C(36)N(3)O(28) O-linked glycosylation 1619 0.5 +Hex(1)HexNAc(1)NeuGc(2)@T 979.312859 979.8411 H(57)C(36)N(3)O(28) 979.312859 H(57)C(36)N(3)O(28) O-linked glycosylation 1619 0.5 +Hex(1)HexNAc(1)NeuAc(2)Ac(1)@T 989.333594 989.879 H(59)C(38)N(3)O(27) 989.333594 H(59)C(38)N(3)O(27) O-linked glycosylation 1620 0.5 +Hex(1)HexNAc(1)NeuAc(2)Ac(1)@S 989.333594 989.879 H(59)C(38)N(3)O(27) 989.333594 H(59)C(38)N(3)O(27) O-linked glycosylation 1620 0.5 +dHex(2)Hex(2)HexA(1)HexNAc(1)@S 995.332925 995.8802 H(61)C(38)N(1)O(29) 995.332925 H(61)C(38)N(1)O(29) O-linked glycosylation 1621 0.5 +dHex(2)Hex(2)HexA(1)HexNAc(1)@T 995.332925 995.8802 H(61)C(38)N(1)O(29) 995.332925 H(61)C(38)N(1)O(29) O-linked glycosylation 1621 0.5 +dHex(1)Hex(1)HexNAc(3)Sulf(1)@T 997.305665 997.9226 H(59)C(36)N(3)O(27)S(1) 997.305665 H(59)C(36)N(3)O(27)S(1) O-linked glycosylation 1622 0.5 +dHex(1)Hex(1)HexNAc(3)Sulf(1)@S 997.305665 997.9226 H(59)C(36)N(3)O(27)S(1) 997.305665 H(59)C(36)N(3)O(27)S(1) O-linked glycosylation 1622 0.5 +Hex(2)HexA(1)NeuAc(1)Pent(1)Sulf(1)@T 1003.232225 1003.8377 H(53)C(34)N(1)O(31)S(1) 1003.232225 H(53)C(34)N(1)O(31)S(1) O-linked glycosylation 1623 0.5 +Hex(2)HexA(1)NeuAc(1)Pent(1)Sulf(1)@S 1003.232225 1003.8377 H(53)C(34)N(1)O(31)S(1) 1003.232225 H(53)C(34)N(1)O(31)S(1) O-linked glycosylation 1623 0.5 +dHex(1)Hex(1)HexNAc(2)NeuAc(1)@S 1005.364894 1005.9214 H(63)C(39)N(3)O(27) 1005.364894 H(63)C(39)N(3)O(27) O-linked glycosylation 1624 0.5 +dHex(1)Hex(1)HexNAc(2)NeuAc(1)@T 1005.364894 1005.9214 H(63)C(39)N(3)O(27) 1005.364894 H(63)C(39)N(3)O(27) O-linked glycosylation 1624 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(1)@S 1011.32784 1011.8796 H(61)C(38)N(1)O(30) 1011.32784 H(61)C(38)N(1)O(30) O-linked glycosylation 1625 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(1)@T 1011.32784 1011.8796 H(61)C(38)N(1)O(30) 1011.32784 H(61)C(38)N(1)O(30) O-linked glycosylation 1625 0.5 +Hex(2)HexNAc(3)Sulf(1)@T 1013.300579 1013.922 H(59)C(36)N(3)O(28)S(1) 1013.300579 H(59)C(36)N(3)O(28)S(1) O-linked glycosylation 1626 0.5 +Hex(2)HexNAc(3)Sulf(1)@S 1013.300579 1013.922 H(59)C(36)N(3)O(28)S(1) 1013.300579 H(59)C(36)N(3)O(28)S(1) O-linked glycosylation 1626 0.5 +Hex(5)HexNAc(1)@T 1013.34349 1013.8955 H(63)C(38)N(1)O(30) 1013.34349 H(63)C(38)N(1)O(30) O-linked glycosylation 1627 0.5 +Hex(5)HexNAc(1)@S 1013.34349 1013.8955 H(63)C(38)N(1)O(30) 1013.34349 H(63)C(38)N(1)O(30) O-linked glycosylation 1627 0.5 +Hex(5)HexNAc(1)@N 1013.34349 1013.8955 H(63)C(38)N(1)O(30) 1013.34349 H(63)C(38)N(1)O(30) N-linked glycosylation 1627 0.5 +HexNAc(5)@S 1015.396863 1015.9626 H(65)C(40)N(5)O(25) 1015.396863 H(65)C(40)N(5)O(25) O-linked glycosylation 1628 0.5 +HexNAc(5)@T 1015.396863 1015.9626 H(65)C(40)N(5)O(25) 1015.396863 H(65)C(40)N(5)O(25) O-linked glycosylation 1628 0.5 +Hex(1)HexNAc(1)NeuAc(2)Ac(2)@T 1031.344159 1031.9156 H(61)C(40)N(3)O(28) 1031.344159 H(61)C(40)N(3)O(28) O-linked glycosylation 1630 0.5 +Hex(1)HexNAc(1)NeuAc(2)Ac(2)@S 1031.344159 1031.9156 H(61)C(40)N(3)O(28) 1031.344159 H(61)C(40)N(3)O(28) O-linked glycosylation 1630 0.5 +Hex(2)HexNAc(2)NeuGc(1)@S 1037.354723 1037.9202 H(63)C(39)N(3)O(29) 1037.354723 H(63)C(39)N(3)O(29) O-linked glycosylation 1631 0.5 +Hex(2)HexNAc(2)NeuGc(1)@T 1037.354723 1037.9202 H(63)C(39)N(3)O(29) 1037.354723 H(63)C(39)N(3)O(29) O-linked glycosylation 1631 0.5 +Hex(5)Phos(3)@T 1050.16311 1050.6427 H(53)C(30)O(34)P(3) 1050.16311 H(53)C(30)O(34)P(3) O-linked glycosylation 1632 0.5 +Hex(5)Phos(3)@S 1050.16311 1050.6427 H(53)C(30)O(34)P(3) 1050.16311 H(53)C(30)O(34)P(3) O-linked glycosylation 1632 0.5 +Hex(6)Phos(1)@T 1052.283272 1052.8235 H(61)C(36)O(33)P(1) 1052.283272 H(61)C(36)O(33)P(1) O-linked glycosylation 1633 0.5 +Hex(6)Phos(1)@S 1052.283272 1052.8235 H(61)C(36)O(33)P(1) 1052.283272 H(61)C(36)O(33)P(1) O-linked glycosylation 1633 0.5 +dHex(1)Hex(2)HexA(1)HexNAc(2)@S 1052.354389 1052.9316 H(64)C(40)N(2)O(30) 1052.354389 H(64)C(40)N(2)O(30) O-linked glycosylation 1634 0.5 +dHex(1)Hex(2)HexA(1)HexNAc(2)@T 1052.354389 1052.9316 H(64)C(40)N(2)O(30) 1052.354389 H(64)C(40)N(2)O(30) O-linked glycosylation 1634 0.5 +dHex(2)Hex(3)HexNAc(1)Sulf(1)@T 1061.310475 1061.9599 H(63)C(38)N(1)O(31)S(1) 1061.310475 H(63)C(38)N(1)O(31)S(1) O-linked glycosylation 1635 0.5 +dHex(2)Hex(3)HexNAc(1)Sulf(1)@S 1061.310475 1061.9599 H(63)C(38)N(1)O(31)S(1) 1061.310475 H(63)C(38)N(1)O(31)S(1) O-linked glycosylation 1635 0.5 +Hex(1)HexNAc(3)NeuAc(1)@S 1062.386358 1062.9727 H(66)C(41)N(4)O(28) 1062.386358 H(66)C(41)N(4)O(28) O-linked glycosylation 1636 0.5 +Hex(1)HexNAc(3)NeuAc(1)@T 1062.386358 1062.9727 H(66)C(41)N(4)O(28) 1062.386358 H(66)C(41)N(4)O(28) O-linked glycosylation 1636 0.5 +dHex(2)Hex(1)HexNAc(3)@S 1063.406759 1064.0006 H(69)C(42)N(3)O(28) 1063.406759 H(69)C(42)N(3)O(28) O-linked glycosylation 1637 0.5 +dHex(2)Hex(1)HexNAc(3)@T 1063.406759 1064.0006 H(69)C(42)N(3)O(28) 1063.406759 H(69)C(42)N(3)O(28) O-linked glycosylation 1637 0.5 +Hex(1)HexNAc(3)NeuGc(1)@S 1078.381273 1078.9721 H(66)C(41)N(4)O(29) 1078.381273 H(66)C(41)N(4)O(29) O-linked glycosylation 1638 0.5 +Hex(1)HexNAc(3)NeuGc(1)@T 1078.381273 1078.9721 H(66)C(41)N(4)O(29) 1078.381273 H(66)C(41)N(4)O(29) O-linked glycosylation 1638 0.5 +dHex(1)Hex(1)HexNAc(2)NeuAc(1)Sulf(1)@T 1085.321709 1085.9846 H(63)C(39)N(3)O(30)S(1) 1085.321709 H(63)C(39)N(3)O(30)S(1) O-linked glycosylation 1639 0.5 +dHex(1)Hex(1)HexNAc(2)NeuAc(1)Sulf(1)@S 1085.321709 1085.9846 H(63)C(39)N(3)O(30)S(1) 1085.321709 H(63)C(39)N(3)O(30)S(1) O-linked glycosylation 1639 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(1)Sulf(1)@T 1091.284655 1091.9428 H(61)C(38)N(1)O(33)S(1) 1091.284655 H(61)C(38)N(1)O(33)S(1) O-linked glycosylation 1640 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(1)Sulf(1)@S 1091.284655 1091.9428 H(61)C(38)N(1)O(33)S(1) 1091.284655 H(61)C(38)N(1)O(33)S(1) O-linked glycosylation 1640 0.5 +dHex(1)Hex(1)HexA(1)HexNAc(3)@S 1093.380938 1093.9835 H(67)C(42)N(3)O(30) 1093.380938 H(67)C(42)N(3)O(30) O-linked glycosylation 1641 0.5 +dHex(1)Hex(1)HexA(1)HexNAc(3)@T 1093.380938 1093.9835 H(67)C(42)N(3)O(30) 1093.380938 H(67)C(42)N(3)O(30) O-linked glycosylation 1641 0.5 +Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@T 1101.316623 1101.984 H(63)C(39)N(3)O(31)S(1) 1101.316623 H(63)C(39)N(3)O(31)S(1) O-linked glycosylation 1642 0.5 +Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@S 1101.316623 1101.984 H(63)C(39)N(3)O(31)S(1) 1101.316623 H(63)C(39)N(3)O(31)S(1) O-linked glycosylation 1642 0.5 +dHex(2)Hex(2)HexNAc(2)Sulf(1)@T 1102.337025 1103.0118 H(66)C(40)N(2)O(31)S(1) 1102.337025 H(66)C(40)N(2)O(31)S(1) O-linked glycosylation 1643 0.5 +dHex(2)Hex(2)HexNAc(2)Sulf(1)@S 1102.337025 1103.0118 H(66)C(40)N(2)O(31)S(1) 1102.337025 H(66)C(40)N(2)O(31)S(1) O-linked glycosylation 1643 0.5 +dHex(2)Hex(1)HexNAc(2)Kdn(1)@T 1110.396254 1111.0107 H(70)C(43)N(2)O(31) 1110.396254 H(70)C(43)N(2)O(31) O-linked glycosylation 1644 0.5 +dHex(2)Hex(1)HexNAc(2)Kdn(1)@S 1110.396254 1111.0107 H(70)C(43)N(2)O(31) 1110.396254 H(70)C(43)N(2)O(31) O-linked glycosylation 1644 0.5 +dHex(1)Hex(1)HexNAc(4)@S 1120.428223 1121.0519 H(72)C(44)N(4)O(29) 1120.428223 H(72)C(44)N(4)O(29) O-linked glycosylation 1645 0.5 +dHex(1)Hex(1)HexNAc(4)@T 1120.428223 1121.0519 H(72)C(44)N(4)O(29) 1120.428223 H(72)C(44)N(4)O(29) O-linked glycosylation 1645 0.5 +Hex(2)HexNAc(4)@T 1136.423137 1137.0513 H(72)C(44)N(4)O(30) 1136.423137 H(72)C(44)N(4)O(30) O-linked glycosylation 1646 0.5 +Hex(2)HexNAc(4)@S 1136.423137 1137.0513 H(72)C(44)N(4)O(30) 1136.423137 H(72)C(44)N(4)O(30) O-linked glycosylation 1646 0.5 +Hex(2)HexNAc(4)@N 1136.423137 1137.0513 H(72)C(44)N(4)O(30) 1136.423137 H(72)C(44)N(4)O(30) N-linked glycosylation 1646 0.5 +Hex(2)HexNAc(1)NeuGc(2)@S 1141.365682 1141.9817 H(67)C(42)N(3)O(33) 1141.365682 H(67)C(42)N(3)O(33) O-linked glycosylation 1647 0.5 +Hex(2)HexNAc(1)NeuGc(2)@T 1141.365682 1141.9817 H(67)C(42)N(3)O(33) 1141.365682 H(67)C(42)N(3)O(33) O-linked glycosylation 1647 0.5 +dHex(2)Hex(4)HexNAc(1)@S 1143.406484 1144.0373 H(73)C(44)N(1)O(33) 1143.406484 H(73)C(44)N(1)O(33) O-linked glycosylation 1648 0.5 +dHex(2)Hex(4)HexNAc(1)@T 1143.406484 1144.0373 H(73)C(44)N(1)O(33) 1143.406484 H(73)C(44)N(1)O(33) O-linked glycosylation 1648 0.5 +Hex(1)HexNAc(2)NeuAc(2)@S 1150.402402 1151.0348 H(70)C(44)N(4)O(31) 1150.402402 H(70)C(44)N(4)O(31) O-linked glycosylation 1649 0.5 +Hex(1)HexNAc(2)NeuAc(2)@T 1150.402402 1151.0348 H(70)C(44)N(4)O(31) 1150.402402 H(70)C(44)N(4)O(31) O-linked glycosylation 1649 0.5 +dHex(2)Hex(1)HexNAc(2)NeuAc(1)@S 1151.422803 1152.0626 H(73)C(45)N(3)O(31) 1151.422803 H(73)C(45)N(3)O(31) O-linked glycosylation 1650 0.5 +dHex(2)Hex(1)HexNAc(2)NeuAc(1)@T 1151.422803 1152.0626 H(73)C(45)N(3)O(31) 1151.422803 H(73)C(45)N(3)O(31) O-linked glycosylation 1650 0.5 +dHex(1)Hex(2)HexNAc(3)Sulf(1)@T 1159.358488 1160.0632 H(69)C(42)N(3)O(32)S(1) 1159.358488 H(69)C(42)N(3)O(32)S(1) O-linked glycosylation 1651 0.5 +dHex(1)Hex(2)HexNAc(3)Sulf(1)@S 1159.358488 1160.0632 H(69)C(42)N(3)O(32)S(1) 1159.358488 H(69)C(42)N(3)O(32)S(1) O-linked glycosylation 1651 0.5 +dHex(1)HexNAc(5)@S 1161.454772 1162.1038 H(75)C(46)N(5)O(29) 1161.454772 H(75)C(46)N(5)O(29) O-linked glycosylation 1652 0.5 +dHex(1)HexNAc(5)@T 1161.454772 1162.1038 H(75)C(46)N(5)O(29) 1161.454772 H(75)C(46)N(5)O(29) O-linked glycosylation 1652 0.5 +dHex(2)Hex(1)HexNAc(2)NeuGc(1)@T 1167.417718 1168.062 H(73)C(45)N(3)O(32) 1167.417718 H(73)C(45)N(3)O(32) O-linked glycosylation 1653 0.5 +dHex(2)Hex(1)HexNAc(2)NeuGc(1)@S 1167.417718 1168.062 H(73)C(45)N(3)O(32) 1167.417718 H(73)C(45)N(3)O(32) O-linked glycosylation 1653 0.5 +dHex(3)Hex(2)HexNAc(2)@S 1168.438119 1169.0898 H(76)C(46)N(2)O(32) 1168.438119 H(76)C(46)N(2)O(32) O-linked glycosylation 1654 0.5 +dHex(3)Hex(2)HexNAc(2)@T 1168.438119 1169.0898 H(76)C(46)N(2)O(32) 1168.438119 H(76)C(46)N(2)O(32) O-linked glycosylation 1654 0.5 +Hex(3)HexNAc(3)Sulf(1)@T 1175.353403 1176.0626 H(69)C(42)N(3)O(33)S(1) 1175.353403 H(69)C(42)N(3)O(33)S(1) O-linked glycosylation 1655 0.5 +Hex(3)HexNAc(3)Sulf(1)@S 1175.353403 1176.0626 H(69)C(42)N(3)O(33)S(1) 1175.353403 H(69)C(42)N(3)O(33)S(1) O-linked glycosylation 1655 0.5 +Hex(3)HexNAc(3)Sulf(1)@N 1175.353403 1176.0626 H(69)C(42)N(3)O(33)S(1) 1175.353403 H(69)C(42)N(3)O(33)S(1) N-linked glycosylation 1655 0.5 +dHex(2)Hex(2)HexNAc(2)Sulf(2)@T 1182.293839 1183.075 H(66)C(40)N(2)O(34)S(2) 1182.293839 H(66)C(40)N(2)O(34)S(2) O-linked glycosylation 1656 0.5 +dHex(2)Hex(2)HexNAc(2)Sulf(2)@S 1182.293839 1183.075 H(66)C(40)N(2)O(34)S(2) 1182.293839 H(66)C(40)N(2)O(34)S(2) O-linked glycosylation 1656 0.5 +dHex(1)Hex(2)HexNAc(2)NeuGc(1)@N 1183.412632 1184.0614 H(73)C(45)N(3)O(33) 1183.412632 H(73)C(45)N(3)O(33) N-linked glycosylation 1657 0.5 +dHex(1)Hex(2)HexNAc(2)NeuGc(1)@T 1183.412632 1184.0614 H(73)C(45)N(3)O(33) 1183.412632 H(73)C(45)N(3)O(33) O-linked glycosylation 1657 0.5 +dHex(1)Hex(2)HexNAc(2)NeuGc(1)@S 1183.412632 1184.0614 H(73)C(45)N(3)O(33) 1183.412632 H(73)C(45)N(3)O(33) O-linked glycosylation 1657 0.5 +dHex(1)Hex(1)HexNAc(3)NeuAc(1)@T 1208.444267 1209.1139 H(76)C(47)N(4)O(32) 1208.444267 H(76)C(47)N(4)O(32) O-linked glycosylation 1658 0.5 +dHex(1)Hex(1)HexNAc(3)NeuAc(1)@S 1208.444267 1209.1139 H(76)C(47)N(4)O(32) 1208.444267 H(76)C(47)N(4)O(32) O-linked glycosylation 1658 0.5 +Hex(6)Phos(3)@T 1212.215934 1212.7833 H(63)C(36)O(39)P(3) 1212.215934 H(63)C(36)O(39)P(3) O-linked glycosylation 1659 0.5 +Hex(6)Phos(3)@S 1212.215934 1212.7833 H(63)C(36)O(39)P(3) 1212.215934 H(63)C(36)O(39)P(3) O-linked glycosylation 1659 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(2)@S 1214.407213 1215.0722 H(74)C(46)N(2)O(35) 1214.407213 H(74)C(46)N(2)O(35) O-linked glycosylation 1660 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(2)@T 1214.407213 1215.0722 H(74)C(46)N(2)O(35) 1214.407213 H(74)C(46)N(2)O(35) O-linked glycosylation 1660 0.5 +dHex(1)Hex(1)HexNAc(3)NeuGc(1)@T 1224.439181 1225.1133 H(76)C(47)N(4)O(33) 1224.439181 H(76)C(47)N(4)O(33) O-linked glycosylation 1661 0.5 +dHex(1)Hex(1)HexNAc(3)NeuGc(1)@S 1224.439181 1225.1133 H(76)C(47)N(4)O(33) 1224.439181 H(76)C(47)N(4)O(33) O-linked glycosylation 1661 0.5 +Hex(1)HexNAc(2)NeuAc(2)Sulf(1)@T 1230.359217 1231.098 H(70)C(44)N(4)O(34)S(1) 1230.359217 H(70)C(44)N(4)O(34)S(1) O-linked glycosylation 1662 0.5 +Hex(1)HexNAc(2)NeuAc(2)Sulf(1)@S 1230.359217 1231.098 H(70)C(44)N(4)O(34)S(1) 1230.359217 H(70)C(44)N(4)O(34)S(1) O-linked glycosylation 1662 0.5 +dHex(2)Hex(3)HexA(1)HexNAc(1)Sulf(1)@T 1237.342563 1238.084 H(71)C(44)N(1)O(37)S(1) 1237.342563 H(71)C(44)N(1)O(37)S(1) O-linked glycosylation 1663 0.5 +dHex(2)Hex(3)HexA(1)HexNAc(1)Sulf(1)@S 1237.342563 1238.084 H(71)C(44)N(1)O(37)S(1) 1237.342563 H(71)C(44)N(1)O(37)S(1) O-linked glycosylation 1663 0.5 +Hex(1)HexNAc(1)NeuAc(3)@S 1238.418446 1239.0969 H(74)C(47)N(4)O(34) 1238.418446 H(74)C(47)N(4)O(34) O-linked glycosylation 1664 0.5 +Hex(1)HexNAc(1)NeuAc(3)@T 1238.418446 1239.0969 H(74)C(47)N(4)O(34) 1238.418446 H(74)C(47)N(4)O(34) O-linked glycosylation 1664 0.5 +Hex(2)HexNAc(3)NeuGc(1)@S 1240.434096 1241.1127 H(76)C(47)N(4)O(34) 1240.434096 H(76)C(47)N(4)O(34) O-linked glycosylation 1665 0.5 +Hex(2)HexNAc(3)NeuGc(1)@T 1240.434096 1241.1127 H(76)C(47)N(4)O(34) 1240.434096 H(76)C(47)N(4)O(34) O-linked glycosylation 1665 0.5 +dHex(1)Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@T 1247.374532 1248.1252 H(73)C(45)N(3)O(35)S(1) 1247.374532 H(73)C(45)N(3)O(35)S(1) O-linked glycosylation 1666 0.5 +dHex(1)Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@S 1247.374532 1248.1252 H(73)C(45)N(3)O(35)S(1) 1247.374532 H(73)C(45)N(3)O(35)S(1) O-linked glycosylation 1666 0.5 +dHex(3)Hex(1)HexNAc(2)Kdn(1)@T 1256.454163 1257.1519 H(80)C(49)N(2)O(35) 1256.454163 H(80)C(49)N(2)O(35) O-linked glycosylation 1667 0.5 +dHex(3)Hex(1)HexNAc(2)Kdn(1)@S 1256.454163 1257.1519 H(80)C(49)N(2)O(35) 1256.454163 H(80)C(49)N(2)O(35) O-linked glycosylation 1667 0.5 +dHex(2)Hex(3)HexNAc(2)Sulf(1)@T 1264.389848 1265.1524 H(76)C(46)N(2)O(36)S(1) 1264.389848 H(76)C(46)N(2)O(36)S(1) O-linked glycosylation 1668 0.5 +dHex(2)Hex(3)HexNAc(2)Sulf(1)@S 1264.389848 1265.1524 H(76)C(46)N(2)O(36)S(1) 1264.389848 H(76)C(46)N(2)O(36)S(1) O-linked glycosylation 1668 0.5 +dHex(2)Hex(2)HexNAc(2)Kdn(1)@T 1272.449077 1273.1513 H(80)C(49)N(2)O(36) 1272.449077 H(80)C(49)N(2)O(36) O-linked glycosylation 1669 0.5 +dHex(2)Hex(2)HexNAc(2)Kdn(1)@S 1272.449077 1273.1513 H(80)C(49)N(2)O(36) 1272.449077 H(80)C(49)N(2)O(36) O-linked glycosylation 1669 0.5 +dHex(2)Hex(2)HexA(1)HexNAc(2)Sulf(1)@T 1278.369113 1279.136 H(74)C(46)N(2)O(37)S(1) 1278.369113 H(74)C(46)N(2)O(37)S(1) O-linked glycosylation 1670 0.5 +dHex(2)Hex(2)HexA(1)HexNAc(2)Sulf(1)@S 1278.369113 1279.136 H(74)C(46)N(2)O(37)S(1) 1278.369113 H(74)C(46)N(2)O(37)S(1) O-linked glycosylation 1670 0.5 +dHex(1)Hex(2)HexNAc(4)@T 1282.481046 1283.1925 H(82)C(50)N(4)O(34) 1282.481046 H(82)C(50)N(4)O(34) O-linked glycosylation 1671 0.5 +dHex(1)Hex(2)HexNAc(4)@S 1282.481046 1283.1925 H(82)C(50)N(4)O(34) 1282.481046 H(82)C(50)N(4)O(34) O-linked glycosylation 1671 0.5 +dHex(1)Hex(2)HexNAc(4)@N 1282.481046 1283.1925 H(82)C(50)N(4)O(34) 1282.481046 H(82)C(50)N(4)O(34) N-linked glycosylation 1671 0.5 +Hex(1)HexNAc(1)NeuGc(3)@S 1286.40319 1287.0951 H(74)C(47)N(4)O(37) 1286.40319 H(74)C(47)N(4)O(37) O-linked glycosylation 1672 0.5 +Hex(1)HexNAc(1)NeuGc(3)@T 1286.40319 1287.0951 H(74)C(47)N(4)O(37) 1286.40319 H(74)C(47)N(4)O(37) O-linked glycosylation 1672 0.5 +dHex(1)Hex(1)HexNAc(3)NeuAc(1)Sulf(1)@T 1288.401081 1289.1771 H(76)C(47)N(4)O(35)S(1) 1288.401081 H(76)C(47)N(4)O(35)S(1) O-linked glycosylation 1673 0.5 +dHex(1)Hex(1)HexNAc(3)NeuAc(1)Sulf(1)@S 1288.401081 1289.1771 H(76)C(47)N(4)O(35)S(1) 1288.401081 H(76)C(47)N(4)O(35)S(1) O-linked glycosylation 1673 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(2)Sulf(1)@T 1294.364027 1295.1354 H(74)C(46)N(2)O(38)S(1) 1294.364027 H(74)C(46)N(2)O(38)S(1) O-linked glycosylation 1674 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(2)Sulf(1)@S 1294.364027 1295.1354 H(74)C(46)N(2)O(38)S(1) 1294.364027 H(74)C(46)N(2)O(38)S(1) O-linked glycosylation 1674 0.5 +dHex(1)Hex(1)HexNAc(2)NeuAc(2)@S 1296.460311 1297.176 H(80)C(50)N(4)O(35) 1296.460311 H(80)C(50)N(4)O(35) O-linked glycosylation 1675 0.5 +dHex(1)Hex(1)HexNAc(2)NeuAc(2)@T 1296.460311 1297.176 H(80)C(50)N(4)O(35) 1296.460311 H(80)C(50)N(4)O(35) O-linked glycosylation 1675 0.5 +dHex(3)HexNAc(3)Kdn(1)@T 1297.480712 1298.2038 H(83)C(51)N(3)O(35) 1297.480712 H(83)C(51)N(3)O(35) O-linked glycosylation 1676 0.5 +dHex(3)HexNAc(3)Kdn(1)@S 1297.480712 1298.2038 H(83)C(51)N(3)O(35) 1297.480712 H(83)C(51)N(3)O(35) O-linked glycosylation 1676 0.5 +Hex(2)HexNAc(3)NeuAc(1)Sulf(1)@T 1304.395996 1305.1765 H(76)C(47)N(4)O(36)S(1) 1304.395996 H(76)C(47)N(4)O(36)S(1) O-linked glycosylation 1678 0.5 +Hex(2)HexNAc(3)NeuAc(1)Sulf(1)@S 1304.395996 1305.1765 H(76)C(47)N(4)O(36)S(1) 1304.395996 H(76)C(47)N(4)O(36)S(1) O-linked glycosylation 1678 0.5 +dHex(2)Hex(2)HexNAc(3)Sulf(1)@T 1305.416397 1306.2044 H(79)C(48)N(3)O(36)S(1) 1305.416397 H(79)C(48)N(3)O(36)S(1) O-linked glycosylation 1679 0.5 +dHex(2)Hex(2)HexNAc(3)Sulf(1)@S 1305.416397 1306.2044 H(79)C(48)N(3)O(36)S(1) 1305.416397 H(79)C(48)N(3)O(36)S(1) O-linked glycosylation 1679 0.5 +dHex(2)HexNAc(5)@S 1307.512681 1308.245 H(85)C(52)N(5)O(33) 1307.512681 H(85)C(52)N(5)O(33) O-linked glycosylation 1680 0.5 +dHex(2)HexNAc(5)@T 1307.512681 1308.245 H(85)C(52)N(5)O(33) 1307.512681 H(85)C(52)N(5)O(33) O-linked glycosylation 1680 0.5 +Hex(2)HexNAc(2)NeuAc(2)@S 1312.455225 1313.1754 H(80)C(50)N(4)O(36) 1312.455225 H(80)C(50)N(4)O(36) O-linked glycosylation 1681 0.5 +Hex(2)HexNAc(2)NeuAc(2)@T 1312.455225 1313.1754 H(80)C(50)N(4)O(36) 1312.455225 H(80)C(50)N(4)O(36) O-linked glycosylation 1681 0.5 +dHex(2)Hex(2)HexNAc(2)NeuAc(1)@T 1313.475627 1314.2032 H(83)C(51)N(3)O(36) 1313.475627 H(83)C(51)N(3)O(36) O-linked glycosylation 1682 0.5 +dHex(2)Hex(2)HexNAc(2)NeuAc(1)@S 1313.475627 1314.2032 H(83)C(51)N(3)O(36) 1313.475627 H(83)C(51)N(3)O(36) O-linked glycosylation 1682 0.5 +dHex(1)Hex(3)HexNAc(3)Sulf(1)@T 1321.411312 1322.2038 H(79)C(48)N(3)O(37)S(1) 1321.411312 H(79)C(48)N(3)O(37)S(1) O-linked glycosylation 1683 0.5 +dHex(1)Hex(3)HexNAc(3)Sulf(1)@S 1321.411312 1322.2038 H(79)C(48)N(3)O(37)S(1) 1321.411312 H(79)C(48)N(3)O(37)S(1) O-linked glycosylation 1683 0.5 +dHex(2)Hex(2)HexNAc(2)NeuGc(1)@T 1329.470541 1330.2026 H(83)C(51)N(3)O(37) 1329.470541 H(83)C(51)N(3)O(37) O-linked glycosylation 1684 0.5 +dHex(2)Hex(2)HexNAc(2)NeuGc(1)@S 1329.470541 1330.2026 H(83)C(51)N(3)O(37) 1329.470541 H(83)C(51)N(3)O(37) O-linked glycosylation 1684 0.5 +Hex(2)HexNAc(5)@S 1339.50251 1340.2438 H(85)C(52)N(5)O(35) 1339.50251 H(85)C(52)N(5)O(35) O-linked glycosylation 1685 0.5 +Hex(2)HexNAc(5)@T 1339.50251 1340.2438 H(85)C(52)N(5)O(35) 1339.50251 H(85)C(52)N(5)O(35) O-linked glycosylation 1685 0.5 +dHex(1)Hex(3)HexNAc(2)NeuGc(1)@S 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) O-linked glycosylation 1686 0.5 +dHex(1)Hex(3)HexNAc(2)NeuGc(1)@T 1345.465456 1346.202 H(83)C(51)N(3)O(38) 1345.465456 H(83)C(51)N(3)O(38) O-linked glycosylation 1686 0.5 +Hex(1)HexNAc(3)NeuAc(2)@S 1353.481775 1354.2273 H(83)C(52)N(5)O(36) 1353.481775 H(83)C(52)N(5)O(36) O-linked glycosylation 1687 0.5 +Hex(1)HexNAc(3)NeuAc(2)@T 1353.481775 1354.2273 H(83)C(52)N(5)O(36) 1353.481775 H(83)C(52)N(5)O(36) O-linked glycosylation 1687 0.5 +dHex(1)Hex(2)HexNAc(3)NeuAc(1)@S 1370.49709 1371.2545 H(86)C(53)N(4)O(37) 1370.49709 H(86)C(53)N(4)O(37) O-linked glycosylation 1688 0.5 +dHex(1)Hex(2)HexNAc(3)NeuAc(1)@T 1370.49709 1371.2545 H(86)C(53)N(4)O(37) 1370.49709 H(86)C(53)N(4)O(37) O-linked glycosylation 1688 0.5 +dHex(3)Hex(2)HexNAc(3)@S 1371.517491 1372.2824 H(89)C(54)N(3)O(37) 1371.517491 H(89)C(54)N(3)O(37) O-linked glycosylation 1689 0.5 +dHex(3)Hex(2)HexNAc(3)@T 1371.517491 1372.2824 H(89)C(54)N(3)O(37) 1371.517491 H(89)C(54)N(3)O(37) O-linked glycosylation 1689 0.5 +Hex(7)Phos(3)@T 1374.268757 1374.9239 H(73)C(42)O(44)P(3) 1374.268757 H(73)C(42)O(44)P(3) O-linked glycosylation 1690 0.5 +Hex(7)Phos(3)@S 1374.268757 1374.9239 H(73)C(42)O(44)P(3) 1374.268757 H(73)C(42)O(44)P(3) O-linked glycosylation 1690 0.5 +dHex(1)Hex(4)HexA(1)HexNAc(2)@S 1376.460036 1377.2128 H(84)C(52)N(2)O(40) 1376.460036 H(84)C(52)N(2)O(40) O-linked glycosylation 1691 0.5 +dHex(1)Hex(4)HexA(1)HexNAc(2)@T 1376.460036 1377.2128 H(84)C(52)N(2)O(40) 1376.460036 H(84)C(52)N(2)O(40) O-linked glycosylation 1691 0.5 +Hex(3)HexNAc(3)NeuAc(1)@T 1386.492005 1387.2539 H(86)C(53)N(4)O(38) 1386.492005 H(86)C(53)N(4)O(38) O-linked glycosylation 1692 0.5 +Hex(3)HexNAc(3)NeuAc(1)@S 1386.492005 1387.2539 H(86)C(53)N(4)O(38) 1386.492005 H(86)C(53)N(4)O(38) O-linked glycosylation 1692 0.5 +dHex(1)Hex(3)HexA(2)HexNAc(2)@S 1390.439301 1391.1963 H(82)C(52)N(2)O(41) 1390.439301 H(82)C(52)N(2)O(41) O-linked glycosylation 1693 0.5 +dHex(1)Hex(3)HexA(2)HexNAc(2)@T 1390.439301 1391.1963 H(82)C(52)N(2)O(41) 1390.439301 H(82)C(52)N(2)O(41) O-linked glycosylation 1693 0.5 +Hex(2)HexNAc(2)NeuAc(2)Sulf(1)@T 1392.41204 1393.2386 H(80)C(50)N(4)O(39)S(1) 1392.41204 H(80)C(50)N(4)O(39)S(1) O-linked glycosylation 1694 0.5 +Hex(2)HexNAc(2)NeuAc(2)Sulf(1)@S 1392.41204 1393.2386 H(80)C(50)N(4)O(39)S(1) 1392.41204 H(80)C(50)N(4)O(39)S(1) O-linked glycosylation 1694 0.5 +dHex(2)Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@T 1393.432441 1394.2664 H(83)C(51)N(3)O(39)S(1) 1393.432441 H(83)C(51)N(3)O(39)S(1) O-linked glycosylation 1695 0.5 +dHex(2)Hex(2)HexNAc(2)NeuAc(1)Sulf(1)@S 1393.432441 1394.2664 H(83)C(51)N(3)O(39)S(1) 1393.432441 H(83)C(51)N(3)O(39)S(1) O-linked glycosylation 1695 0.5 +Hex(3)HexNAc(3)NeuGc(1)@S 1402.48692 1403.2533 H(86)C(53)N(4)O(39) 1402.48692 H(86)C(53)N(4)O(39) O-linked glycosylation 1696 0.5 +Hex(3)HexNAc(3)NeuGc(1)@T 1402.48692 1403.2533 H(86)C(53)N(4)O(39) 1402.48692 H(86)C(53)N(4)O(39) O-linked glycosylation 1696 0.5 +dHex(4)Hex(1)HexNAc(2)Kdn(1)@T 1402.512072 1403.2931 H(90)C(55)N(2)O(39) 1402.512072 H(90)C(55)N(2)O(39) O-linked glycosylation 1697 0.5 +dHex(4)Hex(1)HexNAc(2)Kdn(1)@S 1402.512072 1403.2931 H(90)C(55)N(2)O(39) 1402.512072 H(90)C(55)N(2)O(39) O-linked glycosylation 1697 0.5 +dHex(3)Hex(2)HexNAc(2)Kdn(1)@T 1418.506986 1419.2925 H(90)C(55)N(2)O(40) 1418.506986 H(90)C(55)N(2)O(40) O-linked glycosylation 1698 0.5 +dHex(3)Hex(2)HexNAc(2)Kdn(1)@S 1418.506986 1419.2925 H(90)C(55)N(2)O(40) 1418.506986 H(90)C(55)N(2)O(40) O-linked glycosylation 1698 0.5 +dHex(3)Hex(2)HexA(1)HexNAc(2)Sulf(1)@T 1424.427021 1425.2772 H(84)C(52)N(2)O(41)S(1) 1424.427021 H(84)C(52)N(2)O(41)S(1) O-linked glycosylation 1699 0.5 +dHex(3)Hex(2)HexA(1)HexNAc(2)Sulf(1)@S 1424.427021 1425.2772 H(84)C(52)N(2)O(41)S(1) 1424.427021 H(84)C(52)N(2)O(41)S(1) O-linked glycosylation 1699 0.5 +Hex(2)HexNAc(4)NeuAc(1)@S 1427.518554 1428.3059 H(89)C(55)N(5)O(38) 1427.518554 H(89)C(55)N(5)O(38) O-linked glycosylation 1700 0.5 +Hex(2)HexNAc(4)NeuAc(1)@T 1427.518554 1428.3059 H(89)C(55)N(5)O(38) 1427.518554 H(89)C(55)N(5)O(38) O-linked glycosylation 1700 0.5 +dHex(2)Hex(2)HexNAc(4)@S 1428.538955 1429.3337 H(92)C(56)N(4)O(38) 1428.538955 H(92)C(56)N(4)O(38) O-linked glycosylation 1701 0.5 +dHex(2)Hex(2)HexNAc(4)@T 1428.538955 1429.3337 H(92)C(56)N(4)O(38) 1428.538955 H(92)C(56)N(4)O(38) O-linked glycosylation 1701 0.5 +dHex(2)Hex(3)HexA(1)HexNAc(2)Sulf(1)@T 1440.421936 1441.2766 H(84)C(52)N(2)O(42)S(1) 1440.421936 H(84)C(52)N(2)O(42)S(1) O-linked glycosylation 1702 0.5 +dHex(2)Hex(3)HexA(1)HexNAc(2)Sulf(1)@S 1440.421936 1441.2766 H(84)C(52)N(2)O(42)S(1) 1440.421936 H(84)C(52)N(2)O(42)S(1) O-linked glycosylation 1702 0.5 +dHex(4)HexNAc(3)Kdn(1)@T 1443.538621 1444.345 H(93)C(57)N(3)O(39) 1443.538621 H(93)C(57)N(3)O(39) O-linked glycosylation 1703 0.5 +dHex(4)HexNAc(3)Kdn(1)@S 1443.538621 1444.345 H(93)C(57)N(3)O(39) 1443.538621 H(93)C(57)N(3)O(39) O-linked glycosylation 1703 0.5 +Hex(2)HexNAc(1)NeuGc(3)@S 1448.456013 1449.2357 H(84)C(53)N(4)O(42) 1448.456013 H(84)C(53)N(4)O(42) O-linked glycosylation 1705 0.5 +Hex(2)HexNAc(1)NeuGc(3)@T 1448.456013 1449.2357 H(84)C(53)N(4)O(42) 1448.456013 H(84)C(53)N(4)O(42) O-linked glycosylation 1705 0.5 +dHex(4)Hex(1)HexNAc(1)Kdn(2)@T 1449.501567 1450.3032 H(91)C(56)N(1)O(42) 1449.501567 H(91)C(56)N(1)O(42) O-linked glycosylation 1706 0.5 +dHex(4)Hex(1)HexNAc(1)Kdn(2)@S 1449.501567 1450.3032 H(91)C(56)N(1)O(42) 1449.501567 H(91)C(56)N(1)O(42) O-linked glycosylation 1706 0.5 +dHex(1)Hex(2)HexNAc(3)NeuAc(1)Sulf(1)@T 1450.453905 1451.3177 H(86)C(53)N(4)O(40)S(1) 1450.453905 H(86)C(53)N(4)O(40)S(1) O-linked glycosylation 1707 0.5 +dHex(1)Hex(2)HexNAc(3)NeuAc(1)Sulf(1)@S 1450.453905 1451.3177 H(86)C(53)N(4)O(40)S(1) 1450.453905 H(86)C(53)N(4)O(40)S(1) O-linked glycosylation 1707 0.5 +dHex(1)Hex(2)HexNAc(2)NeuAc(2)@S 1458.513134 1459.3166 H(90)C(56)N(4)O(40) 1458.513134 H(90)C(56)N(4)O(40) O-linked glycosylation 1708 0.5 +dHex(1)Hex(2)HexNAc(2)NeuAc(2)@T 1458.513134 1459.3166 H(90)C(56)N(4)O(40) 1458.513134 H(90)C(56)N(4)O(40) O-linked glycosylation 1708 0.5 +dHex(3)Hex(1)HexNAc(3)Kdn(1)@T 1459.533535 1460.3444 H(93)C(57)N(3)O(40) 1459.533535 H(93)C(57)N(3)O(40) O-linked glycosylation 1709 0.5 +dHex(3)Hex(1)HexNAc(3)Kdn(1)@S 1459.533535 1460.3444 H(93)C(57)N(3)O(40) 1459.533535 H(93)C(57)N(3)O(40) O-linked glycosylation 1709 0.5 +Hex(3)HexNAc(3)NeuAc(1)Sulf(1)@T 1466.44882 1467.3171 H(86)C(53)N(4)O(41)S(1) 1466.44882 H(86)C(53)N(4)O(41)S(1) O-linked glycosylation 1711 0.5 +Hex(3)HexNAc(3)NeuAc(1)Sulf(1)@S 1466.44882 1467.3171 H(86)C(53)N(4)O(41)S(1) 1466.44882 H(86)C(53)N(4)O(41)S(1) O-linked glycosylation 1711 0.5 +Hex(3)HexNAc(2)NeuAc(2)@S 1474.508049 1475.316 H(90)C(56)N(4)O(41) 1474.508049 H(90)C(56)N(4)O(41) O-linked glycosylation 1712 0.5 +Hex(3)HexNAc(2)NeuAc(2)@T 1474.508049 1475.316 H(90)C(56)N(4)O(41) 1474.508049 H(90)C(56)N(4)O(41) O-linked glycosylation 1712 0.5 +Hex(3)HexNAc(3)NeuGc(1)Sulf(1)@T 1482.443734 1483.3165 H(86)C(53)N(4)O(42)S(1) 1482.443734 H(86)C(53)N(4)O(42)S(1) O-linked glycosylation 1713 0.5 +Hex(3)HexNAc(3)NeuGc(1)Sulf(1)@S 1482.443734 1483.3165 H(86)C(53)N(4)O(42)S(1) 1482.443734 H(86)C(53)N(4)O(42)S(1) O-linked glycosylation 1713 0.5 +dHex(1)Hex(2)HexNAc(2)NeuGc(2)@S 1490.502964 1491.3154 H(90)C(56)N(4)O(42) 1490.502964 H(90)C(56)N(4)O(42) O-linked glycosylation 1714 0.5 +dHex(1)Hex(2)HexNAc(2)NeuGc(2)@T 1490.502964 1491.3154 H(90)C(56)N(4)O(42) 1490.502964 H(90)C(56)N(4)O(42) O-linked glycosylation 1714 0.5 +dHex(2)Hex(3)HexNAc(2)NeuGc(1)@T 1491.523365 1492.3432 H(93)C(57)N(3)O(42) 1491.523365 H(93)C(57)N(3)O(42) O-linked glycosylation 1715 0.5 +dHex(2)Hex(3)HexNAc(2)NeuGc(1)@S 1491.523365 1492.3432 H(93)C(57)N(3)O(42) 1491.523365 H(93)C(57)N(3)O(42) O-linked glycosylation 1715 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(3)Sulf(1)@T 1497.4434 1498.3279 H(87)C(54)N(3)O(43)S(1) 1497.4434 H(87)C(54)N(3)O(43)S(1) O-linked glycosylation 1716 0.5 +dHex(1)Hex(3)HexA(1)HexNAc(3)Sulf(1)@S 1497.4434 1498.3279 H(87)C(54)N(3)O(43)S(1) 1497.4434 H(87)C(54)N(3)O(43)S(1) O-linked glycosylation 1716 0.5 +Hex(2)HexNAc(3)NeuAc(2)@S 1515.534598 1516.3679 H(93)C(58)N(5)O(41) 1515.534598 H(93)C(58)N(5)O(41) O-linked glycosylation 1717 0.5 +Hex(2)HexNAc(3)NeuAc(2)@T 1515.534598 1516.3679 H(93)C(58)N(5)O(41) 1515.534598 H(93)C(58)N(5)O(41) O-linked glycosylation 1717 0.5 +dHex(2)Hex(2)HexNAc(3)NeuAc(1)@S 1516.554999 1517.3957 H(96)C(59)N(4)O(41) 1516.554999 H(96)C(59)N(4)O(41) O-linked glycosylation 1718 0.5 +dHex(2)Hex(2)HexNAc(3)NeuAc(1)@T 1516.554999 1517.3957 H(96)C(59)N(4)O(41) 1516.554999 H(96)C(59)N(4)O(41) O-linked glycosylation 1718 0.5 +dHex(4)Hex(2)HexNAc(3)@S 1517.5754 1518.4236 H(99)C(60)N(3)O(41) 1517.5754 H(99)C(60)N(3)O(41) O-linked glycosylation 1719 0.5 +dHex(4)Hex(2)HexNAc(3)@T 1517.5754 1518.4236 H(99)C(60)N(3)O(41) 1517.5754 H(99)C(60)N(3)O(41) O-linked glycosylation 1719 0.5 +Hex(2)HexNAc(3)NeuAc(1)NeuGc(1)@S 1531.529513 1532.3673 H(93)C(58)N(5)O(42) 1531.529513 H(93)C(58)N(5)O(42) O-linked glycosylation 1720 0.5 +Hex(2)HexNAc(3)NeuAc(1)NeuGc(1)@T 1531.529513 1532.3673 H(93)C(58)N(5)O(42) 1531.529513 H(93)C(58)N(5)O(42) O-linked glycosylation 1720 0.5 +dHex(2)Hex(2)HexNAc(3)NeuGc(1)@T 1532.549914 1533.3951 H(96)C(59)N(4)O(42) 1532.549914 H(96)C(59)N(4)O(42) O-linked glycosylation 1721 0.5 +dHex(2)Hex(2)HexNAc(3)NeuGc(1)@S 1532.549914 1533.3951 H(96)C(59)N(4)O(42) 1532.549914 H(96)C(59)N(4)O(42) O-linked glycosylation 1721 0.5 +dHex(3)Hex(3)HexNAc(3)@S 1533.570315 1534.423 H(99)C(60)N(3)O(42) 1533.570315 H(99)C(60)N(3)O(42) O-linked glycosylation 1722 0.5 +dHex(3)Hex(3)HexNAc(3)@T 1533.570315 1534.423 H(99)C(60)N(3)O(42) 1533.570315 H(99)C(60)N(3)O(42) O-linked glycosylation 1722 0.5 +Hex(8)Phos(3)@T 1536.321581 1537.0645 H(83)C(48)O(49)P(3) 1536.321581 H(83)C(48)O(49)P(3) O-linked glycosylation 1723 0.5 +Hex(8)Phos(3)@S 1536.321581 1537.0645 H(83)C(48)O(49)P(3) 1536.321581 H(83)C(48)O(49)P(3) O-linked glycosylation 1723 0.5 +dHex(1)Hex(2)HexNAc(2)NeuAc(2)Sulf(1)@T 1538.469949 1539.3798 H(90)C(56)N(4)O(43)S(1) 1538.469949 H(90)C(56)N(4)O(43)S(1) O-linked glycosylation 1724 0.5 +dHex(1)Hex(2)HexNAc(2)NeuAc(2)Sulf(1)@S 1538.469949 1539.3798 H(90)C(56)N(4)O(43)S(1) 1538.469949 H(90)C(56)N(4)O(43)S(1) O-linked glycosylation 1724 0.5 +Hex(2)HexNAc(3)NeuGc(2)@S 1547.524427 1548.3667 H(93)C(58)N(5)O(43) 1547.524427 H(93)C(58)N(5)O(43) O-linked glycosylation 1725 0.5 +Hex(2)HexNAc(3)NeuGc(2)@T 1547.524427 1548.3667 H(93)C(58)N(5)O(43) 1547.524427 H(93)C(58)N(5)O(43) O-linked glycosylation 1725 0.5 +dHex(4)Hex(2)HexNAc(2)Kdn(1)@T 1564.564895 1565.4337 H(100)C(61)N(2)O(44) 1564.564895 H(100)C(61)N(2)O(44) O-linked glycosylation 1726 0.5 +dHex(4)Hex(2)HexNAc(2)Kdn(1)@S 1564.564895 1565.4337 H(100)C(61)N(2)O(44) 1564.564895 H(100)C(61)N(2)O(44) O-linked glycosylation 1726 0.5 +dHex(1)Hex(2)HexNAc(4)NeuAc(1)@S 1573.576463 1574.4471 H(99)C(61)N(5)O(42) 1573.576463 H(99)C(61)N(5)O(42) O-linked glycosylation 1727 0.5 +dHex(1)Hex(2)HexNAc(4)NeuAc(1)@T 1573.576463 1574.4471 H(99)C(61)N(5)O(42) 1573.576463 H(99)C(61)N(5)O(42) O-linked glycosylation 1727 0.5 +dHex(3)Hex(2)HexNAc(4)@S 1574.596864 1575.4749 H(102)C(62)N(4)O(42) 1574.596864 H(102)C(62)N(4)O(42) O-linked glycosylation 1728 0.5 +dHex(3)Hex(2)HexNAc(4)@T 1574.596864 1575.4749 H(102)C(62)N(4)O(42) 1574.596864 H(102)C(62)N(4)O(42) O-linked glycosylation 1728 0.5 +Hex(1)HexNAc(1)NeuGc(4)@S 1593.493521 1594.349 H(91)C(58)N(5)O(46) 1593.493521 H(91)C(58)N(5)O(46) O-linked glycosylation 1729 0.5 +Hex(1)HexNAc(1)NeuGc(4)@T 1593.493521 1594.349 H(91)C(58)N(5)O(46) 1593.493521 H(91)C(58)N(5)O(46) O-linked glycosylation 1729 0.5 +dHex(4)Hex(1)HexNAc(3)Kdn(1)@T 1605.591444 1606.4856 H(103)C(63)N(3)O(44) 1605.591444 H(103)C(63)N(3)O(44) O-linked glycosylation 1730 0.5 +dHex(4)Hex(1)HexNAc(3)Kdn(1)@S 1605.591444 1606.4856 H(103)C(63)N(3)O(44) 1605.591444 H(103)C(63)N(3)O(44) O-linked glycosylation 1730 0.5 +Hex(4)HexNAc(4)Sulf(2)@T 1620.442414 1621.4589 H(92)C(56)N(4)O(46)S(2) 1620.442414 H(92)C(56)N(4)O(46)S(2) O-linked glycosylation 1732 0.5 +Hex(4)HexNAc(4)Sulf(2)@S 1620.442414 1621.4589 H(92)C(56)N(4)O(46)S(2) 1620.442414 H(92)C(56)N(4)O(46)S(2) O-linked glycosylation 1732 0.5 +dHex(3)Hex(2)HexNAc(3)Kdn(1)@T 1621.586359 1622.485 H(103)C(63)N(3)O(45) 1621.586359 H(103)C(63)N(3)O(45) O-linked glycosylation 1733 0.5 +dHex(3)Hex(2)HexNAc(3)Kdn(1)@S 1621.586359 1622.485 H(103)C(63)N(3)O(45) 1621.586359 H(103)C(63)N(3)O(45) O-linked glycosylation 1733 0.5 +dHex(2)Hex(2)HexNAc(5)@S 1631.618328 1632.5262 H(105)C(64)N(5)O(43) 1631.618328 H(105)C(64)N(5)O(43) O-linked glycosylation 1735 0.5 +dHex(2)Hex(2)HexNAc(5)@T 1631.618328 1632.5262 H(105)C(64)N(5)O(43) 1631.618328 H(105)C(64)N(5)O(43) O-linked glycosylation 1735 0.5 +dHex(2)Hex(3)HexA(1)HexNAc(3)Sulf(1)@T 1643.501309 1644.4691 H(97)C(60)N(3)O(47)S(1) 1643.501309 H(97)C(60)N(3)O(47)S(1) O-linked glycosylation 1736 0.5 +dHex(2)Hex(3)HexA(1)HexNAc(3)Sulf(1)@S 1643.501309 1644.4691 H(97)C(60)N(3)O(47)S(1) 1643.501309 H(97)C(60)N(3)O(47)S(1) O-linked glycosylation 1736 0.5 +dHex(1)Hex(4)HexA(1)HexNAc(3)Sulf(1)@T 1659.496223 1660.4685 H(97)C(60)N(3)O(48)S(1) 1659.496223 H(97)C(60)N(3)O(48)S(1) O-linked glycosylation 1737 0.5 +dHex(1)Hex(4)HexA(1)HexNAc(3)Sulf(1)@S 1659.496223 1660.4685 H(97)C(60)N(3)O(48)S(1) 1659.496223 H(97)C(60)N(3)O(48)S(1) O-linked glycosylation 1737 0.5 +Hex(3)HexNAc(3)NeuAc(2)@S 1677.587422 1678.5085 H(103)C(64)N(5)O(46) 1677.587422 H(103)C(64)N(5)O(46) O-linked glycosylation 1738 0.5 +Hex(3)HexNAc(3)NeuAc(2)@T 1677.587422 1678.5085 H(103)C(64)N(5)O(46) 1677.587422 H(103)C(64)N(5)O(46) O-linked glycosylation 1738 0.5 +dHex(2)Hex(3)HexNAc(3)NeuAc(1)@T 1678.607823 1679.5363 H(106)C(65)N(4)O(46) 1678.607823 H(106)C(65)N(4)O(46) O-linked glycosylation 1739 0.5 +dHex(2)Hex(3)HexNAc(3)NeuAc(1)@S 1678.607823 1679.5363 H(106)C(65)N(4)O(46) 1678.607823 H(106)C(65)N(4)O(46) O-linked glycosylation 1739 0.5 +dHex(4)Hex(3)HexNAc(3)@S 1679.628224 1680.5642 H(109)C(66)N(3)O(46) 1679.628224 H(109)C(66)N(3)O(46) O-linked glycosylation 1740 0.5 +dHex(4)Hex(3)HexNAc(3)@T 1679.628224 1680.5642 H(109)C(66)N(3)O(46) 1679.628224 H(109)C(66)N(3)O(46) O-linked glycosylation 1740 0.5 +Hex(9)Phos(3)@T 1698.374404 1699.2051 H(93)C(54)O(54)P(3) 1698.374404 H(93)C(54)O(54)P(3) O-linked glycosylation 1742 0.5 +Hex(9)Phos(3)@S 1698.374404 1699.2051 H(93)C(54)O(54)P(3) 1698.374404 H(93)C(54)O(54)P(3) O-linked glycosylation 1742 0.5 +dHex(2)HexNAc(7)@S 1713.671426 1714.63 H(111)C(68)N(7)O(43) 1713.671426 H(111)C(68)N(7)O(43) O-linked glycosylation 1743 0.5 +dHex(2)HexNAc(7)@T 1713.671426 1714.63 H(111)C(68)N(7)O(43) 1713.671426 H(111)C(68)N(7)O(43) O-linked glycosylation 1743 0.5 +Hex(2)HexNAc(1)NeuGc(4)@S 1755.546345 1756.4896 H(101)C(64)N(5)O(51) 1755.546345 H(101)C(64)N(5)O(51) O-linked glycosylation 1744 0.5 +Hex(2)HexNAc(1)NeuGc(4)@T 1755.546345 1756.4896 H(101)C(64)N(5)O(51) 1755.546345 H(101)C(64)N(5)O(51) O-linked glycosylation 1744 0.5 +Hex(3)HexNAc(3)NeuAc(2)Sulf(1)@T 1757.544236 1758.5717 H(103)C(64)N(5)O(49)S(1) 1757.544236 H(103)C(64)N(5)O(49)S(1) O-linked glycosylation 1745 0.5 +Hex(3)HexNAc(3)NeuAc(2)Sulf(1)@S 1757.544236 1758.5717 H(103)C(64)N(5)O(49)S(1) 1757.544236 H(103)C(64)N(5)O(49)S(1) O-linked glycosylation 1745 0.5 +dHex(2)Hex(3)HexNAc(5)@T 1793.671151 1794.6668 H(115)C(70)N(5)O(48) 1793.671151 H(115)C(70)N(5)O(48) O-linked glycosylation 1746 0.5 +dHex(2)Hex(3)HexNAc(5)@S 1793.671151 1794.6668 H(115)C(70)N(5)O(48) 1793.671151 H(115)C(70)N(5)O(48) O-linked glycosylation 1746 0.5 +dHex(2)Hex(3)HexNAc(5)@N 1793.671151 1794.6668 H(115)C(70)N(5)O(48) 1793.671151 H(115)C(70)N(5)O(48) N-linked glycosylation 1746 0.5 +dHex(1)Hex(2)HexNAc(2)NeuGc(3)@S 1797.593295 1798.5694 H(107)C(67)N(5)O(51) 1797.593295 H(107)C(67)N(5)O(51) O-linked glycosylation 1747 0.5 +dHex(1)Hex(2)HexNAc(2)NeuGc(3)@T 1797.593295 1798.5694 H(107)C(67)N(5)O(51) 1797.593295 H(107)C(67)N(5)O(51) O-linked glycosylation 1747 0.5 +dHex(2)Hex(4)HexA(1)HexNAc(3)Sulf(1)@T 1805.554132 1806.6097 H(107)C(66)N(3)O(52)S(1) 1805.554132 H(107)C(66)N(3)O(52)S(1) O-linked glycosylation 1748 0.5 +dHex(2)Hex(4)HexA(1)HexNAc(3)Sulf(1)@S 1805.554132 1806.6097 H(107)C(66)N(3)O(52)S(1) 1805.554132 H(107)C(66)N(3)O(52)S(1) O-linked glycosylation 1748 0.5 +Hex(2)HexNAc(3)NeuAc(3)@S 1806.630015 1807.6225 H(110)C(69)N(6)O(49) 1806.630015 H(110)C(69)N(6)O(49) O-linked glycosylation 1749 0.5 +Hex(2)HexNAc(3)NeuAc(3)@T 1806.630015 1807.6225 H(110)C(69)N(6)O(49) 1806.630015 H(110)C(69)N(6)O(49) O-linked glycosylation 1749 0.5 +dHex(1)Hex(3)HexNAc(3)NeuAc(2)@S 1823.64533 1824.6497 H(113)C(70)N(5)O(50) 1823.64533 H(113)C(70)N(5)O(50) O-linked glycosylation 1750 0.5 +dHex(1)Hex(3)HexNAc(3)NeuAc(2)@T 1823.64533 1824.6497 H(113)C(70)N(5)O(50) 1823.64533 H(113)C(70)N(5)O(50) O-linked glycosylation 1750 0.5 +dHex(3)Hex(3)HexNAc(3)NeuAc(1)@S 1824.665732 1825.6775 H(116)C(71)N(4)O(50) 1824.665732 H(116)C(71)N(4)O(50) O-linked glycosylation 1751 0.5 +dHex(3)Hex(3)HexNAc(3)NeuAc(1)@T 1824.665732 1825.6775 H(116)C(71)N(4)O(50) 1824.665732 H(116)C(71)N(4)O(50) O-linked glycosylation 1751 0.5 +Hex(2)HexNAc(3)NeuGc(3)@S 1854.614759 1855.6207 H(110)C(69)N(6)O(52) 1854.614759 H(110)C(69)N(6)O(52) O-linked glycosylation 1752 0.5 +Hex(2)HexNAc(3)NeuGc(3)@T 1854.614759 1855.6207 H(110)C(69)N(6)O(52) 1854.614759 H(110)C(69)N(6)O(52) O-linked glycosylation 1752 0.5 +Hex(10)Phos(3)@T 1860.427228 1861.3457 H(103)C(60)O(59)P(3) 1860.427228 H(103)C(60)O(59)P(3) O-linked glycosylation 1753 0.5 +Hex(10)Phos(3)@S 1860.427228 1861.3457 H(103)C(60)O(59)P(3) 1860.427228 H(103)C(60)O(59)P(3) O-linked glycosylation 1753 0.5 +dHex(1)Hex(2)HexNAc(4)NeuAc(2)@S 1864.67188 1865.7016 H(116)C(72)N(6)O(50) 1864.67188 H(116)C(72)N(6)O(50) O-linked glycosylation 1754 0.5 +dHex(1)Hex(2)HexNAc(4)NeuAc(2)@T 1864.67188 1865.7016 H(116)C(72)N(6)O(50) 1864.67188 H(116)C(72)N(6)O(50) O-linked glycosylation 1754 0.5 +Hex(1)HexNAc(1)NeuGc(5)@S 1900.583852 1901.603 H(108)C(69)N(6)O(55) 1900.583852 H(108)C(69)N(6)O(55) O-linked glycosylation 1755 0.5 +Hex(1)HexNAc(1)NeuGc(5)@T 1900.583852 1901.603 H(108)C(69)N(6)O(55) 1900.583852 H(108)C(69)N(6)O(55) O-linked glycosylation 1755 0.5 +Hex(4)HexNAc(4)NeuAc(1)Sulf(2)@T 1911.53783 1912.7135 H(109)C(67)N(5)O(54)S(2) 1911.53783 H(109)C(67)N(5)O(54)S(2) O-linked glycosylation 1756 0.5 +Hex(4)HexNAc(4)NeuAc(1)Sulf(2)@S 1911.53783 1912.7135 H(109)C(67)N(5)O(54)S(2) 1911.53783 H(109)C(67)N(5)O(54)S(2) O-linked glycosylation 1756 0.5 +Hex(4)HexNAc(4)NeuGc(1)Sulf(2)@T 1927.532745 1928.7129 H(109)C(67)N(5)O(55)S(2) 1927.532745 H(109)C(67)N(5)O(55)S(2) O-linked glycosylation 1757 0.5 +Hex(4)HexNAc(4)NeuGc(1)Sulf(2)@S 1927.532745 1928.7129 H(109)C(67)N(5)O(55)S(2) 1927.532745 H(109)C(67)N(5)O(55)S(2) O-linked glycosylation 1757 0.5 +dHex(2)Hex(3)HexNAc(3)NeuAc(2)@S 1969.703239 1970.7909 H(123)C(76)N(5)O(54) 1969.703239 H(123)C(76)N(5)O(54) O-linked glycosylation 1758 0.5 +dHex(2)Hex(3)HexNAc(3)NeuAc(2)@T 1969.703239 1970.7909 H(123)C(76)N(5)O(54) 1969.703239 H(123)C(76)N(5)O(54) O-linked glycosylation 1758 0.5 +Hex(4)HexNAc(4)NeuAc(1)Sulf(3)@T 1991.494645 1992.7767 H(109)C(67)N(5)O(57)S(3) 1991.494645 H(109)C(67)N(5)O(57)S(3) O-linked glycosylation 1759 0.5 +Hex(4)HexNAc(4)NeuAc(1)Sulf(3)@S 1991.494645 1992.7767 H(109)C(67)N(5)O(57)S(3) 1991.494645 H(109)C(67)N(5)O(57)S(3) O-linked glycosylation 1759 0.5 +dHex(2)Hex(2)HexNAc(2)@S 1022.38021 1022.9486 H(66)C(40)N(2)O(28) 1022.38021 H(66)C(40)N(2)O(28) O-linked glycosylation 1760 0.5 +dHex(2)Hex(2)HexNAc(2)@T 1022.38021 1022.9486 H(66)C(40)N(2)O(28) 1022.38021 H(66)C(40)N(2)O(28) O-linked glycosylation 1760 0.5 +dHex(2)Hex(2)HexNAc(2)@N 1022.38021 1022.9486 H(66)C(40)N(2)O(28) 1022.38021 H(66)C(40)N(2)O(28) N-linked glycosylation 1760 0.5 +dHex(1)Hex(3)HexNAc(2)@S 1038.375125 1038.948 H(66)C(40)N(2)O(29) 1038.375125 H(66)C(40)N(2)O(29) O-linked glycosylation 1761 0.5 +dHex(1)Hex(3)HexNAc(2)@T 1038.375125 1038.948 H(66)C(40)N(2)O(29) 1038.375125 H(66)C(40)N(2)O(29) O-linked glycosylation 1761 0.5 +dHex(1)Hex(3)HexNAc(2)@N 1038.375125 1038.948 H(66)C(40)N(2)O(29) 1038.375125 H(66)C(40)N(2)O(29) N-linked glycosylation 1761 0.5 +dHex(1)Hex(2)HexNAc(3)@S 1079.401674 1080.0 H(69)C(42)N(3)O(29) 1079.401674 H(69)C(42)N(3)O(29) O-linked glycosylation 1762 0.5 +dHex(1)Hex(2)HexNAc(3)@T 1079.401674 1080.0 H(69)C(42)N(3)O(29) 1079.401674 H(69)C(42)N(3)O(29) O-linked glycosylation 1762 0.5 +dHex(1)Hex(2)HexNAc(3)@N 1079.401674 1080.0 H(69)C(42)N(3)O(29) 1079.401674 H(69)C(42)N(3)O(29) N-linked glycosylation 1762 0.5 +Hex(3)HexNAc(3)@S 1095.396588 1095.9994 H(69)C(42)N(3)O(30) 1095.396588 H(69)C(42)N(3)O(30) O-linked glycosylation 1763 0.5 +Hex(3)HexNAc(3)@T 1095.396588 1095.9994 H(69)C(42)N(3)O(30) 1095.396588 H(69)C(42)N(3)O(30) O-linked glycosylation 1763 0.5 +Hex(3)HexNAc(3)@N 1095.396588 1095.9994 H(69)C(42)N(3)O(30) 1095.396588 H(69)C(42)N(3)O(30) N-linked glycosylation 1763 0.5 +dHex(1)Hex(3)HexNAc(2)Sulf(1)@N 1118.331939 1119.0112 H(66)C(40)N(2)O(32)S(1) 1118.331939 H(66)C(40)N(2)O(32)S(1) N-linked glycosylation 1764 0.5 +dHex(1)Hex(3)HexNAc(2)Sulf(1)@T 1118.331939 1119.0112 H(66)C(40)N(2)O(32)S(1) 1118.331939 H(66)C(40)N(2)O(32)S(1) O-linked glycosylation 1764 0.5 +dHex(1)Hex(3)HexNAc(2)Sulf(1)@S 1118.331939 1119.0112 H(66)C(40)N(2)O(32)S(1) 1118.331939 H(66)C(40)N(2)O(32)S(1) O-linked glycosylation 1764 0.5 +dHex(2)Hex(3)HexNAc(2)@S 1184.433033 1185.0892 H(76)C(46)N(2)O(33) 1184.433033 H(76)C(46)N(2)O(33) O-linked glycosylation 1765 0.5 +dHex(2)Hex(3)HexNAc(2)@T 1184.433033 1185.0892 H(76)C(46)N(2)O(33) 1184.433033 H(76)C(46)N(2)O(33) O-linked glycosylation 1765 0.5 +dHex(2)Hex(3)HexNAc(2)@N 1184.433033 1185.0892 H(76)C(46)N(2)O(33) 1184.433033 H(76)C(46)N(2)O(33) N-linked glycosylation 1765 0.5 +dHex(1)Hex(4)HexNAc(2)@S 1200.427948 1201.0886 H(76)C(46)N(2)O(34) 1200.427948 H(76)C(46)N(2)O(34) O-linked glycosylation 1766 0.5 +dHex(1)Hex(4)HexNAc(2)@T 1200.427948 1201.0886 H(76)C(46)N(2)O(34) 1200.427948 H(76)C(46)N(2)O(34) O-linked glycosylation 1766 0.5 +dHex(1)Hex(4)HexNAc(2)@N 1200.427948 1201.0886 H(76)C(46)N(2)O(34) 1200.427948 H(76)C(46)N(2)O(34) N-linked glycosylation 1766 0.5 +dHex(2)Hex(2)HexNAc(3)@S 1225.459583 1226.1412 H(79)C(48)N(3)O(33) 1225.459583 H(79)C(48)N(3)O(33) O-linked glycosylation 1767 0.5 +dHex(2)Hex(2)HexNAc(3)@T 1225.459583 1226.1412 H(79)C(48)N(3)O(33) 1225.459583 H(79)C(48)N(3)O(33) O-linked glycosylation 1767 0.5 +dHex(2)Hex(2)HexNAc(3)@N 1225.459583 1226.1412 H(79)C(48)N(3)O(33) 1225.459583 H(79)C(48)N(3)O(33) N-linked glycosylation 1767 0.5 +dHex(1)Hex(3)HexNAc(3)@S 1241.454497 1242.1406 H(79)C(48)N(3)O(34) 1241.454497 H(79)C(48)N(3)O(34) O-linked glycosylation 1768 0.5 +dHex(1)Hex(3)HexNAc(3)@T 1241.454497 1242.1406 H(79)C(48)N(3)O(34) 1241.454497 H(79)C(48)N(3)O(34) O-linked glycosylation 1768 0.5 +dHex(1)Hex(3)HexNAc(3)@N 1241.454497 1242.1406 H(79)C(48)N(3)O(34) 1241.454497 H(79)C(48)N(3)O(34) N-linked glycosylation 1768 0.5 +Hex(4)HexNAc(3)@S 1257.449412 1258.14 H(79)C(48)N(3)O(35) 1257.449412 H(79)C(48)N(3)O(35) O-linked glycosylation 1769 0.5 +Hex(4)HexNAc(3)@T 1257.449412 1258.14 H(79)C(48)N(3)O(35) 1257.449412 H(79)C(48)N(3)O(35) O-linked glycosylation 1769 0.5 +Hex(4)HexNAc(3)@N 1257.449412 1258.14 H(79)C(48)N(3)O(35) 1257.449412 H(79)C(48)N(3)O(35) N-linked glycosylation 1769 0.5 +dHex(2)Hex(4)HexNAc(2)@S 1346.485857 1347.2298 H(86)C(52)N(2)O(38) 1346.485857 H(86)C(52)N(2)O(38) O-linked glycosylation 1770 0.5 +dHex(2)Hex(4)HexNAc(2)@T 1346.485857 1347.2298 H(86)C(52)N(2)O(38) 1346.485857 H(86)C(52)N(2)O(38) O-linked glycosylation 1770 0.5 +dHex(2)Hex(4)HexNAc(2)@N 1346.485857 1347.2298 H(86)C(52)N(2)O(38) 1346.485857 H(86)C(52)N(2)O(38) N-linked glycosylation 1770 0.5 +dHex(2)Hex(3)HexNAc(3)@S 1387.512406 1388.2818 H(89)C(54)N(3)O(38) 1387.512406 H(89)C(54)N(3)O(38) O-linked glycosylation 1771 0.5 +dHex(2)Hex(3)HexNAc(3)@T 1387.512406 1388.2818 H(89)C(54)N(3)O(38) 1387.512406 H(89)C(54)N(3)O(38) O-linked glycosylation 1771 0.5 +dHex(2)Hex(3)HexNAc(3)@N 1387.512406 1388.2818 H(89)C(54)N(3)O(38) 1387.512406 H(89)C(54)N(3)O(38) N-linked glycosylation 1771 0.5 +Hex(3)HexNAc(5)@N 1501.555334 1502.3844 H(95)C(58)N(5)O(40) 1501.555334 H(95)C(58)N(5)O(40) N-linked glycosylation 1772 0.5 +Hex(3)HexNAc(5)@T 1501.555334 1502.3844 H(95)C(58)N(5)O(40) 1501.555334 H(95)C(58)N(5)O(40) O-linked glycosylation 1772 0.5 +Hex(3)HexNAc(5)@S 1501.555334 1502.3844 H(95)C(58)N(5)O(40) 1501.555334 H(95)C(58)N(5)O(40) O-linked glycosylation 1772 0.5 +Hex(4)HexNAc(3)NeuAc(1)@N 1548.544828 1549.3945 H(96)C(59)N(4)O(43) 1548.544828 H(96)C(59)N(4)O(43) N-linked glycosylation 1773 0.5 +Hex(4)HexNAc(3)NeuAc(1)@T 1548.544828 1549.3945 H(96)C(59)N(4)O(43) 1548.544828 H(96)C(59)N(4)O(43) O-linked glycosylation 1773 0.5 +Hex(4)HexNAc(3)NeuAc(1)@S 1548.544828 1549.3945 H(96)C(59)N(4)O(43) 1548.544828 H(96)C(59)N(4)O(43) O-linked glycosylation 1773 0.5 +dHex(2)Hex(3)HexNAc(4)@S 1590.591779 1591.4743 H(102)C(62)N(4)O(43) 1590.591779 H(102)C(62)N(4)O(43) O-linked glycosylation 1774 0.5 +dHex(2)Hex(3)HexNAc(4)@T 1590.591779 1591.4743 H(102)C(62)N(4)O(43) 1590.591779 H(102)C(62)N(4)O(43) O-linked glycosylation 1774 0.5 +dHex(2)Hex(3)HexNAc(4)@N 1590.591779 1591.4743 H(102)C(62)N(4)O(43) 1590.591779 H(102)C(62)N(4)O(43) N-linked glycosylation 1774 0.5 +dHex(1)Hex(3)HexNAc(5)@S 1647.613242 1648.5256 H(105)C(64)N(5)O(44) 1647.613242 H(105)C(64)N(5)O(44) O-linked glycosylation 1775 0.5 +dHex(1)Hex(3)HexNAc(5)@T 1647.613242 1648.5256 H(105)C(64)N(5)O(44) 1647.613242 H(105)C(64)N(5)O(44) O-linked glycosylation 1775 0.5 +dHex(1)Hex(3)HexNAc(5)@N 1647.613242 1648.5256 H(105)C(64)N(5)O(44) 1647.613242 H(105)C(64)N(5)O(44) N-linked glycosylation 1775 0.5 +Hex(3)HexNAc(6)@N 1704.634706 1705.5769 H(108)C(66)N(6)O(45) 1704.634706 H(108)C(66)N(6)O(45) N-linked glycosylation 1776 0.5 +Hex(3)HexNAc(6)@T 1704.634706 1705.5769 H(108)C(66)N(6)O(45) 1704.634706 H(108)C(66)N(6)O(45) O-linked glycosylation 1776 0.5 +Hex(3)HexNAc(6)@S 1704.634706 1705.5769 H(108)C(66)N(6)O(45) 1704.634706 H(108)C(66)N(6)O(45) O-linked glycosylation 1776 0.5 +Hex(4)HexNAc(4)NeuAc(1)@S 1751.624201 1752.5871 H(109)C(67)N(5)O(48) 1751.624201 H(109)C(67)N(5)O(48) O-linked glycosylation 1777 0.5 +Hex(4)HexNAc(4)NeuAc(1)@T 1751.624201 1752.5871 H(109)C(67)N(5)O(48) 1751.624201 H(109)C(67)N(5)O(48) O-linked glycosylation 1777 0.5 +Hex(4)HexNAc(4)NeuAc(1)@N 1751.624201 1752.5871 H(109)C(67)N(5)O(48) 1751.624201 H(109)C(67)N(5)O(48) N-linked glycosylation 1777 0.5 +dHex(2)Hex(4)HexNAc(4)@N 1752.644602 1753.6149 H(112)C(68)N(4)O(48) 1752.644602 H(112)C(68)N(4)O(48) N-linked glycosylation 1778 0.5 +dHex(2)Hex(4)HexNAc(4)@T 1752.644602 1753.6149 H(112)C(68)N(4)O(48) 1752.644602 H(112)C(68)N(4)O(48) O-linked glycosylation 1778 0.5 +dHex(2)Hex(4)HexNAc(4)@S 1752.644602 1753.6149 H(112)C(68)N(4)O(48) 1752.644602 H(112)C(68)N(4)O(48) O-linked glycosylation 1778 0.5 +Hex(6)HexNAc(4)@S 1784.634431 1785.6137 H(112)C(68)N(4)O(50) 1784.634431 H(112)C(68)N(4)O(50) O-linked glycosylation 1779 0.5 +Hex(6)HexNAc(4)@T 1784.634431 1785.6137 H(112)C(68)N(4)O(50) 1784.634431 H(112)C(68)N(4)O(50) O-linked glycosylation 1779 0.5 +Hex(6)HexNAc(4)@N 1784.634431 1785.6137 H(112)C(68)N(4)O(50) 1784.634431 H(112)C(68)N(4)O(50) N-linked glycosylation 1779 0.5 +Hex(5)HexNAc(5)@S 1825.660981 1826.6656 H(115)C(70)N(5)O(50) 1825.660981 H(115)C(70)N(5)O(50) O-linked glycosylation 1780 0.5 +Hex(5)HexNAc(5)@T 1825.660981 1826.6656 H(115)C(70)N(5)O(50) 1825.660981 H(115)C(70)N(5)O(50) O-linked glycosylation 1780 0.5 +Hex(5)HexNAc(5)@N 1825.660981 1826.6656 H(115)C(70)N(5)O(50) 1825.660981 H(115)C(70)N(5)O(50) N-linked glycosylation 1780 0.5 +dHex(1)Hex(3)HexNAc(6)@S 1850.692615 1851.7181 H(118)C(72)N(6)O(49) 1850.692615 H(118)C(72)N(6)O(49) O-linked glycosylation 1781 0.5 +dHex(1)Hex(3)HexNAc(6)@T 1850.692615 1851.7181 H(118)C(72)N(6)O(49) 1850.692615 H(118)C(72)N(6)O(49) O-linked glycosylation 1781 0.5 +dHex(1)Hex(3)HexNAc(6)@N 1850.692615 1851.7181 H(118)C(72)N(6)O(49) 1850.692615 H(118)C(72)N(6)O(49) N-linked glycosylation 1781 0.5 +dHex(1)Hex(4)HexNAc(4)NeuAc(1)@N 1897.68211 1898.7283 H(119)C(73)N(5)O(52) 1897.68211 H(119)C(73)N(5)O(52) N-linked glycosylation 1782 0.5 +dHex(1)Hex(4)HexNAc(4)NeuAc(1)@T 1897.68211 1898.7283 H(119)C(73)N(5)O(52) 1897.68211 H(119)C(73)N(5)O(52) O-linked glycosylation 1782 0.5 +dHex(1)Hex(4)HexNAc(4)NeuAc(1)@S 1897.68211 1898.7283 H(119)C(73)N(5)O(52) 1897.68211 H(119)C(73)N(5)O(52) O-linked glycosylation 1782 0.5 +dHex(3)Hex(4)HexNAc(4)@S 1898.702511 1899.7561 H(122)C(74)N(4)O(52) 1898.702511 H(122)C(74)N(4)O(52) O-linked glycosylation 1783 0.5 +dHex(3)Hex(4)HexNAc(4)@T 1898.702511 1899.7561 H(122)C(74)N(4)O(52) 1898.702511 H(122)C(74)N(4)O(52) O-linked glycosylation 1783 0.5 +dHex(3)Hex(4)HexNAc(4)@N 1898.702511 1899.7561 H(122)C(74)N(4)O(52) 1898.702511 H(122)C(74)N(4)O(52) N-linked glycosylation 1783 0.5 +dHex(1)Hex(3)HexNAc(5)NeuAc(1)@S 1938.708659 1939.7802 H(122)C(75)N(6)O(52) 1938.708659 H(122)C(75)N(6)O(52) O-linked glycosylation 1784 0.5 +dHex(1)Hex(3)HexNAc(5)NeuAc(1)@T 1938.708659 1939.7802 H(122)C(75)N(6)O(52) 1938.708659 H(122)C(75)N(6)O(52) O-linked glycosylation 1784 0.5 +dHex(1)Hex(3)HexNAc(5)NeuAc(1)@N 1938.708659 1939.7802 H(122)C(75)N(6)O(52) 1938.708659 H(122)C(75)N(6)O(52) N-linked glycosylation 1784 0.5 +dHex(2)Hex(4)HexNAc(5)@S 1955.723975 1956.8074 H(125)C(76)N(5)O(53) 1955.723975 H(125)C(76)N(5)O(53) O-linked glycosylation 1785 0.5 +dHex(2)Hex(4)HexNAc(5)@T 1955.723975 1956.8074 H(125)C(76)N(5)O(53) 1955.723975 H(125)C(76)N(5)O(53) O-linked glycosylation 1785 0.5 +dHex(2)Hex(4)HexNAc(5)@N 1955.723975 1956.8074 H(125)C(76)N(5)O(53) 1955.723975 H(125)C(76)N(5)O(53) N-linked glycosylation 1785 0.5 +NQIGG@K 469.228496 469.4921 H(31)C(19)N(7)O(7) 0.0 Post-translational 1799 0.0 +Carboxyethylpyrrole@K 122.036779 122.1213 H(6)C(7)O(2) 0.0 Other 1800 0.0 +Fluorescein-tyramine@Y 493.116152 493.4637 H(19)C(29)N(1)O(7) 0.0 Chemical derivative 1801 0.0 +dHex(1)Hex(7)HexNAc(4)@N 2092.745164 2093.8955 H(132)C(80)N(4)O(59) 0.0 N-linked glycosylation 1840 0.0 +betaFNA@C 454.210387 454.5155 H(30)C(25)N(2)O(6) 0.0 Chemical derivative 1839 0.0 +betaFNA@K 454.210387 454.5155 H(30)C(25)N(2)O(6) 0.0 Chemical derivative 1839 0.0 +Brij58@Any_N-term 224.250401 224.4253 H(32)C(16) 0.0 Other 1838 0.0 +Brij35@Any_N-term 168.187801 168.319 H(24)C(12) 0.0 Other 1837 0.0 +Triton@Any_N-term 188.156501 188.3086 H(20)C(14) 0.0 Other 1836 0.0 +Triton@Any_C-term 188.156501 188.3086 H(20)C(14) 0.0 Other 1836 0.0 +Tween80@Any_C-term 263.237491 263.4381 H(31)C(18)O(1) 0.0 Other 1835 0.0 +Tween20@Any_N-term 165.164326 165.2951 H(21)C(12) 0.0 Other 1834 0.0 +Tris@N 104.071154 104.1277 H(10)C(4)N(1)O(2) 0.0 Artefact 1831 0.0 +Biotin-tyramide@Y 361.146012 361.4585 H(23)C(18)N(3)O(3)S(1) 0.0 Chemical derivative 1830 0.0 +Biotin-tyramide@W 361.146012 361.4585 H(23)C(18)N(3)O(3)S(1) 0.0 Chemical derivative 1830 0.0 +Biotin-tyramide@C 361.146012 361.4585 H(23)C(18)N(3)O(3)S(1) 0.0 Chemical derivative 1830 0.0 +LRGG+dimethyl@K 411.259403 411.4991 H(33)C(18)N(7)O(4) 0.0 Post-translational 1829 0.0 +RNPXL@R^Any_N-term 324.035867 324.1813 H(13)C(9)N(2)O(9)P(1) 324.035867 H(13)C(9)N(2)O(9)P(1) Other 1825 0.5 +RNPXL@K^Any_N-term 324.035867 324.1813 H(13)C(9)N(2)O(9)P(1) 324.035867 H(13)C(9)N(2)O(9)P(1) Other 1825 0.5 +GEE@Q 86.036779 86.0892 H(6)C(4)O(2) 0.0 Chemical derivative 1824 0.0 +Glu->pyro-Glu+Methyl@E^Any_N-term -3.994915 -3.9887 C(1)O(-1) 0.0 Artefact 1826 0.0 +Glu->pyro-Glu+Methyl:2H(2)13C(1)@E^Any_N-term -0.979006 -0.9837 H(-2)2H(2)13C(1)O(-1) 0.0 Artefact 1827 0.0 +LRGG+methyl@K 397.243753 397.4725 H(31)C(17)N(7)O(4) 0.0 Post-translational 1828 0.0 +NP40@Any_N-term 220.182715 220.3505 H(24)C(15)O(1) 0.0 Other 1833 0.0 +IASD@C 452.034807 452.4582 H(16)C(18)N(2)O(8)S(2) 0.0 Chemical derivative 1832 0.0 +Biotin:Thermo-21328@K 389.090154 389.5564 H(23)C(15)N(3)O(3)S(3) 0.0 Chemical derivative 1841 0.0 +Biotin:Thermo-21328@Any_N-term 389.090154 389.5564 H(23)C(15)N(3)O(3)S(3) 0.0 Chemical derivative 1841 0.0 +PhosphoCytidine@Y 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 +PhosphoCytidine@T 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 +PhosphoCytidine@S 305.041287 305.1812 H(12)C(9)N(3)O(7)P(1) 0.0 Post-translational 1843 0.0 +AzidoF@F 41.001397 41.0122 H(-1)N(3) 0.0 Chemical derivative 1845 0.0 +Dimethylaminoethyl@C 71.073499 71.121 H(9)C(4)N(1) 0.0 Chemical derivative 1846 0.0 +Gluratylation@K 114.031694 114.0993 H(6)C(5)O(3) 0.0 Post-translational 1848 0.0 +hydroxyisobutyryl@K 86.036779 86.0892 H(6)C(4)O(2) 0.0 Post-translational 1849 0.0 +MeMePhosphorothioate@S 107.979873 108.0993 H(5)C(2)O(1)P(1)S(1) 0.0 Chemical derivative 1868 0.0 +Cation:Fe[III]@D 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 +Cation:Fe[III]@E 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 +Cation:Fe[III]@Any_C-term 52.911464 52.8212 H(-3)Fe(1) 0.0 Artefact 1870 0.0 +DTT@C 151.996571 152.2351 H(8)C(4)O(2)S(2) 0.0 Artefact 1871 0.0 +DYn-2@C 161.09664 161.2203 H(13)C(11)O(1) 0.0 Other 1872 0.0 +Xlink:DSSO[176]@K 176.01433 176.1903 H(8)C(6)O(4)S(1) 0.0 Chemical derivative 1878 0.0 +Xlink:DSSO[176]@Protein_N-term 176.01433 176.1903 H(8)C(6)O(4)S(1) 0.0 Chemical derivative 1878 0.0 +MesitylOxide@K 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 +MesitylOxide@H 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 +MesitylOxide@Protein_N-term 98.073165 98.143 H(10)C(6)O(1) 0.0 Chemical derivative 1873 0.0 +Xlink:DSS[259]@K 259.141973 259.2988 H(21)C(12)N(1)O(5) 0.0 Chemical derivative 1877 0.0 +Xlink:DSS[259]@Protein_N-term 259.141973 259.2988 H(21)C(12)N(1)O(5) 0.0 Chemical derivative 1877 0.0 +methylol@Y 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 +methylol@W 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 +methylol@K 30.010565 30.026 H(2)C(1)O(1) 0.0 Chemical derivative 1875 0.0 +Xlink:DSSO[175]@K 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1879 0.0 +Xlink:DSSO[175]@Protein_N-term 175.030314 175.2056 H(9)C(6)N(1)O(3)S(1) 0.0 Chemical derivative 1879 0.0 +Xlink:DSSO[279]@K 279.077658 279.3101 H(17)C(10)N(1)O(6)S(1) 0.0 Chemical derivative 1880 0.0 +Xlink:DSSO[279]@Protein_N-term 279.077658 279.3101 H(17)C(10)N(1)O(6)S(1) 0.0 Chemical derivative 1880 0.0 +Xlink:DSSO[54]@Protein_N-term 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 1881 0.0 +Xlink:DSSO[54]@K 54.010565 54.0474 H(2)C(3)O(1) 0.0 Chemical derivative 1881 0.0 +Xlink:DSSO[86]@K 85.982635 86.1124 H(2)C(3)O(1)S(1) 0.0 Chemical derivative 1882 0.0 +Xlink:DSSO[86]@Protein_N-term 85.982635 86.1124 H(2)C(3)O(1)S(1) 0.0 Chemical derivative 1882 0.0 +Xlink:DSSO[104]@K 103.9932 104.1277 H(4)C(3)O(2)S(1) 0.0 Chemical derivative 1883 0.0 +Xlink:DSSO[104]@Protein_N-term 103.9932 104.1277 H(4)C(3)O(2)S(1) 0.0 Chemical derivative 1883 0.0 +Xlink:BuUrBu[111]@S 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 +Xlink:BuUrBu[111]@Protein_N-term 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 +Xlink:BuUrBu[111]@K 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 +Xlink:BuUrBu[111]@T 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 +Xlink:BuUrBu[111]@Y 111.032028 111.0987 H(5)C(5)N(1)O(2) 0.0 Chemical derivative 1885 0.0 +Xlink:BuUrBu[85]@S 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 +Xlink:BuUrBu[85]@Protein_N-term 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 +Xlink:BuUrBu[85]@K 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 +Xlink:BuUrBu[85]@T 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 +Xlink:BuUrBu[85]@Y 85.052764 85.1045 H(7)C(4)N(1)O(1) 0.0 Chemical derivative 1886 0.0 +Xlink:BuUrBu[214]@S 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 +Xlink:BuUrBu[214]@Protein_N-term 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 +Xlink:BuUrBu[214]@K 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 +Xlink:BuUrBu[214]@T 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 +Xlink:BuUrBu[214]@Y 214.095357 214.2185 H(14)C(9)N(2)O(4) 0.0 Chemical derivative 1888 0.0 +Xlink:BuUrBu[317]@S 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 +Xlink:BuUrBu[317]@Protein_N-term 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 +Xlink:BuUrBu[317]@K 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 +Xlink:BuUrBu[317]@T 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 +Xlink:BuUrBu[317]@Y 317.158686 317.3382 H(23)C(13)N(3)O(6) 0.0 Chemical derivative 1889 0.0 +Xlink:DSSO[158]@K 158.003765 158.175 H(6)C(6)O(3)S(1) 0.0 Chemical derivative 1896 0.0 +Xlink:DSSO[158]@Protein_N-term 158.003765 158.175 H(6)C(6)O(3)S(1) 0.0 Chemical derivative 1896 0.0 +Xlink:DSS[138]@K 138.06808 138.1638 H(10)C(8)O(2) 0.0 Chemical derivative 1898 0.0 +Xlink:DSS[138]@Protein_N-term 138.06808 138.1638 H(10)C(8)O(2) 0.0 Chemical derivative 1898 0.0 +Xlink:BuUrBu[196]@S 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 +Xlink:BuUrBu[196]@Protein_N-term 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 +Xlink:BuUrBu[196]@K 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 +Xlink:BuUrBu[196]@T 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 +Xlink:BuUrBu[196]@Y 196.084792 196.2032 H(12)C(9)N(2)O(3) 0.0 Chemical derivative 1899 0.0 +Xlink:DTBP[172]@K 172.01289 172.2711 H(8)C(6)N(2)S(2) 0.0 Chemical derivative 1900 0.0 +Xlink:DTBP[172]@Protein_N-term 172.01289 172.2711 H(8)C(6)N(2)S(2) 0.0 Chemical derivative 1900 0.0 +Xlink:DST[114]@K 113.995309 114.0563 H(2)C(4)O(4) 0.0 Chemical derivative 1901 0.0 +Xlink:DST[114]@Protein_N-term 113.995309 114.0563 H(2)C(4)O(4) 0.0 Chemical derivative 1901 0.0 +Xlink:DTSSP[174]@K 173.980921 174.2406 H(6)C(6)O(2)S(2) 0.0 Chemical derivative 1902 0.0 +Xlink:DTSSP[174]@Protein_N-term 173.980921 174.2406 H(6)C(6)O(2)S(2) 0.0 Chemical derivative 1902 0.0 +Xlink:SMCC[219]@C 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 +Xlink:SMCC[219]@K 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 +Xlink:SMCC[219]@Protein_N-term 219.089543 219.2365 H(13)C(12)N(1)O(3) 0.0 Chemical derivative 1903 0.0 +Cation:Al[III]@D 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 +Cation:Al[III]@E 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 +Cation:Al[III]@Any_C-term 23.958063 23.9577 H(-3)Al(1) 0.0 Artefact 1910 0.0 +Xlink:BS2G[113]@Protein_N-term 113.047679 113.1146 H(7)C(5)N(1)O(2) 0.0 Chemical derivative 1906 0.0 +Xlink:BS2G[113]@K 113.047679 113.1146 H(7)C(5)N(1)O(2) 0.0 Chemical derivative 1906 0.0 +Xlink:BS2G[114]@Protein_N-term 114.031694 114.0993 H(6)C(5)O(3) 0.0 Chemical derivative 1907 0.0 +Xlink:BS2G[114]@K 114.031694 114.0993 H(6)C(5)O(3) 0.0 Chemical derivative 1907 0.0 +Xlink:BS2G[217]@Protein_N-term 217.095023 217.2191 H(15)C(9)N(1)O(5) 0.0 Chemical derivative 1908 0.0 +Xlink:BS2G[217]@K 217.095023 217.2191 H(15)C(9)N(1)O(5) 0.0 Chemical derivative 1908 0.0 +Xlink:DMP[139]@K 139.110947 139.1982 H(13)C(7)N(3) 0.0 Chemical derivative 1911 0.0 +Xlink:DMP[139]@Protein_N-term 139.110947 139.1982 H(13)C(7)N(3) 0.0 Chemical derivative 1911 0.0 +Xlink:DMP[122]@K 122.084398 122.1677 H(10)C(7)N(2) 0.0 Chemical derivative 1912 0.0 +Xlink:DMP[122]@Protein_N-term 122.084398 122.1677 H(10)C(7)N(2) 0.0 Chemical derivative 1912 0.0 +glyoxalAGE@R 21.98435 22.0055 H(-2)C(2) 0.0 Post-translational 1913 0.0 +Met->AspSA@M -32.008456 -32.1081 H(-4)C(-1)O(1)S(-1) 0.0 Chemical derivative 1914 0.0 +Decarboxylation@D -30.010565 -30.026 H(-2)C(-1)O(-1) 0.0 Chemical derivative 1915 0.0 +Decarboxylation@E -30.010565 -30.026 H(-2)C(-1)O(-1) 0.0 Chemical derivative 1915 0.0 +Aspartylurea@H -10.031969 -10.0412 H(-2)C(-1)N(-2)O(2) 0.0 Chemical derivative 1916 0.0 +Formylasparagine@H 4.97893 4.9735 H(-1)C(-1)N(-1)O(2) 0.0 Chemical derivative 1917 0.0 +Carbonyl@S 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 +Carbonyl@R 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 +Carbonyl@Q 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 +Carbonyl@L 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 +Carbonyl@I 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 +Carbonyl@E 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 +Carbonyl@A 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 +Carbonyl@V 13.979265 13.9835 H(-2)O(1) 0.0 Chemical derivative 1918 0.0 +Pro->HAVA@P 18.010565 18.0153 H(2)O(1) 0.0 Chemical derivative 1922 0.0 +AFB1_Dialdehyde@K 310.047738 310.2577 H(10)C(17)O(6) 0.0 Post-translational 1920 0.0 +Delta:H(-4)O(2)@W 27.958529 27.967 H(-4)O(2) 0.0 Chemical derivative 1923 0.0 +Delta:H(-4)O(3)@W 43.953444 43.9664 H(-4)O(3) 0.0 Chemical derivative 1924 0.0 +Delta:O(4)@W 63.979659 63.9976 O(4) 0.0 Artefact 1925 0.0 +Delta:H(4)C(3)O(2)@K 72.021129 72.0627 H(4)C(3)O(2) 0.0 Artefact 1926 0.0 +Delta:H(4)C(5)O(1)@R 80.026215 80.0847 H(4)C(5)O(1) 0.0 Chemical derivative 1927 0.0 +Delta:H(10)C(8)O(1)@K 122.073165 122.1644 H(10)C(8)O(1) 0.0 Artefact 1928 0.0 +Delta:H(6)C(7)O(4)@R 154.026609 154.1201 H(6)C(7)O(4) 0.0 Chemical derivative 1929 0.0 +Hex(2)Sulf(1)@T 404.062462 404.3444 H(20)C(12)O(13)S(1) 404.062462 H(20)C(12)O(13)S(1) O-linked glycosylation 1932 0.5 +Hex(2)Sulf(1)@S 404.062462 404.3444 H(20)C(12)O(13)S(1) 404.062462 H(20)C(12)O(13)S(1) O-linked glycosylation 1932 0.5 +Pent(2)@T 264.084518 264.2292 H(16)C(10)O(8) 264.084518 H(16)C(10)O(8) O-linked glycosylation 1930 0.5 +Pent(2)@S 264.084518 264.2292 H(16)C(10)O(8) 264.084518 H(16)C(10)O(8) O-linked glycosylation 1930 0.5 +Pent(1)HexNAc(1)@T 335.121631 335.3071 H(21)C(13)N(1)O(9) 335.121631 H(21)C(13)N(1)O(9) O-linked glycosylation 1931 0.5 +Pent(1)HexNAc(1)@S 335.121631 335.3071 H(21)C(13)N(1)O(9) 335.121631 H(21)C(13)N(1)O(9) O-linked glycosylation 1931 0.5 +Hex(1)Pent(2)Me(1)@T 440.152991 440.3964 H(28)C(17)O(13) 440.152991 H(28)C(17)O(13) O-linked glycosylation 1933 0.5 +Hex(1)Pent(2)Me(1)@S 440.152991 440.3964 H(28)C(17)O(13) 440.152991 H(28)C(17)O(13) O-linked glycosylation 1933 0.5 +HexNAc(2)Sulf(1)@S 486.11556 486.4482 H(26)C(16)N(2)O(13)S(1) 486.11556 H(26)C(16)N(2)O(13)S(1) O-linked glycosylation 1934 0.5 +HexNAc(2)Sulf(1)@T 486.11556 486.4482 H(26)C(16)N(2)O(13)S(1) 486.11556 H(26)C(16)N(2)O(13)S(1) O-linked glycosylation 1934 0.5 +Hex(1)Pent(3)Me(1)@S 572.19525 572.511 H(36)C(22)O(17) 572.19525 H(36)C(22)O(17) O-linked glycosylation 1935 0.5 +Hex(1)Pent(3)Me(1)@T 572.19525 572.511 H(36)C(22)O(17) 572.19525 H(36)C(22)O(17) O-linked glycosylation 1935 0.5 +Hex(2)Pent(2)@S 588.190165 588.5104 H(36)C(22)O(18) 588.190165 H(36)C(22)O(18) O-linked glycosylation 1936 0.5 +Hex(2)Pent(2)@T 588.190165 588.5104 H(36)C(22)O(18) 588.190165 H(36)C(22)O(18) O-linked glycosylation 1936 0.5 +Hex(2)Pent(2)Me(1)@S 602.205815 602.537 H(38)C(23)O(18) 602.205815 H(38)C(23)O(18) O-linked glycosylation 1937 0.5 +Hex(2)Pent(2)Me(1)@T 602.205815 602.537 H(38)C(23)O(18) 602.205815 H(38)C(23)O(18) O-linked glycosylation 1937 0.5 +Hex(4)HexA(1)@S 824.243382 824.6865 H(48)C(30)O(26) 824.243382 H(48)C(30)O(26) O-linked glycosylation 1938 0.5 +Hex(4)HexA(1)@T 824.243382 824.6865 H(48)C(30)O(26) 824.243382 H(48)C(30)O(26) O-linked glycosylation 1938 0.5 +Hex(2)HexNAc(1)Pent(1)HexA(1)@S 835.259366 835.7125 H(49)C(31)N(1)O(25) 835.259366 H(49)C(31)N(1)O(25) O-linked glycosylation 1939 0.5 +Hex(2)HexNAc(1)Pent(1)HexA(1)@T 835.259366 835.7125 H(49)C(31)N(1)O(25) 835.259366 H(49)C(31)N(1)O(25) O-linked glycosylation 1939 0.5 +Hex(3)HexNAc(1)HexA(1)@S 865.269931 865.7384 H(51)C(32)N(1)O(26) 865.269931 H(51)C(32)N(1)O(26) O-linked glycosylation 1940 0.5 +Hex(3)HexNAc(1)HexA(1)@T 865.269931 865.7384 H(51)C(32)N(1)O(26) 865.269931 H(51)C(32)N(1)O(26) O-linked glycosylation 1940 0.5 +Hex(1)HexNAc(2)dHex(2)Sulf(1)@S 940.284201 940.8712 H(56)C(34)N(2)O(26)S(1) 940.284201 H(56)C(34)N(2)O(26)S(1) O-linked glycosylation 1941 0.5 +Hex(1)HexNAc(2)dHex(2)Sulf(1)@T 940.284201 940.8712 H(56)C(34)N(2)O(26)S(1) 940.284201 H(56)C(34)N(2)O(26)S(1) O-linked glycosylation 1941 0.5 +HexA(2)HexNAc(3)@S 961.302294 961.8258 H(55)C(36)N(3)O(27) 961.302294 H(55)C(36)N(3)O(27) O-linked glycosylation 1942 0.5 +HexA(2)HexNAc(3)@T 961.302294 961.8258 H(55)C(36)N(3)O(27) 961.302294 H(55)C(36)N(3)O(27) O-linked glycosylation 1942 0.5 +dHex(1)Hex(4)HexA(1)@T 970.301291 970.8277 H(58)C(36)O(30) 970.301291 H(58)C(36)O(30) O-linked glycosylation 1943 0.5 +dHex(1)Hex(4)HexA(1)@S 970.301291 970.8277 H(58)C(36)O(30) 970.301291 H(58)C(36)O(30) O-linked glycosylation 1943 0.5 +Hex(5)HexA(1)@S 986.296206 986.8271 H(58)C(36)O(31) 986.296206 H(58)C(36)O(31) O-linked glycosylation 1944 0.5 +Hex(5)HexA(1)@T 986.296206 986.8271 H(58)C(36)O(31) 986.296206 H(58)C(36)O(31) O-linked glycosylation 1944 0.5 +Hex(4)HexA(1)HexNAc(1)@T 1027.322755 1027.879 H(61)C(38)N(1)O(31) 1027.322755 H(61)C(38)N(1)O(31) O-linked glycosylation 1945 0.5 +Hex(4)HexA(1)HexNAc(1)@S 1027.322755 1027.879 H(61)C(38)N(1)O(31) 1027.322755 H(61)C(38)N(1)O(31) O-linked glycosylation 1945 0.5 +dHex(3)Hex(3)HexNAc(1)@T 1127.41157 1128.0379 H(73)C(44)N(1)O(32) 1127.41157 H(73)C(44)N(1)O(32) O-linked glycosylation 1946 0.5 +dHex(3)Hex(3)HexNAc(1)@S 1127.41157 1128.0379 H(73)C(44)N(1)O(32) 1127.41157 H(73)C(44)N(1)O(32) O-linked glycosylation 1946 0.5 +Hex(6)HexNAc(1)@N 1175.396314 1176.0361 H(73)C(44)N(1)O(35) 1175.396314 H(73)C(44)N(1)O(35) N-linked glycosylation 1947 0.5 +Hex(1)HexNAc(4)dHex(1)Sulf(1)@T 1200.385037 1201.1151 H(72)C(44)N(4)O(32)S(1) 1200.385037 H(72)C(44)N(4)O(32)S(1) O-linked glycosylation 1948 0.5 +Hex(1)HexNAc(4)dHex(1)Sulf(1)@S 1200.385037 1201.1151 H(72)C(44)N(4)O(32)S(1) 1200.385037 H(72)C(44)N(4)O(32)S(1) O-linked glycosylation 1948 0.5 +dHex(1)Hex(2)HexNAc(1)NeuAc(2)@T 1255.433762 1256.1241 H(77)C(48)N(3)O(35) 1255.433762 H(77)C(48)N(3)O(35) O-linked glycosylation 1949 0.5 +dHex(1)Hex(2)HexNAc(1)NeuAc(2)@S 1255.433762 1256.1241 H(77)C(48)N(3)O(35) 1255.433762 H(77)C(48)N(3)O(35) O-linked glycosylation 1949 0.5 +dHex(3)Hex(3)HexNAc(2)@T 1330.490942 1331.2304 H(86)C(52)N(2)O(37) 1330.490942 H(86)C(52)N(2)O(37) O-linked glycosylation 1950 0.5 +dHex(3)Hex(3)HexNAc(2)@S 1330.490942 1331.2304 H(86)C(52)N(2)O(37) 1330.490942 H(86)C(52)N(2)O(37) O-linked glycosylation 1950 0.5 +dHex(2)Hex(1)HexNAc(4)Sulf(1)@T 1346.442946 1347.2563 H(82)C(50)N(4)O(36)S(1) 1346.442946 H(82)C(50)N(4)O(36)S(1) O-linked glycosylation 1951 0.5 +dHex(2)Hex(1)HexNAc(4)Sulf(1)@S 1346.442946 1347.2563 H(82)C(50)N(4)O(36)S(1) 1346.442946 H(82)C(50)N(4)O(36)S(1) O-linked glycosylation 1951 0.5 +dHex(1)Hex(2)HexNAc(4)Sulf(2)@T 1442.394675 1443.3189 H(82)C(50)N(4)O(40)S(2) 1442.394675 H(82)C(50)N(4)O(40)S(2) O-linked glycosylation 1952 0.5 +dHex(1)Hex(2)HexNAc(4)Sulf(2)@S 1442.394675 1443.3189 H(82)C(50)N(4)O(40)S(2) 1442.394675 H(82)C(50)N(4)O(40)S(2) O-linked glycosylation 1952 0.5 +Hex(9)@N 1458.475412 1459.2654 H(90)C(54)O(45) 1458.475412 H(90)C(54)O(45) N-linked glycosylation 1953 0.5 +dHex(2)Hex(3)HexNAc(3)Sulf(1)@T 1467.469221 1468.345 H(89)C(54)N(3)O(41)S(1) 1467.469221 H(89)C(54)N(3)O(41)S(1) O-linked glycosylation 1954 0.5 +dHex(2)Hex(3)HexNAc(3)Sulf(1)@S 1467.469221 1468.345 H(89)C(54)N(3)O(41)S(1) 1467.469221 H(89)C(54)N(3)O(41)S(1) O-linked glycosylation 1954 0.5 +dHex(2)Hex(5)HexNAc(2)Me(1)@T 1522.554331 1523.397 H(98)C(59)N(2)O(43) 1522.554331 H(98)C(59)N(2)O(43) O-linked glycosylation 1955 0.5 +dHex(2)Hex(5)HexNAc(2)Me(1)@S 1522.554331 1523.397 H(98)C(59)N(2)O(43) 1522.554331 H(98)C(59)N(2)O(43) O-linked glycosylation 1955 0.5 +dHex(2)Hex(2)HexNAc(4)Sulf(2)@T 1588.452584 1589.4601 H(92)C(56)N(4)O(44)S(2) 1588.452584 H(92)C(56)N(4)O(44)S(2) O-linked glycosylation 1956 0.5 +dHex(2)Hex(2)HexNAc(4)Sulf(2)@S 1588.452584 1589.4601 H(92)C(56)N(4)O(44)S(2) 1588.452584 H(92)C(56)N(4)O(44)S(2) O-linked glycosylation 1956 0.5 +Hex(9)HexNAc(1)@N 1661.554784 1662.4579 H(103)C(62)N(1)O(50) 1661.554784 H(103)C(62)N(1)O(50) N-linked glycosylation 1957 0.5 +dHex(3)Hex(2)HexNAc(4)Sulf(2)@S 1734.510493 1735.6013 H(102)C(62)N(4)O(48)S(2) 1734.510493 H(102)C(62)N(4)O(48)S(2) O-linked glycosylation 1958 0.5 +dHex(3)Hex(2)HexNAc(4)Sulf(2)@T 1734.510493 1735.6013 H(102)C(62)N(4)O(48)S(2) 1734.510493 H(102)C(62)N(4)O(48)S(2) O-linked glycosylation 1958 0.5 +Hex(4)HexNAc(4)NeuGc(1)@N 1767.619116 1768.5865 H(109)C(67)N(5)O(49) 1767.619116 H(109)C(67)N(5)O(49) N-linked glycosylation 1959 0.5 +Hex(4)HexNAc(4)NeuGc(1)@S 1767.619116 1768.5865 H(109)C(67)N(5)O(49) 1767.619116 H(109)C(67)N(5)O(49) O-linked glycosylation 1959 0.5 +Hex(4)HexNAc(4)NeuGc(1)@T 1767.619116 1768.5865 H(109)C(67)N(5)O(49) 1767.619116 H(109)C(67)N(5)O(49) O-linked glycosylation 1959 0.5 +dHex(4)Hex(3)HexNAc(2)NeuAc(1)@T 1767.644268 1768.6262 H(113)C(69)N(3)O(49) 1767.644268 H(113)C(69)N(3)O(49) O-linked glycosylation 1960 0.5 +dHex(4)Hex(3)HexNAc(2)NeuAc(1)@S 1767.644268 1768.6262 H(113)C(69)N(3)O(49) 1767.644268 H(113)C(69)N(3)O(49) O-linked glycosylation 1960 0.5 +Hex(3)HexNAc(5)NeuAc(1)@N 1792.65075 1793.639 H(112)C(69)N(6)O(48) 1792.65075 H(112)C(69)N(6)O(48) N-linked glycosylation 1961 0.5 +Hex(10)HexNAc(1)@N 1823.607608 1824.5985 H(113)C(68)N(1)O(55) 1823.607608 H(113)C(68)N(1)O(55) N-linked glycosylation 1962 0.5 +dHex(1)Hex(8)HexNAc(2)@N 1848.639242 1849.651 H(116)C(70)N(2)O(54) 1848.639242 H(116)C(70)N(2)O(54) N-linked glycosylation 1963 0.5 +Hex(3)HexNAc(4)NeuAc(2)@N 1880.666794 1881.701 H(116)C(72)N(6)O(51) 1880.666794 H(116)C(72)N(6)O(51) N-linked glycosylation 1964 0.5 +dHex(2)Hex(3)HexNAc(4)NeuAc(1)@N 1881.687195 1882.7289 H(119)C(73)N(5)O(51) 1881.687195 H(119)C(73)N(5)O(51) N-linked glycosylation 1965 0.5 +dHex(2)Hex(2)HexNAc(6)Sulf(1)@S 1914.654515 1915.7819 H(118)C(72)N(6)O(51)S(1) 1914.654515 H(118)C(72)N(6)O(51)S(1) O-linked glycosylation 1966 0.5 +dHex(2)Hex(2)HexNAc(6)Sulf(1)@T 1914.654515 1915.7819 H(118)C(72)N(6)O(51)S(1) 1914.654515 H(118)C(72)N(6)O(51)S(1) O-linked glycosylation 1966 0.5 +Hex(5)HexNAc(4)NeuAc(1)Ac(1)@N 1955.687589 1956.7643 H(121)C(75)N(5)O(54) 1955.687589 H(121)C(75)N(5)O(54) N-linked glycosylation 1967 0.5 +Hex(3)HexNAc(3)NeuAc(3)@S 1968.682838 1969.7631 H(120)C(75)N(6)O(54) 1968.682838 H(120)C(75)N(6)O(54) O-linked glycosylation 1968 0.5 +Hex(3)HexNAc(3)NeuAc(3)@T 1968.682838 1969.7631 H(120)C(75)N(6)O(54) 1968.682838 H(120)C(75)N(6)O(54) O-linked glycosylation 1968 0.5 +Hex(5)HexNAc(4)NeuAc(1)Ac(2)@N 1997.698154 1998.801 H(123)C(77)N(5)O(55) 1997.698154 H(123)C(77)N(5)O(55) N-linked glycosylation 1969 0.5 +Unknown:162@Any_C-term 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 +Unknown:162@E 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 +Unknown:162@D 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 +Unknown:162@Any_N-term 162.125595 162.2267 H(18)C(8)O(3) 0.0 Artefact 1970 0.0 +Unknown:177@D 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 +Unknown:177@E 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 +Unknown:177@Any_C-term 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 +Unknown:177@Any_N-term 176.744957 176.4788 H(-7)O(1)Fe(3) 0.0 Artefact 1971 0.0 +Unknown:210@D 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 +Unknown:210@E 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 +Unknown:210@Any_C-term 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 +Unknown:210@Any_N-term 210.16198 210.3126 H(22)C(13)O(2) 0.0 Artefact 1972 0.0 +Unknown:216@D 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 +Unknown:216@E 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 +Unknown:216@Any_C-term 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 +Unknown:216@Any_N-term 216.099774 216.231 H(16)C(10)O(5) 0.0 Artefact 1973 0.0 +Unknown:234@D 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 +Unknown:234@E 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 +Unknown:234@Any_C-term 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 +Unknown:234@Any_N-term 234.073953 234.2033 H(14)C(9)O(7) 0.0 Artefact 1974 0.0 +Unknown:248@D 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 +Unknown:248@E 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 +Unknown:248@Any_C-term 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 +Unknown:248@Any_N-term 248.19876 248.359 H(28)C(13)O(4) 0.0 Artefact 1975 0.0 +Unknown:250@D 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 +Unknown:250@E 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 +Unknown:250@Any_C-term 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 +Unknown:250@Any_N-term 249.981018 250.2075 H(4)C(10)N(1)O(5)S(1) 0.0 Artefact 1976 0.0 +Unknown:302@D 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 +Unknown:302@E 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 +Unknown:302@Any_C-term 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 +Unknown:302@Any_N-term 301.986514 302.2656 H(8)C(4)N(5)O(7)S(2) 0.0 Artefact 1977 0.0 +Unknown:306@D 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 +Unknown:306@E 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 +Unknown:306@Any_C-term 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 +Unknown:306@Any_N-term 306.095082 306.2659 H(18)C(12)O(9) 0.0 Artefact 1978 0.0 +Unknown:420@Any_N-term 420.051719 420.5888 H(24)C(12)N(2)O(6)S(4) 420.051719 H(24)C(12)N(2)O(6)S(4) Artefact 1979 0.5 +Unknown:420@Any_C-term 420.051719 420.5888 H(24)C(12)N(2)O(6)S(4) 420.051719 H(24)C(12)N(2)O(6)S(4) Artefact 1979 0.5 +Diethylphosphothione@Y 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 +Diethylphosphothione@T 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 +Diethylphosphothione@S 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 +Diethylphosphothione@K 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 +Diethylphosphothione@H 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 +Diethylphosphothione@C 152.006087 152.1518 H(9)C(4)O(2)P(1)S(1) 0.0 Chemical derivative 1986 0.0 +CIGG@K 330.136176 330.4032 H(22)C(13)N(4)O(4)S(1) 0.0 Post-translational 1990 0.0 +GNLLFLACYCIGG@K 1324.6308 1325.598 H(92)C(61)N(14)O(15)S(2) 0.0 Post-translational 1991 0.0 +Dimethylphosphothione@S 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 +Dimethylphosphothione@K 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 +Dimethylphosphothione@H 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 +Dimethylphosphothione@C 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 +Dimethylphosphothione@Y 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 +Dimethylphosphothione@T 123.974787 124.0987 H(5)C(2)O(2)P(1)S(1) 0.0 Chemical derivative 1987 0.0 +monomethylphosphothione@S 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 +monomethylphosphothione@K 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 +monomethylphosphothione@H 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 +monomethylphosphothione@C 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 +monomethylphosphothione@T 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 +monomethylphosphothione@Y 109.959137 110.0721 H(3)C(1)O(2)P(1)S(1) 0.0 Chemical derivative 1989 0.0 +TMPP-Ac:13C(9)@Y 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Artefact 1993 0.0 +TMPP-Ac:13C(9)@K 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Artefact 1993 0.0 +TMPP-Ac:13C(9)@Any_N-term 581.211328 581.474 H(33)C(20)13C(9)O(10)P(1) 0.0 Chemical derivative 1993 0.0 +Lys+O(2)@H 160.084792 160.1711 H(12)C(6)N(2)O(3) 0.0 Post-translational 2036 0.0 +ZQG@K 320.100836 320.2973 H(16)C(15)N(2)O(6) 134.036779 H(6)C(8)O(2) Chemical derivative 2001 0.5 +Xlink:DST[56]@Protein_N-term 55.989829 56.0202 C(2)O(2) 0.0 Chemical derivative 1999 0.0 +Xlink:DST[56]@K 55.989829 56.0202 C(2)O(2) 0.0 Chemical derivative 1999 0.0 +Haloxon@Y 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 +Haloxon@T 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 +Haloxon@S 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 +Haloxon@K 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 +Haloxon@H 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 +Haloxon@C 203.950987 204.9763 H(7)C(4)O(3)P(1)Cl(2) 0.0 Chemical derivative 2006 0.0 +Methamidophos-O@Y 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 +Methamidophos-O@T 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 +Methamidophos-O@S 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 +Methamidophos-O@K 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 +Methamidophos-O@H 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 +Methamidophos-O@C 92.997965 93.0217 H(4)C(1)N(1)O(2)P(1) 0.0 Chemical derivative 2008 0.0 +Nitrene@Y 12.995249 12.9988 H(-1)N(1) 0.0 Artefact 2014 0.0 +shTMT@Any_N-term 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 +shTMT@Protein_N-term 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 +shTMT@K 235.176741 235.2201 H(20)C(3)13C(9)15N(2)O(2) 0.0 Chemical derivative 2015 0.0 +TMTpro@T 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 +TMTpro@S 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 +TMTpro@H 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 +TMTpro@Protein_N-term 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 +TMTpro@Any_N-term 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 +TMTpro@K 304.207146 304.3127 H(25)C(8)13C(7)N(1)15N(2)O(3) 0.0 Isotopic label 2016 0.0 +TMTpro_zero@S 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 +TMTpro_zero@H 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 +TMTpro_zero@Protein_N-term 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 +TMTpro_zero@Any_N-term 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 +TMTpro_zero@K 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 +TMTpro_zero@T 295.189592 295.3773 H(25)C(15)N(3)O(3) 0.0 Chemical derivative 2017 0.0 +3-hydroxybenzyl-phosphate@S 186.008196 186.1018 H(7)C(7)O(4)P(1) 0.0 Chemical derivative 2041 0.0 +3-hydroxybenzyl-phosphate@K 186.008196 186.1018 H(7)C(7)O(4)P(1) 0.0 Chemical derivative 2041 0.0 +3-hydroxybenzyl-phosphate@T 186.008196 186.1018 H(7)C(7)O(4)P(1) 0.0 Chemical derivative 2041 0.0 +3-hydroxybenzyl-phosphate@Y 186.008196 186.1018 H(7)C(7)O(4)P(1) 0.0 Chemical derivative 2041 0.0 +Hex(6)HexNAc(5)NeuAc(3)@N 2861.000054 2862.5699 H(176)C(109)N(8)O(79) 2861.000054 H(176)C(109)N(8)O(79) N-linked glycosylation 2028 0.5 +Andro-H2O@C 332.19876 332.4339 H(28)C(20)O(4) 0.0 Chemical derivative 2025 0.0 +His+O(2)@H 169.048741 169.1381 H(7)C(6)N(3)O(3) 0.0 Post-translational 2027 0.0 +Hex(7)HexNAc(6)@S 2352.846 2354.1393 H(148)C(90)N(6)O(65) 2352.846 H(148)C(90)N(6)O(65) O-linked glycosylation 2029 0.5 +Hex(7)HexNAc(6)@T 2352.846 2354.1393 H(148)C(90)N(6)O(65) 2352.846 H(148)C(90)N(6)O(65) O-linked glycosylation 2029 0.5 +Hex(7)HexNAc(6)@N 2352.846 2354.1393 H(148)C(90)N(6)O(65) 2352.846 H(148)C(90)N(6)O(65) N-linked glycosylation 2029 0.5 +Met+O(2)@H 163.030314 163.1949 H(9)C(5)N(1)O(3)S(1) 0.0 Chemical derivative 2033 0.0 +Gly+O(2)@H 89.011293 89.0501 H(3)C(2)N(1)O(3) 0.0 Chemical derivative 2034 0.0 +Glu+O(2)@H 161.032422 161.1128 H(7)C(5)N(1)O(5) 0.0 Post-translational 2037 0.0 +MBS+peptide@C 1482.77 1483.7597 H(108)C(81)N(7)O(19) 0.0 Chemical derivative 2040 0.0 +phenyl-phosphate@S 155.997631 156.0759 H(5)C(6)O(3)P(1) 0.0 Chemical derivative 2042 0.0 +phenyl-phosphate@K 155.997631 156.0759 H(5)C(6)O(3)P(1) 0.0 Chemical derivative 2042 0.0 +phenyl-phosphate@T 155.997631 156.0759 H(5)C(6)O(3)P(1) 0.0 Chemical derivative 2042 0.0 +phenyl-phosphate@Y 155.997631 156.0759 H(5)C(6)O(3)P(1) 0.0 Chemical derivative 2042 0.0 +RBS-ID_Uridine@Y 244.069536 244.2014 H(12)C(9)N(2)O(6) 0.0 Other 2044 0.0 +pRBS-ID_4-thiouridine@F 226.058972 226.1861 H(10)C(9)N(2)O(5) 132.042259 H(8)C(5)O(4) Other 2054 0.5 +Biotin:Aha-PC@M 690.24316 690.7246 H(38)C(29)N(8)O(10)S(1) 0.0 Chemical derivative 2053 0.0 +DBIA@C 296.184841 296.3654 H(24)C(14)N(4)O(3) 0.0 Chemical derivative 2062 0.0 +pRBS-ID_6-thioguanosine@W 265.081104 265.2254 H(11)C(10)N(5)O(4) 132.042259 H(8)C(5)O(4) Other 2055 0.5 +6C-CysPAT@Y 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Artefact 2057 0.0 +6C-CysPAT@T 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Artefact 2057 0.0 +6C-CysPAT@S 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Artefact 2057 0.0 +6C-CysPAT@E 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Artefact 2057 0.0 +6C-CysPAT@D 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Artefact 2057 0.0 +6C-CysPAT@H 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Artefact 2057 0.0 +6C-CysPAT@Any_N-term 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Artefact 2057 0.0 +6C-CysPAT@K 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Artefact 2057 0.0 +6C-CysPAT@C 221.081695 221.1907 H(16)C(8)N(1)O(4)P(1) 0.0 Chemical derivative 2057 0.0 +Xlink:DSPP[210]@Protein_N-term 209.97181 210.0802 H(3)C(8)O(5)P(1) 0.0 Chemical derivative 2058 0.0 +Xlink:DSPP[210]@K 209.97181 210.0802 H(3)C(8)O(5)P(1) 0.0 Chemical derivative 2058 0.0 +Xlink:DSPP[228]@Protein_N-term 227.982375 228.0955 H(5)C(8)O(6)P(1) 0.0 Chemical derivative 2059 0.0 +Xlink:DSPP[228]@K 227.982375 228.0955 H(5)C(8)O(6)P(1) 0.0 Chemical derivative 2059 0.0 +Xlink:DSPP[331]@Protein_N-term 331.045704 331.2152 H(14)C(12)N(1)O(8)P(1) 0.0 Chemical derivative 2060 0.0 +Xlink:DSPP[331]@K 331.045704 331.2152 H(14)C(12)N(1)O(8)P(1) 0.0 Chemical derivative 2060 0.0 +Xlink:DSPP[226]@K 225.990534 226.1028 H(5)C(8)N(1)O(5)P(1) 0.0 Chemical derivative 2061 0.0 +Xlink:DSPP[226]@Protein_N-term 225.990534 226.1028 H(5)C(8)N(1)O(5)P(1) 0.0 Chemical derivative 2061 0.0 +N6pAMP@Y 367.06817 367.2539 H(14)C(13)N(5)O(6)P(1) 0.0 Chemical derivative 2073 0.0 +N6pAMP@T 367.06817 367.2539 H(14)C(13)N(5)O(6)P(1) 0.0 Chemical derivative 2073 0.0 +N6pAMP@S 367.06817 367.2539 H(14)C(13)N(5)O(6)P(1) 0.0 Chemical derivative 2073 0.0 +DABCYL-C2-maleimide@K 391.16444 391.4231 H(21)C(21)N(5)O(3) 251.105862 H(13)C(15)N(3)O(1) Chemical derivative 2074 0.5 +DABCYL-C2-maleimide@C 391.16444 391.4231 H(21)C(21)N(5)O(3) 251.105862 H(13)C(15)N(3)O(1) Chemical derivative 2074 0.5 +Ethynyl@C 24.0 24.0214 C(2) 0.0 Chemical derivative 2081 0.0 +Mono_Nγ-propargyl-L-Gln_desthiobiotin@C 596.328211 596.6764 H(44)C(26)N(8)O(8) 0.0 Chemical derivative 2067 0.0 +Di_L-Glu_Nγ-propargyl-L-Gln_desthiobiotin@E 709.375889 709.7909 H(51)C(31)N(9)O(10) 469.301268 H(39)C(21)N(7)O(5) Chemical derivative 2068 0.5 +Di_L-Glu_Nγ-propargyl-L-Gln_desthiobiotin@D 709.375889 709.7909 H(51)C(31)N(9)O(10) 469.301268 H(39)C(21)N(7)O(5) Chemical derivative 2068 0.5 +Di_L-Gln_Nγ-propargyl-L-Gln_desthiobiotin@E 708.391873 708.8062 H(52)C(31)N(10)O(9) 726.402438 H(54)C(31)N(10)O(10) Chemical derivative 2069 0.5 +Di_L-Gln_Nγ-propargyl-L-Gln_desthiobiotin@D 708.391873 708.8062 H(52)C(31)N(10)O(9) 726.402438 H(54)C(31)N(10)O(10) Chemical derivative 2069 0.5 +L-Gln@D 128.058578 128.1292 H(8)C(5)N(2)O(2) 0.0 Post-translational 2070 0.0 +L-Gln@E 128.058578 128.1292 H(8)C(5)N(2)O(2) 0.0 Post-translational 2070 0.0 +Glyceroyl@Protein_N-term 88.016044 88.0621 H(4)C(3)O(3) 0.0 Post-translational 2072 0.0 +Glyceroyl@K 88.016044 88.0621 H(4)C(3)O(3) 0.0 Post-translational 2072 0.0 +NBF@R 163.001791 163.0904 H(1)C(6)N(3)O(3) 0.0 Chemical derivative 2079 0.0 +NBF@K 163.001791 163.0904 H(1)C(6)N(3)O(3) 0.0 Chemical derivative 2079 0.0 +NBF@C 163.001791 163.0904 H(1)C(6)N(3)O(3) 0.0 Chemical derivative 2079 0.0 +DCP@C 168.078644 168.1898 H(12)C(9)O(3) 0.0 Chemical derivative 2080 0.0 +QQTGG@K 471.207761 471.465 H(29)C(18)N(7)O(8) 0.0 Other 2082 0.0 +Pyro-QQTGG@K 454.181212 454.4344 H(26)C(18)N(6)O(8) 0.0 Other 2083 0.0 +NQTGG@K 457.192111 457.4384 H(27)C(17)N(7)O(8) 0.0 Other 2084 0.0 +DVFQQQTGG@K 960.43011 960.9865 H(60)C(41)N(12)O(15) 0.0 Other 2085 0.0 +iST-NHS_specific_cysteine_modification@C 113.084064 113.1576 H(11)C(6)N(1)O(1) 0.0 Chemical derivative 2086 0.0 +Label:13C(2)15N(1)@G 3.003745 2.9787 C(-2)13C(2)N(-1)15N(1) 0.0 Isotopic label 2088 0.0 +GlyGly@K 114.042927 114.1026 H(6)C(4)N(2)O(2) 0.0 Multiple 121 1000000.0 diff --git a/scripts/unimod_to_tsv.ipynb b/scripts/unimod_to_tsv.ipynb index fe640ada..02bab711 100644 --- a/scripts/unimod_to_tsv.ipynb +++ b/scripts/unimod_to_tsv.ipynb @@ -1,45 +1,49 @@ { "cells": [ { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "import xml.etree.ElementTree as ET\n", - "import yaml\n", - "import pandas as pd" + "url = \"https://www.unimod.org/xml/unimod.xml\"\n", + "\n", + "# download unimod.xml to temp directory\n", + "import tempfile\n", + "import os\n", + "import urllib.request\n", + "\n", + "temp_dir = tempfile.mkdtemp()\n", + "temp_file = os.path.join(temp_dir, 'unimod.xml')\n", + "urllib.request.urlretrieve(url, temp_file)" ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ + "import xml.etree.ElementTree as ET\n", + "import yaml\n", + "import pandas as pd\n", + "\n", "def save_yaml(filename, unimod):\n", " with open(filename, \"w\") as file:\n", " yaml.dump(unimod, file)\n", "\n", + "xml = ET.parse(temp_file)\n", + "root = xml.getroot()\n", + "\n", "\n", "def get_composition(node):\n", " composition = \"\"\n", " for elem in node.findall(f'{xmlns}element'):\n", " composition += elem.attrib['symbol']+'('+elem.attrib['number']+')'\n", - " return composition" - ] - }, - { - "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-25T11:39:09.284411Z", - "start_time": "2024-06-25T11:39:08.507205Z" - } - }, - "source": [ - "xml = ET.parse('unimod.xml')\n", - "root = xml.getroot()\n", + " return composition\n", + "\n", + "def replace_modseq_with_whitespace(modseq):\n", + " return modseq.replace(\" \", \"_\")\n", "\n", "xmlns = '{http://www.unimod.org/xmlns/schema/unimod_2}'\n", "unimod = {}\n", @@ -69,6 +73,8 @@ " ptm_nl_composition = get_composition(nl)\n", " break\n", " mod_site = f'{modname}@{site}'\n", + " mod_site = replace_modseq_with_whitespace(mod_site)\n", + "\n", " unimod[mod_site] = {}\n", " unimod[mod_site]['unimod_mass'] = float(unimod_mass)\n", " unimod[mod_site]['unimod_avge_mass'] = float(unimod_avge_mass)\n", @@ -77,10 +83,14 @@ " unimod[mod_site]['modloss_composition'] = ptm_nl_composition\n", " unimod[mod_site]['classification'] = _class\n", " unimod[mod_site]['unimod_id'] = int(id)\n", + " unimod[mod_site]['smiles'] = ''\n", + "\n", "\n", " if '~' in site:\n", " print(mod_site)\n", " mod_site = f'{modname}@{pos}'\n", + " mod_site = replace_modseq_with_whitespace(mod_site)\n", + "\n", " unimod[mod_site] = {}\n", " unimod[mod_site]['unimod_mass'] = float(unimod_mass)\n", " unimod[mod_site]['unimod_avge_mass'] = float(unimod_avge_mass)\n", @@ -88,23 +98,18 @@ " unimod[mod_site]['unimod_modloss'] = float(ptm_nl)\n", " unimod[mod_site]['modloss_composition'] = ptm_nl_composition\n", " unimod[mod_site]['classification'] = _class\n", - " unimod[mod_site]['unimod_id'] = int(id)" - ], - "outputs": [], - "execution_count": 1 + " unimod[mod_site]['unimod_id'] = int(id)\n", + " unimod[mod_site]['smiles'] = ''" + ] }, { "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-25T11:56:30.184321Z", - "start_time": "2024-06-25T11:56:30.165972Z" - } - }, + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df = pd.DataFrame().from_dict(unimod, orient='index')\n", - "df.index = df.index.str.replace(\" \", \"_\", regex=False)\n", - "df['modloss_importance'] = 0.0\n", + "df['modloss_importance'] = 0\n", "df.loc[df.modloss_composition != '','modloss_importance'] = 0.5\n", "df.loc['Phospho@S','modloss_importance'] = 1e8\n", "df.loc['Phospho@T','modloss_importance'] = 1e7\n", @@ -115,248 +120,52 @@ "df = df[['mod_name']+[col for col in df.columns if col != 'mod_name']]\n", "df['unimod_id'] = df.unimod_id.astype(int)\n", "df" - ], - "outputs": [ - { - "data": { - "text/plain": [ - " mod_name unimod_mass unimod_avge_mass \\\n", - "Acetyl@T Acetyl@T 42.010565 42.0367 \n", - "Acetyl@Protein_N-term Acetyl@Protein_N-term 42.010565 42.0367 \n", - "Acetyl@S Acetyl@S 42.010565 42.0367 \n", - "Acetyl@C Acetyl@C 42.010565 42.0367 \n", - "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 42.0367 \n", - "... ... ... ... \n", - "TMTpro_zero@K TMTpro_zero@K 295.189592 295.3773 \n", - "TMTpro_zero@T TMTpro_zero@T 295.189592 295.3773 \n", - "Andro-H2O@C Andro-H2O@C 332.198760 332.4339 \n", - "His+O(2)@H His+O(2)@H 169.048741 169.1381 \n", - "GlyGly@K GlyGly@K 114.042927 114.1026 \n", - "\n", - " composition unimod_modloss modloss_composition \\\n", - "Acetyl@T H(2)C(2)O(1) 0.0 \n", - "Acetyl@Protein_N-term H(2)C(2)O(1) 0.0 \n", - "Acetyl@S H(2)C(2)O(1) 0.0 \n", - "Acetyl@C H(2)C(2)O(1) 0.0 \n", - "Acetyl@Any_N-term H(2)C(2)O(1) 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K H(25)C(15)N(3)O(3) 0.0 \n", - "TMTpro_zero@T H(25)C(15)N(3)O(3) 0.0 \n", - "Andro-H2O@C H(28)C(20)O(4) 0.0 \n", - "His+O(2)@H H(7)C(6)N(3)O(3) 0.0 \n", - "GlyGly@K H(6)C(4)N(2)O(2) 0.0 \n", - "\n", - " classification unimod_id modloss_importance \n", - "Acetyl@T Post-translational 1 0.0 \n", - "Acetyl@Protein_N-term Post-translational 1 0.0 \n", - "Acetyl@S Post-translational 1 0.0 \n", - "Acetyl@C Post-translational 1 0.0 \n", - "Acetyl@Any_N-term Multiple 1 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K Chemical derivative 2017 0.0 \n", - "TMTpro_zero@T Chemical derivative 2017 0.0 \n", - "Andro-H2O@C Chemical derivative 2025 0.0 \n", - "His+O(2)@H Post-translational 2027 0.0 \n", - "GlyGly@K Multiple 121 1000000.0 \n", - "\n", - "[2685 rows x 9 columns]" - ], - "text/html": [ - "
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    mod_nameunimod_massunimod_avge_masscompositionunimod_modlossmodloss_compositionclassificationunimod_idmodloss_importance
    Acetyl@TAcetyl@T42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.0
    Acetyl@Protein_N-termAcetyl@Protein_N-term42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.0
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    Acetyl@CAcetyl@C42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.0
    Acetyl@Any_N-termAcetyl@Any_N-term42.01056542.0367H(2)C(2)O(1)0.0Multiple10.0
    ..............................
    TMTpro_zero@KTMTpro_zero@K295.189592295.3773H(25)C(15)N(3)O(3)0.0Chemical derivative20170.0
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    His+O(2)@HHis+O(2)@H169.048741169.1381H(7)C(6)N(3)O(3)0.0Post-translational20270.0
    GlyGly@KGlyGly@K114.042927114.1026H(6)C(4)N(2)O(2)0.0Multiple1211000000.0
    \n", - "

    2685 rows × 9 columns

    \n", - "
    " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 16 + ] }, { "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-25T11:54:47.941969Z", - "start_time": "2024-06-25T11:54:47.931377Z" - } - }, - "source": [ - "df.to_csv('../alphabase/constants/const_files/modification.tsv', index=False, sep='\\t', header=True)" - ], + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": 15 + "source": [ + "from alphabase.constants.modification import MOD_DF\n", + "\n", + "stored_columns = ['mod_name', 'unimod_mass', 'unimod_avge_mass', 'composition', 'unimod_modloss', 'modloss_composition', 'classification', 'unimod_id', 'modloss_importance','smiles']\n", + "\n", + "if 'smiles' not in MOD_DF.columns:\n", + " MOD_DF['smiles'] = ''\n", + "\n", + "mod_df = MOD_DF[stored_columns]\n", + "new_modifications = df[~df.index.isin(mod_df.index)]" + ] }, { + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], + "source": [ + "new_mod_df = pd.concat([mod_df, new_modifications])\n", + "new_mod_df = new_mod_df.sort_values(by=['unimod_id','mod_name'])" + ] + }, + { "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], + "source": [ + "new_mod_df" + ] + }, + { + "cell_type": "code", "execution_count": null, - "source": "" + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv('../alphabase/constants/const_files/modification.tsv', index=False, sep='\\t', header=True)" + ] } ], "metadata": { @@ -375,7 +184,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.11.7" }, "vscode": { "interpreter": { From f29a255fa6fc636a3e9d590205ab30a0df2e69a6 Mon Sep 17 00:00:00 2001 From: jalew188 Date: Wed, 17 Jul 2024 13:24:22 +0200 Subject: [PATCH 40/53] #198 fix mods with spaces --- CHANGELOG.md | 2 +- nbs_tests/constants/modification.ipynb | 48 +++++++++---------- nbs_tests/spectral_library/library_base.ipynb | 2 +- 3 files changed, 26 insertions(+), 26 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6c373a5b..0a33a703 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -12,7 +12,7 @@ Follow the changelog format from https://keepachangelog.com/en/1.0.0/. ### Changed -- `mod@Any N-term` and `mod@Any_N-term` are both supported, `Any_N-term` is prefered as there are no spaces and hence better for command line tools. The same for `mod@Protein N-term`, `mod@Any C-term`, and `mod@Protein C-term`. +- `mod@Any_N-term` are both supported, `Any_N-term` is prefered as there are no spaces and hence better for command line tools. The same for `mod@Protein_N-term`, `mod@Any_C-term`, and `mod@Protein_C-term`. - Enable customizing dtypes of peak mz and intensty values. - `SWATHLibraryReader` to `LibraryBaseReader` in `alphabase.spectral_library.reader`. - New `LibraryReaderBase._get_fragment_intensity` implementation which is called at the end of the parsing process in `PSMReaderBase._post_process`. This allows it to operate only on the translated column names. By default, all non-fragment columns will be grouped and part of the final output. diff --git a/nbs_tests/constants/modification.ipynb b/nbs_tests/constants/modification.ipynb index a3996002..355c919a 100644 --- a/nbs_tests/constants/modification.ipynb +++ b/nbs_tests/constants/modification.ipynb @@ -272,7 +272,7 @@ "Acetyl@Protein_N-term Acetyl@Protein_N-term 42.010565 42.0367 \n", "Acetyl@S Acetyl@S 42.010565 42.0367 \n", "Acetyl@C Acetyl@C 42.010565 42.0367 \n", - "Acetyl@Any N-term Acetyl@Any N-term 42.010565 42.0367 \n", + "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 42.0367 \n", "... ... ... ... \n", "TMTpro_zero@K TMTpro_zero@K 295.189592 295.3773 \n", "TMTpro_zero@T TMTpro_zero@T 295.189592 295.3773 \n", @@ -286,7 +286,7 @@ "Acetyl@Protein_N-term H(2)C(2)O(1) 0.0 \n", "Acetyl@S H(2)C(2)O(1) 0.0 \n", "Acetyl@C H(2)C(2)O(1) 0.0 \n", - "Acetyl@Any N-term H(2)C(2)O(1) 0.0 \n", + "Acetyl@Any_N-term H(2)C(2)O(1) 0.0 \n", "... ... ... ... \n", "TMTpro_zero@K H(25)C(15)N(3)O(3) 0.0 \n", "TMTpro_zero@T H(25)C(15)N(3)O(3) 0.0 \n", @@ -300,7 +300,7 @@ "Acetyl@Protein_N-term Post-translational 1 0.0 \n", "Acetyl@S Post-translational 1 0.0 \n", "Acetyl@C Post-translational 1 0.0 \n", - "Acetyl@Any N-term Multiple 1 0.0 \n", + "Acetyl@Any_N-term Multiple 1 0.0 \n", "... ... ... ... \n", "TMTpro_zero@K Chemical derivative 2017 0.0 \n", "TMTpro_zero@T Chemical derivative 2017 0.0 \n", @@ -314,7 +314,7 @@ "Acetyl@Protein_N-term 42.010565 0.0 0.0 \n", "Acetyl@S 42.010565 0.0 0.0 \n", "Acetyl@C 42.010565 0.0 0.0 \n", - "Acetyl@Any N-term 42.010565 0.0 0.0 \n", + "Acetyl@Any_N-term 42.010565 0.0 0.0 \n", "... ... ... ... \n", "TMTpro_zero@K 295.189592 0.0 0.0 \n", "TMTpro_zero@T 295.189592 0.0 0.0 \n", @@ -510,8 +510,8 @@ " 0.0\n", " \n", " \n", - " Acetyl@Any N-term\n", - " Acetyl@Any N-term\n", + " Acetyl@Any_N-term\n", + " Acetyl@Any_N-term\n", " 42.010565\n", " 42.0367\n", " H(2)C(2)O(1)\n", @@ -712,7 +712,7 @@ " mod_name unimod_mass \\\n", "mod_name \n", "Acetyl@T Acetyl@T 42.010565 \n", - "Acetyl@Any N-term Acetyl@Any N-term 42.010565 \n", + "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 \n", "Acetyl@Y Acetyl@Y 42.010565 \n", "Acetyl@R Acetyl@R 42.010565 \n", "ICAT-G@C ICAT-G@C 486.251206 \n", @@ -729,7 +729,7 @@ " unimod_avge_mass composition \\\n", "mod_name \n", "Acetyl@T 42.0367 H(2)C(2)O(1) \n", - "Acetyl@Any N-term 42.0367 H(2)C(2)O(1) \n", + "Acetyl@Any_N-term 42.0367 H(2)C(2)O(1) \n", "Acetyl@Y 42.0367 H(2)C(2)O(1) \n", "Acetyl@R 42.0367 H(2)C(2)O(1) \n", "ICAT-G@C 486.6253 H(38)C(22)N(4)O(6)S(1) \n", @@ -746,7 +746,7 @@ " unimod_modloss modloss_composition \\\n", "mod_name \n", "Acetyl@T 0.000000 \n", - "Acetyl@Any N-term 0.000000 \n", + "Acetyl@Any_N-term 0.000000 \n", "Acetyl@Y 0.000000 \n", "Acetyl@R 0.000000 \n", "ICAT-G@C 0.000000 \n", @@ -763,7 +763,7 @@ " classification unimod_id \\\n", "mod_name \n", "Acetyl@T Post-translational 1 \n", - "Acetyl@Any N-term Multiple 1 \n", + "Acetyl@Any_N-term Multiple 1 \n", "Acetyl@Y Chemical derivative 1 \n", "Acetyl@R Artefact 1 \n", "ICAT-G@C Isotopic label 8 \n", @@ -780,7 +780,7 @@ " modloss_importance mass \\\n", "mod_name \n", "Acetyl@T 0.0 42.010565 \n", - "Acetyl@Any N-term 0.0 42.010565 \n", + "Acetyl@Any_N-term 0.0 42.010565 \n", "Acetyl@Y 0.0 42.010565 \n", "Acetyl@R 0.0 42.010565 \n", "ICAT-G@C 0.0 486.251206 \n", @@ -797,7 +797,7 @@ " modloss_original modloss \n", "mod_name \n", "Acetyl@T 0.000000 0.0 \n", - "Acetyl@Any N-term 0.000000 0.0 \n", + "Acetyl@Any_N-term 0.000000 0.0 \n", "Acetyl@Y 0.000000 0.0 \n", "Acetyl@R 0.000000 0.0 \n", "ICAT-G@C 0.000000 0.0 \n", @@ -951,8 +951,8 @@ " False\n", " \n", " \n", - " Acetyl@Any N-term\n", - " Acetyl@Any N-term\n", + " Acetyl@Any_N-term\n", + " Acetyl@Any_N-term\n", " 42.010565\n", " 42.0367\n", " H(2)C(2)O(1)\n", @@ -1074,7 +1074,7 @@ "Acetyl@Protein_N-term Acetyl@Protein_N-term 42.010565 42.0367 \n", "Acetyl@S Acetyl@S 42.010565 42.0367 \n", "Acetyl@C Acetyl@C 42.010565 42.0367 \n", - "Acetyl@Any N-term Acetyl@Any N-term 42.010565 42.0367 \n", + "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 42.0367 \n", "... ... ... ... \n", "TMTpro_zero@k TMTpro_zero@k 295.189592 295.3773 \n", "TMTpro_zero@t TMTpro_zero@t 295.189592 295.3773 \n", @@ -1088,7 +1088,7 @@ "Acetyl@Protein_N-term H(2)C(2)O(1) 0.0 \n", "Acetyl@S H(2)C(2)O(1) 0.0 \n", "Acetyl@C H(2)C(2)O(1) 0.0 \n", - "Acetyl@Any N-term H(2)C(2)O(1) 0.0 \n", + "Acetyl@Any_N-term H(2)C(2)O(1) 0.0 \n", "... ... ... ... \n", "TMTpro_zero@k H(25)C(15)N(3)O(3) 0.0 \n", "TMTpro_zero@t H(25)C(15)N(3)O(3) 0.0 \n", @@ -1102,7 +1102,7 @@ "Acetyl@Protein_N-term Post-translational 1 0.0 \n", "Acetyl@S Post-translational 1 0.0 \n", "Acetyl@C Post-translational 1 0.0 \n", - "Acetyl@Any N-term Multiple 1 0.0 \n", + "Acetyl@Any_N-term Multiple 1 0.0 \n", "... ... ... ... \n", "TMTpro_zero@k Chemical derivative 2017 0.0 \n", "TMTpro_zero@t Chemical derivative 2017 0.0 \n", @@ -1116,7 +1116,7 @@ "Acetyl@Protein_N-term 42.010565 0.0 0.0 False \n", "Acetyl@S 42.010565 0.0 0.0 False \n", "Acetyl@C 42.010565 0.0 0.0 False \n", - "Acetyl@Any N-term 42.010565 0.0 0.0 False \n", + "Acetyl@Any_N-term 42.010565 0.0 0.0 False \n", "... ... ... ... ... \n", "TMTpro_zero@k 295.189592 0.0 0.0 True \n", "TMTpro_zero@t 295.189592 0.0 0.0 True \n", @@ -1640,8 +1640,8 @@ " False\n", " \n", " \n", - " Acetyl@Any N-term\n", - " Acetyl@Any N-term\n", + " Acetyl@Any_N-term\n", + " Acetyl@Any_N-term\n", " 42.010565\n", " 42.0367\n", " H(2)C(2)O(1)\n", @@ -1763,7 +1763,7 @@ "Acetyl@Protein_N-term Acetyl@Protein_N-term 42.010565 42.0367 \n", "Acetyl@S Acetyl@S 42.010565 42.0367 \n", "Acetyl@C Acetyl@C 42.010565 42.0367 \n", - "Acetyl@Any N-term Acetyl@Any N-term 42.010565 42.0367 \n", + "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 42.0367 \n", "... ... ... ... \n", "GlyGly@k GlyGly@k 114.042927 114.1026 \n", "Hello@S Hello@S 0.000000 0.0000 \n", @@ -1777,7 +1777,7 @@ "Acetyl@Protein_N-term H(2)C(2)O(1) 0.0 \n", "Acetyl@S H(2)C(2)O(1) 0.0 \n", "Acetyl@C H(2)C(2)O(1) 0.0 \n", - "Acetyl@Any N-term H(2)C(2)O(1) 0.0 \n", + "Acetyl@Any_N-term H(2)C(2)O(1) 0.0 \n", "... ... ... ... \n", "GlyGly@k H(6)C(4)N(2)O(2) 0.0 \n", "Hello@S H(2) 0.0 \n", @@ -1791,7 +1791,7 @@ "Acetyl@Protein_N-term Post-translational 1.0 0.000000e+00 \n", "Acetyl@S Post-translational 1.0 0.000000e+00 \n", "Acetyl@C Post-translational 1.0 0.000000e+00 \n", - "Acetyl@Any N-term Multiple 1.0 0.000000e+00 \n", + "Acetyl@Any_N-term Multiple 1.0 0.000000e+00 \n", "... ... ... ... \n", "GlyGly@k Post-translational 121.0 1.000000e+06 \n", "Hello@S User-added 0.0 0.000000e+00 \n", @@ -1805,7 +1805,7 @@ "Acetyl@Protein_N-term 42.010565 0.0 0.000000 False \n", "Acetyl@S 42.010565 0.0 0.000000 False \n", "Acetyl@C 42.010565 0.0 0.000000 False \n", - "Acetyl@Any N-term 42.010565 0.0 0.000000 False \n", + "Acetyl@Any_N-term 42.010565 0.0 0.000000 False \n", "... ... ... ... ... \n", "GlyGly@k 114.042927 0.0 0.000000 True \n", "Hello@S 2.015650 0.0 0.000000 0 \n", diff --git a/nbs_tests/spectral_library/library_base.ipynb b/nbs_tests/spectral_library/library_base.ipynb index 8504bfa3..743c9104 100644 --- a/nbs_tests/spectral_library/library_base.ipynb +++ b/nbs_tests/spectral_library/library_base.ipynb @@ -417,7 +417,7 @@ "\n", "repeat = 3\n", "peptides = ['AGHCEWQMK']*repeat\n", - "mods = ['Acetyl@Protein N-term;Carbamidomethyl@C;Oxidation@M']*repeat\n", + "mods = ['Acetyl@Protein_N-term;Carbamidomethyl@C;Oxidation@M']*repeat\n", "sites = ['0;4;8']*repeat\n", "peptides += ['AGHCEWQMKAADER']*repeat\n", "mods += ['']*repeat\n", From 77ebdf0cbfaaeb9f6ae135222e29c6a1d1256de0 Mon Sep 17 00:00:00 2001 From: jalew188 Date: Wed, 17 Jul 2024 15:57:57 +0200 Subject: [PATCH 41/53] #187 fix space PTMs --- docs/tutorials/basic_definations.ipynb | 50 +++++++++++++------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/docs/tutorials/basic_definations.ipynb b/docs/tutorials/basic_definations.ipynb index 14cb9358..a7fe1ba8 100644 --- a/docs/tutorials/basic_definations.ipynb +++ b/docs/tutorials/basic_definations.ipynb @@ -648,7 +648,7 @@ "source": [ "### Modifications\n", "\n", - "In AlphaBase, we used `mod_name@aa` to represent a modification, the `mod_name` is from UniMod. We also used `mod_name@Protein N-term`, `mod_name@Any N-term` and `mod_name@Any C-term` for terminal modifications, which follow the UniMod terminal name schema.\n", + "In AlphaBase, we used `mod_name@aa` to represent a modification, the `mod_name` is from UniMod. We also used `mod_name@Protein_N-term`, `mod_name@Any_N-term` and `mod_name@Any_C-term` for terminal modifications, which follow the UniMod terminal name schema.\n", "\n", "The default modification TSV is stored in `alphabase/constants/const_files/modification.tsv`, users can add more modifications into the tsv file (only `mod_name` and `composition` colums are required). Please https://github.com/MannLabs/alphabase/blob/main/alphabase/constants/const_files/modification.tsv." ] @@ -725,8 +725,8 @@ " 0.0\n", " \n", " \n", - " Acetyl@Protein N-term\n", - " Acetyl@Protein N-term\n", + " Acetyl@Protein_N-term\n", + " Acetyl@Protein_N-term\n", " 42.010565\n", " 42.0367\n", " H(2)C(2)O(1)\n", @@ -770,8 +770,8 @@ " 0.0\n", " \n", " \n", - " Acetyl@Any N-term\n", - " Acetyl@Any N-term\n", + " Acetyl@Any_N-term\n", + " Acetyl@Any_N-term\n", " 42.010565\n", " 42.0367\n", " H(2)C(2)O(1)\n", @@ -867,7 +867,7 @@ " H(6)C(4)N(2)O(2)\n", " 0.0\n", " \n", - " Post-translational\n", + " Multiple\n", " 121\n", " 1000000.0\n", " 114.042927\n", @@ -883,10 +883,10 @@ " mod_name unimod_mass unimod_avge_mass \\\n", "mod_name \n", "Acetyl@T Acetyl@T 42.010565 42.0367 \n", - "Acetyl@Protein N-term Acetyl@Protein N-term 42.010565 42.0367 \n", + "Acetyl@Protein_N-term Acetyl@Protein_N-term 42.010565 42.0367 \n", "Acetyl@S Acetyl@S 42.010565 42.0367 \n", "Acetyl@C Acetyl@C 42.010565 42.0367 \n", - "Acetyl@Any N-term Acetyl@Any N-term 42.010565 42.0367 \n", + "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 42.0367 \n", "... ... ... ... \n", "TMTpro_zero@K TMTpro_zero@K 295.189592 295.3773 \n", "TMTpro_zero@T TMTpro_zero@T 295.189592 295.3773 \n", @@ -897,10 +897,10 @@ " composition unimod_modloss modloss_composition \\\n", "mod_name \n", "Acetyl@T H(2)C(2)O(1) 0.0 \n", - "Acetyl@Protein N-term H(2)C(2)O(1) 0.0 \n", + "Acetyl@Protein_N-term H(2)C(2)O(1) 0.0 \n", "Acetyl@S H(2)C(2)O(1) 0.0 \n", "Acetyl@C H(2)C(2)O(1) 0.0 \n", - "Acetyl@Any N-term H(2)C(2)O(1) 0.0 \n", + "Acetyl@Any_N-term H(2)C(2)O(1) 0.0 \n", "... ... ... ... \n", "TMTpro_zero@K H(25)C(15)N(3)O(3) 0.0 \n", "TMTpro_zero@T H(25)C(15)N(3)O(3) 0.0 \n", @@ -911,24 +911,24 @@ " classification unimod_id modloss_importance \\\n", "mod_name \n", "Acetyl@T Post-translational 1 0.0 \n", - "Acetyl@Protein N-term Post-translational 1 0.0 \n", + "Acetyl@Protein_N-term Post-translational 1 0.0 \n", "Acetyl@S Post-translational 1 0.0 \n", "Acetyl@C Post-translational 1 0.0 \n", - "Acetyl@Any N-term Multiple 1 0.0 \n", + "Acetyl@Any_N-term Multiple 1 0.0 \n", "... ... ... ... \n", "TMTpro_zero@K Chemical derivative 2017 0.0 \n", "TMTpro_zero@T Chemical derivative 2017 0.0 \n", "Andro-H2O@C Chemical derivative 2025 0.0 \n", "His+O(2)@H Post-translational 2027 0.0 \n", - "GlyGly@K Post-translational 121 1000000.0 \n", + "GlyGly@K Multiple 121 1000000.0 \n", "\n", " mass modloss_original modloss \n", "mod_name \n", "Acetyl@T 42.010565 0.0 0.0 \n", - "Acetyl@Protein N-term 42.010565 0.0 0.0 \n", + "Acetyl@Protein_N-term 42.010565 0.0 0.0 \n", "Acetyl@S 42.010565 0.0 0.0 \n", "Acetyl@C 42.010565 0.0 0.0 \n", - "Acetyl@Any N-term 42.010565 0.0 0.0 \n", + "Acetyl@Any_N-term 42.010565 0.0 0.0 \n", "... ... ... ... \n", "TMTpro_zero@K 295.189592 0.0 0.0 \n", "TMTpro_zero@T 295.189592 0.0 0.0 \n", @@ -978,7 +978,7 @@ "source": [ "from alphabase.constants.modification import calc_modification_mass\n", "sequence = 'MACDEFG'\n", - "mod_names = ['Acetyl@Any N-term', 'Carbamidomethyl@C']\n", + "mod_names = ['Acetyl@Any_N-term', 'Carbamidomethyl@C']\n", "mod_sites = [0,3]\n", "calc_modification_mass(\n", " nAA=len(sequence),\n", @@ -1013,7 +1013,7 @@ ], "source": [ "sequence = 'MAKDEFG'\n", - "mod_names = ['Acetyl@Any N-term', 'Oxidation@M']\n", + "mod_names = ['Acetyl@Any_N-term', 'Oxidation@M']\n", "mod_sites = [0,1]\n", "calc_modification_mass(\n", " nAA=len(sequence),\n", @@ -1090,8 +1090,8 @@ "calc_mod_masses_for_same_len_seqs(\n", " nAA=7,\n", " mod_names_list=[\n", - " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any N-term', 'Oxidation@M'],\n", + " ['Acetyl@Any_N-term', 'Carbamidomethyl@C'],\n", + " ['Acetyl@Any_N-term', 'Oxidation@M'],\n", " ['GlyGly@K', 'Dimethyl@K'],\n", " ],\n", " mod_sites_list=[\n", @@ -1143,8 +1143,8 @@ "mod_masses = calc_mod_masses_for_same_len_seqs(\n", " nAA=7,\n", " mod_names_list=[\n", - " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any N-term', 'Oxidation@M'],\n", + " ['Acetyl@Any_N-term', 'Carbamidomethyl@C'],\n", + " ['Acetyl@Any_N-term', 'Oxidation@M'],\n", " ['GlyGly@K', 'Dimethyl@K'],\n", " ],\n", " mod_sites_list=[\n", @@ -1225,8 +1225,8 @@ "peptide_masses = calc_peptide_masses_for_same_len_seqs(\n", " ['MACDEFG', 'MAKDEFG', 'MAKDEFR'],\n", " mod_list=[\n", - " 'Acetyl@Any N-term;Carbamidomethyl@C',\n", - " 'Acetyl@Any N-term;Oxidation@M',\n", + " 'Acetyl@Any_N-term;Carbamidomethyl@C',\n", + " 'Acetyl@Any_N-term;Oxidation@M',\n", " 'GlyGly@K;Dimethyl@K',\n", " ],\n", ")\n", @@ -1254,8 +1254,8 @@ "b_masses, y_masses, peptide_masses = calc_b_y_and_peptide_masses_for_same_len_seqs(\n", " ['MACDEFG', 'MAKDEFG', 'MAKDEFR'],\n", " mod_list=[\n", - " ['Acetyl@Any N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any N-term', 'Oxidation@M'],\n", + " ['Acetyl@Any_N-term', 'Carbamidomethyl@C'],\n", + " ['Acetyl@Any_N-term', 'Oxidation@M'],\n", " ['GlyGly@K', 'Dimethyl@K'],\n", " ],\n", " site_list=[\n", From 6f5b05d49d8b7a7abed38539fbc2f5d4f0875f7a Mon Sep 17 00:00:00 2001 From: jalew188 Date: Wed, 17 Jul 2024 16:50:06 +0200 Subject: [PATCH 42/53] Modify descriptions --- docs/nbs/tutorial_dev_basic_definations.ipynb | 1668 ----------------- ...ipynb => tutorial_basic_definations.ipynb} | 317 ++-- ...nb => tutorial_dataframe_structures.ipynb} | 8 +- ...pynb => tutorial_spectral_libraries.ipynb} | 0 4 files changed, 211 insertions(+), 1782 deletions(-) delete mode 100644 docs/nbs/tutorial_dev_basic_definations.ipynb rename docs/tutorials/{basic_definations.ipynb => tutorial_basic_definations.ipynb} (96%) rename docs/tutorials/{dataframe_structures.ipynb => tutorial_dataframe_structures.ipynb} (99%) rename docs/tutorials/{spectral_libraries.ipynb => tutorial_spectral_libraries.ipynb} (100%) diff --git a/docs/nbs/tutorial_dev_basic_definations.ipynb b/docs/nbs/tutorial_dev_basic_definations.ipynb deleted file mode 100644 index 34e02e82..00000000 --- a/docs/nbs/tutorial_dev_basic_definations.ipynb +++ /dev/null @@ -1,1668 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial for Dev: Basic Definations\n", - "\n", - "This notebook introduces low-level functionalities use in AlphaBase to developers." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Atoms/Elements\n", - "\n", - "The masses of all amino acids and modifications are calculated from their atom compositions.\n", - "\n", - "The atom information are defined in https://github.com/MannLabs/alphabase/blob/main/alphabase/constants/const_files/nist_element.yaml which is parsed from NIST, see https://github.com/MannLabs/alphabase/blob/main/nbs/nist_chem_to_yaml.ipynb.\n", - "\n", - "After adding some heavy isotopes, including 13C, 15N, 2H, and 18O, we obtain 109 kinds of atoms:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    abundancemass
    13C[0.01, 0.99][12.0, 13.00335483507]
    14N[0.996337, 0.003663][14.00307400443, 15.00010889888]
    15N[0.01, 0.99][14.00307400443, 15.00010889888]
    18O[0.005, 0.005, 0.99][15.99491461957, 16.9991317565, 17.99915961286]
    2H[0.01, 0.99][1.00782503223, 2.01410177812]
    .........
    Xe[0.000952, 0.00089, 0.019102, 0.264006, 0.0407...[123.905892, 125.9042983, 127.903531, 128.9047...
    Y[1.0][88.9058403]
    Yb[0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0....[167.9338896, 169.9347664, 170.9363302, 171.93...
    Zn[0.4917, 0.2773, 0.0404, 0.1845, 0.0061][63.92914201, 65.92603381, 66.92712775, 67.924...
    Zr[0.5145, 0.1122, 0.1715, 0.1738, 0.028][89.9046977, 90.9056396, 91.9050347, 93.906310...
    \n", - "

    109 rows × 2 columns

    \n", - "
    " - ], - "text/plain": [ - " abundance \\\n", - "13C [0.01, 0.99] \n", - "14N [0.996337, 0.003663] \n", - "15N [0.01, 0.99] \n", - "18O [0.005, 0.005, 0.99] \n", - "2H [0.01, 0.99] \n", - ".. ... \n", - "Xe [0.000952, 0.00089, 0.019102, 0.264006, 0.0407... \n", - "Y [1.0] \n", - "Yb [0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0.... \n", - "Zn [0.4917, 0.2773, 0.0404, 0.1845, 0.0061] \n", - "Zr [0.5145, 0.1122, 0.1715, 0.1738, 0.028] \n", - "\n", - " mass \n", - "13C [12.0, 13.00335483507] \n", - "14N [14.00307400443, 15.00010889888] \n", - "15N [14.00307400443, 15.00010889888] \n", - "18O [15.99491461957, 16.9991317565, 17.99915961286] \n", - "2H [1.00782503223, 2.01410177812] \n", - ".. ... \n", - "Xe [123.905892, 125.9042983, 127.903531, 128.9047... \n", - "Y [88.9058403] \n", - "Yb [167.9338896, 169.9347664, 170.9363302, 171.93... \n", - "Zn [63.92914201, 65.92603381, 66.92712775, 67.924... \n", - "Zr [89.9046977, 90.9056396, 91.9050347, 93.906310... \n", - "\n", - "[109 rows x 2 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "from alphabase.constants.atom import CHEM_INFO_DICT\n", - "pd.DataFrame().from_dict(CHEM_INFO_DICT, orient='index')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And their mono-isotopic mass are in `CHEM_MONO_MASS` (dict):" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    0
    13C13.003355
    14N14.003074
    15N15.000109
    18O17.999160
    2H2.014102
    ......
    Xe131.904155
    Y88.905840
    Yb173.938866
    Zn63.929142
    Zr89.904698
    \n", - "

    109 rows × 1 columns

    \n", - "
    " - ], - "text/plain": [ - " 0\n", - "13C 13.003355\n", - "14N 14.003074\n", - "15N 15.000109\n", - "18O 17.999160\n", - "2H 2.014102\n", - ".. ...\n", - "Xe 131.904155\n", - "Y 88.905840\n", - "Yb 173.938866\n", - "Zn 63.929142\n", - "Zr 89.904698\n", - "\n", - "[109 rows x 1 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.atom import CHEM_MONO_MASS\n", - "pd.DataFrame().from_dict(CHEM_MONO_MASS, orient='index')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These atom masses are used to calculate the masses of amino acids, modifications, and then subsequent masses of peptides and fragments." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Commonly used molecular masses" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.007276467, 1.0033, 17.02654910112, 18.01056468403)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.atom import (\n", - " MASS_PROTON, MASS_ISOTOPE, MASS_NH3, MASS_H2O\n", - ")\n", - "MASS_PROTON, MASS_ISOTOPE, MASS_NH3, MASS_H2O" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Amino Acids" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    aaformulamass
    65AC(3)H(5)N(1)O(1)S(0)7.103711e+01
    66BC(1000000)1.200000e+07
    67CC(3)H(5)N(1)O(1)S(1)1.030092e+02
    68DC(4)H(5)N(1)O(3)S(0)1.150269e+02
    69EC(5)H(7)N(1)O(3)S(0)1.290426e+02
    70FC(9)H(9)N(1)O(1)S(0)1.470684e+02
    71GC(2)H(3)N(1)O(1)S(0)5.702146e+01
    72HC(6)H(7)N(3)O(1)S(0)1.370589e+02
    73IC(6)H(11)N(1)O(1)S(0)1.130841e+02
    74JC(6)H(11)N(1)O(1)S(0)1.130841e+02
    75KC(6)H(12)N(2)O(1)S(0)1.280950e+02
    76LC(6)H(11)N(1)O(1)S(0)1.130841e+02
    77MC(5)H(9)N(1)O(1)S(1)1.310405e+02
    78NC(4)H(6)N(2)O(2)S(0)1.140429e+02
    79OC(12)H(19)N(3)O(2)2.371477e+02
    80PC(5)H(7)N(1)O(1)S(0)9.705276e+01
    81QC(5)H(8)N(2)O(2)S(0)1.280586e+02
    82RC(6)H(12)N(4)O(1)S(0)1.561011e+02
    83SC(3)H(5)N(1)O(2)S(0)8.703203e+01
    84TC(4)H(7)N(1)O(2)S(0)1.010477e+02
    85UC(3)H(5)N(1)O(1)Se(1)1.509536e+02
    86VC(5)H(9)N(1)O(1)S(0)9.906841e+01
    87WC(11)H(10)N(2)O(1)S(0)1.860793e+02
    88XC(1000000)1.200000e+07
    89YC(9)H(9)N(1)O(2)S(0)1.630633e+02
    90ZC(1000000)1.200000e+07
    \n", - "
    " - ], - "text/plain": [ - " aa formula mass\n", - "65 A C(3)H(5)N(1)O(1)S(0) 7.103711e+01\n", - "66 B C(1000000) 1.200000e+07\n", - "67 C C(3)H(5)N(1)O(1)S(1) 1.030092e+02\n", - "68 D C(4)H(5)N(1)O(3)S(0) 1.150269e+02\n", - "69 E C(5)H(7)N(1)O(3)S(0) 1.290426e+02\n", - "70 F C(9)H(9)N(1)O(1)S(0) 1.470684e+02\n", - "71 G C(2)H(3)N(1)O(1)S(0) 5.702146e+01\n", - "72 H C(6)H(7)N(3)O(1)S(0) 1.370589e+02\n", - "73 I C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", - "74 J C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", - "75 K C(6)H(12)N(2)O(1)S(0) 1.280950e+02\n", - "76 L C(6)H(11)N(1)O(1)S(0) 1.130841e+02\n", - "77 M C(5)H(9)N(1)O(1)S(1) 1.310405e+02\n", - "78 N C(4)H(6)N(2)O(2)S(0) 1.140429e+02\n", - "79 O C(12)H(19)N(3)O(2) 2.371477e+02\n", - "80 P C(5)H(7)N(1)O(1)S(0) 9.705276e+01\n", - "81 Q C(5)H(8)N(2)O(2)S(0) 1.280586e+02\n", - "82 R C(6)H(12)N(4)O(1)S(0) 1.561011e+02\n", - "83 S C(3)H(5)N(1)O(2)S(0) 8.703203e+01\n", - "84 T C(4)H(7)N(1)O(2)S(0) 1.010477e+02\n", - "85 U C(3)H(5)N(1)O(1)Se(1) 1.509536e+02\n", - "86 V C(5)H(9)N(1)O(1)S(0) 9.906841e+01\n", - "87 W C(11)H(10)N(2)O(1)S(0) 1.860793e+02\n", - "88 X C(1000000) 1.200000e+07\n", - "89 Y C(9)H(9)N(1)O(2)S(0) 1.630633e+02\n", - "90 Z C(1000000) 1.200000e+07" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.aa import AA_DF\n", - "AA_DF.loc[ord('A'):ord('Z')]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From `AA_DF`, we can see that amino acids are encoded by ASCII (128 characters). 65==ord('A'), ..., 90==ord('Z'). Unicode strings can be fastly converted to ascii int32 values using numpy:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([65, 66, 67, 88, 89, 90], dtype=int32)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "np.array(['ABCXYZ']).view(np.int32)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But users does not need to know this, as we provided easy to use functionalities to get residue masses from sequences." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate AA masses in batch" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[131.04048509, 71.03711379, 103.00918496, 115.02694302,\n", - " 129.04259309, 147.06841391, 57.02146372],\n", - " [131.04048509, 71.03711379, 128.09496302, 115.02694302,\n", - " 129.04259309, 147.06841391, 57.02146372],\n", - " [131.04048509, 71.03711379, 128.09496302, 115.02694302,\n", - " 129.04259309, 147.06841391, 156.10111102]])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.aa import calc_AA_masses_for_same_len_seqs\n", - "calc_AA_masses_for_same_len_seqs(\n", - " [\n", - " 'MACDEFG', 'MAKDEFG', 'MAKDEFR'\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Modifications\n", - "\n", - "In AlphaBase, we used `mod_name@aa` to represent a modification, the `mod_name` is from UniMod. We also used `mod_name@Protein_N-term`, `mod_name@Any_N-term` and `mod_name@Any_C-term` for terminal modifications, which follow the UniMod terminal name schema.\n", - "\n", - "The default modification TSV is stored in `alphabase/constants/const_files/modification.tsv`, users can add more modifications into the tsv file (only `mod_name` and `composition` colums are required). Please https://github.com/MannLabs/alphabase/blob/main/alphabase/constants/const_files/modification.tsv." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    mod_nameunimod_massunimod_avge_masscompositionunimod_modlossmodloss_compositionclassificationunimod_idmodloss_importancemassmodloss_originalmodloss
    mod_name
    Acetyl@TAcetyl@T42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
    Acetyl@Protein_N-termAcetyl@Protein_N-term42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
    Acetyl@SAcetyl@S42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
    Acetyl@CAcetyl@C42.01056542.0367H(2)C(2)O(1)0.0Post-translational10.042.0105650.00.0
    Acetyl@Any_N-termAcetyl@Any_N-term42.01056542.0367H(2)C(2)O(1)0.0Multiple10.042.0105650.00.0
    .......................................
    TMTpro_zero@KTMTpro_zero@K295.189592295.3773H(25)C(15)N(3)O(3)0.0Chemical derivative20170.0295.1895920.00.0
    TMTpro_zero@TTMTpro_zero@T295.189592295.3773H(25)C(15)N(3)O(3)0.0Chemical derivative20170.0295.1895920.00.0
    Andro-H2O@CAndro-H2O@C332.198760332.4339H(28)C(20)O(4)0.0Chemical derivative20250.0332.1987590.00.0
    His+O(2)@HHis+O(2)@H169.048741169.1381H(7)C(6)N(3)O(3)0.0Post-translational20270.0169.0487410.00.0
    GlyGly@KGlyGly@K114.042927114.1026H(6)C(4)N(2)O(2)0.0Post-translational1211000000.0114.0429270.00.0
    \n", - "

    2685 rows × 12 columns

    \n", - "
    " - ], - "text/plain": [ - " mod_name unimod_mass unimod_avge_mass \\\n", - "mod_name \n", - "Acetyl@T Acetyl@T 42.010565 42.0367 \n", - "Acetyl@Protein_N-term Acetyl@Protein_N-term 42.010565 42.0367 \n", - "Acetyl@S Acetyl@S 42.010565 42.0367 \n", - "Acetyl@C Acetyl@C 42.010565 42.0367 \n", - "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 42.0367 \n", - "... ... ... ... \n", - "TMTpro_zero@K TMTpro_zero@K 295.189592 295.3773 \n", - "TMTpro_zero@T TMTpro_zero@T 295.189592 295.3773 \n", - "Andro-H2O@C Andro-H2O@C 332.198760 332.4339 \n", - "His+O(2)@H His+O(2)@H 169.048741 169.1381 \n", - "GlyGly@K GlyGly@K 114.042927 114.1026 \n", - "\n", - " composition unimod_modloss modloss_composition \\\n", - "mod_name \n", - "Acetyl@T H(2)C(2)O(1) 0.0 \n", - "Acetyl@Protein_N-term H(2)C(2)O(1) 0.0 \n", - "Acetyl@S H(2)C(2)O(1) 0.0 \n", - "Acetyl@C H(2)C(2)O(1) 0.0 \n", - "Acetyl@Any_N-term H(2)C(2)O(1) 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K H(25)C(15)N(3)O(3) 0.0 \n", - "TMTpro_zero@T H(25)C(15)N(3)O(3) 0.0 \n", - "Andro-H2O@C H(28)C(20)O(4) 0.0 \n", - "His+O(2)@H H(7)C(6)N(3)O(3) 0.0 \n", - "GlyGly@K H(6)C(4)N(2)O(2) 0.0 \n", - "\n", - " classification unimod_id modloss_importance \\\n", - "mod_name \n", - "Acetyl@T Post-translational 1 0.0 \n", - "Acetyl@Protein_N-term Post-translational 1 0.0 \n", - "Acetyl@S Post-translational 1 0.0 \n", - "Acetyl@C Post-translational 1 0.0 \n", - "Acetyl@Any_N-term Multiple 1 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K Chemical derivative 2017 0.0 \n", - "TMTpro_zero@T Chemical derivative 2017 0.0 \n", - "Andro-H2O@C Chemical derivative 2025 0.0 \n", - "His+O(2)@H Post-translational 2027 0.0 \n", - "GlyGly@K Post-translational 121 1000000.0 \n", - "\n", - " mass modloss_original modloss \n", - "mod_name \n", - "Acetyl@T 42.010565 0.0 0.0 \n", - "Acetyl@Protein_N-term 42.010565 0.0 0.0 \n", - "Acetyl@S 42.010565 0.0 0.0 \n", - "Acetyl@C 42.010565 0.0 0.0 \n", - "Acetyl@Any_N-term 42.010565 0.0 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K 295.189592 0.0 0.0 \n", - "TMTpro_zero@T 295.189592 0.0 0.0 \n", - "Andro-H2O@C 332.198759 0.0 0.0 \n", - "His+O(2)@H 169.048741 0.0 0.0 \n", - "GlyGly@K 114.042927 0.0 0.0 \n", - "\n", - "[2685 rows x 12 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.modification import MOD_DF\n", - "MOD_DF" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Modification sites\n", - "\n", - "In alphabase, we use 0 and -1 to represent modification site of N-term and C-term, respectively. For other modification sites, we use 1 to n." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([42.01056468, 0. , 57.02146372, 0. , 0. ,\n", - " 0. , 0. ])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.modification import calc_modification_mass\n", - "sequence = 'MACDEFG'\n", - "mod_names = ['Acetyl@Any_N-term', 'Carbamidomethyl@C']\n", - "mod_sites = [0,3]\n", - "calc_modification_mass(\n", - " nAA=len(sequence),\n", - " mod_names=mod_names,\n", - " mod_sites=mod_sites\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The modifications on the first amino acid and N-term will be added." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([58.0054793, 0. , 0. , 0. , 0. ,\n", - " 0. , 0. ])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sequence = 'MAKDEFG'\n", - "mod_names = ['Acetyl@Any_N-term', 'Oxidation@M']\n", - "mod_sites = [0,1]\n", - "calc_modification_mass(\n", - " nAA=len(sequence),\n", - " mod_names=mod_names,\n", - " mod_sites=mod_sites\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Multiple modification at a single site is supported, for example, in the following example, `K3` contains both `GlyGly@K` and `Dimethyl@K`:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 0. , 142.07422757, 0. ,\n", - " 0. , 0. , 0. ])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sequence = 'MAKDEFR'\n", - "mod_names = ['GlyGly@K', 'Dimethyl@K']\n", - "mod_sites = [3,3]\n", - "calc_modification_mass(\n", - " nAA=len(sequence),\n", - " mod_names=mod_names,\n", - " mod_sites=mod_sites\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Caculate modification masses in batch" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 42.01056468, 0. , 57.02146372, 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 58.0054793 , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , 142.07422757, 0. ,\n", - " 0. , 0. , 0. ]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.modification import calc_mod_masses_for_same_len_seqs\n", - "calc_mod_masses_for_same_len_seqs(\n", - " nAA=7,\n", - " mod_names_list=[\n", - " ['Acetyl@Any_N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any_N-term', 'Oxidation@M'],\n", - " ['GlyGly@K', 'Dimethyl@K'],\n", - " ],\n", - " mod_sites_list=[\n", - " [0, 3],\n", - " [0, 1],\n", - " [3, 3],\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mass calculation functionalities" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate AA and modification masses in batch" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[173.05104977, 71.03711379, 160.03064868, 115.02694302,\n", - " 129.04259309, 147.06841391, 57.02146372],\n", - " [189.04596439, 71.03711379, 128.09496302, 115.02694302,\n", - " 129.04259309, 147.06841391, 57.02146372],\n", - " [131.04048509, 71.03711379, 270.16919059, 115.02694302,\n", - " 129.04259309, 147.06841391, 156.10111102]])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.aa import calc_AA_masses_for_same_len_seqs\n", - "from alphabase.constants.modification import calc_mod_masses_for_same_len_seqs\n", - "mod_masses = calc_mod_masses_for_same_len_seqs(\n", - " nAA=7,\n", - " mod_names_list=[\n", - " ['Acetyl@Any_N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any_N-term', 'Oxidation@M'],\n", - " ['GlyGly@K', 'Dimethyl@K'],\n", - " ],\n", - " mod_sites_list=[\n", - " [0, 3],\n", - " [0, 1],\n", - " [3, 3],\n", - " ]\n", - ")\n", - "aa_masses = calc_AA_masses_for_same_len_seqs(\n", - " [\n", - " 'MACDEFG', 'MAKDEFG', 'MAKDEFR'\n", - " ]\n", - ")\n", - "mod_masses+aa_masses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### np.cumsum to get b-ion neutral masses" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 173.05104977, 244.08816356, 404.11881224, 519.14575526,\n", - " 648.18834835, 795.25676227, 852.27822599],\n", - " [ 189.04596439, 260.08307818, 388.17804119, 503.20498422,\n", - " 632.24757731, 779.31599122, 836.33745494],\n", - " [ 131.04048509, 202.07759887, 472.24678946, 587.27373248,\n", - " 716.31632557, 863.38473949, 1019.48585051]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "np.cumsum(aa_masses+mod_masses, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Mass functionalities in 'mass_calc'\n", - "\n", - "The functionalities for peptide and fragment neutral masses have been implement in `alphabase.peptide.mass_calc`:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 870.28879067, 854.34801962, 1037.49641519])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.peptide.mass_calc import calc_peptide_masses_for_same_len_seqs\n", - "\n", - "peptide_masses = calc_peptide_masses_for_same_len_seqs(\n", - " ['MACDEFG', 'MAKDEFG', 'MAKDEFR'],\n", - " mod_list=[\n", - " 'Acetyl@Any_N-term;Carbamidomethyl@C',\n", - " 'Acetyl@Any_N-term;Oxidation@M',\n", - " 'GlyGly@K;Dimethyl@K',\n", - " ],\n", - ")\n", - "peptide_masses" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 870.28879067, 854.34801962, 1037.49641519])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.peptide.mass_calc import calc_b_y_and_peptide_masses_for_same_len_seqs\n", - "b_masses, y_masses, peptide_masses = calc_b_y_and_peptide_masses_for_same_len_seqs(\n", - " ['MACDEFG', 'MAKDEFG', 'MAKDEFR'],\n", - " mod_list=[\n", - " ['Acetyl@Any_N-term', 'Carbamidomethyl@C'],\n", - " ['Acetyl@Any_N-term', 'Oxidation@M'],\n", - " ['GlyGly@K', 'Dimethyl@K'],\n", - " ],\n", - " site_list=[\n", - " [0, 3],\n", - " [0, 1],\n", - " [3, 3],\n", - " ],\n", - ")\n", - "peptide_masses" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[173.05104977, 244.08816356, 404.11881224, 519.14575526,\n", - " 648.18834835, 795.25676227],\n", - " [189.04596439, 260.08307818, 388.17804119, 503.20498422,\n", - " 632.24757731, 779.31599122],\n", - " [131.04048509, 202.07759887, 472.24678946, 587.27373248,\n", - " 716.31632557, 863.38473949]])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b_masses" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[697.2377409 , 626.20062711, 466.16997843, 351.14303541,\n", - " 222.10044232, 75.0320284 ],\n", - " [665.30205523, 594.26494145, 466.16997843, 351.14303541,\n", - " 222.10044232, 75.0320284 ],\n", - " [906.45593011, 835.41881632, 565.24962574, 450.22268271,\n", - " 321.18008962, 174.11167571]])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_masses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Isotope distribution\n", - "\n", - "`alphabase.constants.isotope.IsotopeDistribution` will calculate the isotope distribution and the mono-isotopic idx in the distribution for a given atom composition. \n", - "\n", - "What is the mono-isotopic idx (mono_idx)? For an atom, the `mono_idx` points to the highest abundance isotope, so the value is `round(mass of highest isotope - mass of first isotope)`." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    abundancemassmono_idx
    13C[0.01, 0.99][12.0, 13.00335483507]1
    14N[0.996337, 0.003663][14.00307400443, 15.00010889888]0
    15N[0.01, 0.99][14.00307400443, 15.00010889888]1
    18O[0.005, 0.005, 0.99][15.99491461957, 16.9991317565, 17.99915961286]2
    2H[0.01, 0.99][1.00782503223, 2.01410177812]1
    ............
    Xe[0.000952, 0.00089, 0.019102, 0.264006, 0.0407...[123.905892, 125.9042983, 127.903531, 128.9047...8
    Y[1.0][88.9058403]0
    Yb[0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0....[167.9338896, 169.9347664, 170.9363302, 171.93...6
    Zn[0.4917, 0.2773, 0.0404, 0.1845, 0.0061][63.92914201, 65.92603381, 66.92712775, 67.924...0
    Zr[0.5145, 0.1122, 0.1715, 0.1738, 0.028][89.9046977, 90.9056396, 91.9050347, 93.906310...0
    \n", - "

    109 rows × 3 columns

    \n", - "
    " - ], - "text/plain": [ - " abundance \\\n", - "13C [0.01, 0.99] \n", - "14N [0.996337, 0.003663] \n", - "15N [0.01, 0.99] \n", - "18O [0.005, 0.005, 0.99] \n", - "2H [0.01, 0.99] \n", - ".. ... \n", - "Xe [0.000952, 0.00089, 0.019102, 0.264006, 0.0407... \n", - "Y [1.0] \n", - "Yb [0.00123, 0.02982, 0.1409, 0.2168, 0.16103, 0.... \n", - "Zn [0.4917, 0.2773, 0.0404, 0.1845, 0.0061] \n", - "Zr [0.5145, 0.1122, 0.1715, 0.1738, 0.028] \n", - "\n", - " mass mono_idx \n", - "13C [12.0, 13.00335483507] 1 \n", - "14N [14.00307400443, 15.00010889888] 0 \n", - "15N [14.00307400443, 15.00010889888] 1 \n", - "18O [15.99491461957, 16.9991317565, 17.99915961286] 2 \n", - "2H [1.00782503223, 2.01410177812] 1 \n", - ".. ... ... \n", - "Xe [123.905892, 125.9042983, 127.903531, 128.9047... 8 \n", - "Y [88.9058403] 0 \n", - "Yb [167.9338896, 169.9347664, 170.9363302, 171.93... 6 \n", - "Zn [63.92914201, 65.92603381, 66.92712775, 67.924... 0 \n", - "Zr [89.9046977, 90.9056396, 91.9050347, 93.906310... 0 \n", - "\n", - "[109 rows x 3 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "from alphabase.constants.atom import CHEM_INFO_DICT\n", - "atom_df = pd.DataFrame().from_dict(CHEM_INFO_DICT, orient='index')\n", - "def get_mono(masses_abundances):\n", - " masses, abundances = masses_abundances\n", - " return round(masses[np.argmax(abundances)]-masses[0])\n", - "atom_df['mono_idx'] = atom_df[['mass','abundance']].apply(\n", - " get_mono, axis=1\n", - ")\n", - "atom_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`mono_idx` of an atom composition refers to the sum of the `mono_idx` of all atoms. In AlphaBase, `alphabase.constants.isotope.IsotopeDistribution` calculate both isotope abundance and `mono_idx`. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For example, `Fe`'s `mono_idx` is 2," - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "abundance [0.05845, 0.91754, 0.02119, 0.00282]\n", - "mass [53.93960899, 55.93493633, 56.93539284, 57.933...\n", - "mono_idx 2\n", - "Name: Fe, dtype: object" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "atom_df.loc['Fe']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So `C(1)Fe(1)`'s `mono_idx` is also 2:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([5.78245850e-02, 6.25415000e-04, 9.07722322e-01, 3.07809450e-02,\n", - " 3.01655900e-03, 3.01740000e-05, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00]),\n", - " 2)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.isotope import IsotopeDistribution, parse_formula\n", - "iso = IsotopeDistribution()\n", - "iso.calc_formula_distribution(\n", - " [('C',1),('Fe',1)]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But `13C(1)Fe(1)`'s `mono_idx` should be 3:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([5.845000e-04, 5.786550e-02, 9.175400e-03, 9.085765e-01,\n", - " 2.100630e-02, 2.791800e-03, 0.000000e+00, 0.000000e+00,\n", - " 0.000000e+00, 0.000000e+00]),\n", - " 3)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iso.calc_formula_distribution(\n", - " [('13C',1),('Fe',1)]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `mono_idx` for most of the atom compositions is 0, no matter how big the compositions are." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('C', 100), ('H', 100), ('O', 50), ('Na', 1)]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from alphabase.constants.isotope import IsotopeDistribution, parse_formula\n", - "iso = IsotopeDistribution()\n", - "\n", - "formula = 'C(100)H(100)O(50)Na(1)'\n", - "formula = parse_formula(formula)\n", - "formula" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> `mono` isotope is not the `highest` isotope!!!" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0,\n", - " 1,\n", - " array([2.98521241e-01, 3.31991573e-01, 2.13532938e-01, 1.00604878e-01,\n", - " 3.82856126e-02, 1.23872292e-02, 3.51773755e-03, 8.95830236e-04,\n", - " 2.07763024e-04, 4.43944472e-05]))" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dist, mono = iso.calc_formula_distribution(formula)\n", - "mono, dist.argmax(), dist" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All these low-level functionalities have been integrated into DataFrame functionalities, see `tutorial_dev_dataframes.ipynb` or `Tutorial for Dev: Peptide and Fragment DataFrames`" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.3 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8a3b27e141e49c996c9b863f8707e97aabd49c4a7e8445b9b783b34e4a21a9b2" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/tutorials/basic_definations.ipynb b/docs/tutorials/tutorial_basic_definations.ipynb similarity index 96% rename from docs/tutorials/basic_definations.ipynb rename to docs/tutorials/tutorial_basic_definations.ipynb index a7fe1ba8..bd7f14dc 100644 --- a/docs/tutorials/basic_definations.ipynb +++ b/docs/tutorials/tutorial_basic_definations.ipynb @@ -6,11 +6,11 @@ "source": [ "# Tutorial: Basic Definations and Settings\n", "\n", - "Measuring m/z values is the very elemental function of MS technologies, therefore the calculation of mass values for a peptide and its fragments becomes the most essential part in MS-based computational tools. AlphaBase calculates all mass values from atoms. And the masses of amino acids and modifications are calculated from their atom compositions, repectively. Eventually, the masses of peptides or precursors as well as their fragments can be calculated by the amino acid sequences with or without modifications (See figure below).\n", + "Measuring m/z values is the elemental function of MS technologies, therefore the calculation of mass values for a peptide and its fragments becomes the most essential part in MS-based computational tools. AlphaBase calculates all mass values from atoms. And the masses of amino acids and modifications are calculated from their atom compositions, repectively. Eventually, the masses of peptides or precursors as well as their fragments can be calculated by the amino acid sequences with or without modifications (See figure below).\n", "\n", "Calculating masses from atoms makes it much easier to switch between unlabeled and heavy-labeled peptides, as we did in Steller MS for 15N-labeled peptides as the reference for targeted proteomics (https://www.biorxiv.org/content/10.1101/2024.06.02.597029v2.full).\n", "\n", - "The other advantage of starting from atoms is that AlphaBase can calculate isotope distributions of peptides based on a pre-defined isotope distribution list of atoms (e.g., NIST atom table in https://physics.nist.gov/cgi-bin/Compositions/stand_alone.pl). The isotope information has been applied in our AlphaDIA search engine to boost the identification of DIA-MS data (https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1)." + "The other advantage of starting from atoms is that AlphaBase can calculate isotope distributions of peptides based on a pre-defined isotope distribution list of atoms (e.g., NIST atom table in https://physics.nist.gov/cgi-bin/Compositions/stand_alone.pl). The isotope information has been applied in our alphaDIA search engine to boost the identification of DIA-MS data (https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1)." ] }, { @@ -687,6 +687,7 @@ " modloss_composition\n", " classification\n", " unimod_id\n", + " smiles\n", " modloss_importance\n", " mass\n", " modloss_original\n", @@ -706,6 +707,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -719,6 +721,7 @@ " \n", " Post-translational\n", " 1\n", + " \n", " 0.0\n", " 42.010565\n", " 0.0\n", @@ -734,6 +737,7 @@ " \n", " Post-translational\n", " 1\n", + " \n", " 0.0\n", " 42.010565\n", " 0.0\n", @@ -749,6 +753,7 @@ " \n", " Post-translational\n", " 1\n", + " \n", " 0.0\n", " 42.010565\n", " 0.0\n", @@ -764,6 +769,7 @@ " \n", " Post-translational\n", " 1\n", + " \n", " 0.0\n", " 42.010565\n", " 0.0\n", @@ -779,6 +785,7 @@ " \n", " Multiple\n", " 1\n", + " \n", " 0.0\n", " 42.010565\n", " 0.0\n", @@ -798,64 +805,69 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " TMTpro_zero@K\n", - " TMTpro_zero@K\n", - " 295.189592\n", - " 295.3773\n", - " H(25)C(15)N(3)O(3)\n", + " NQTGG@K\n", + " NQTGG@K\n", + " 457.192111\n", + " 457.4384\n", + " H(27)C(17)N(7)O(8)\n", " 0.0\n", " \n", - " Chemical derivative\n", - " 2017\n", + " Other\n", + " 2084\n", + " \n", " 0.0\n", - " 295.189592\n", + " 457.192111\n", " 0.0\n", " 0.0\n", " \n", " \n", - " TMTpro_zero@T\n", - " TMTpro_zero@T\n", - " 295.189592\n", - " 295.3773\n", - " H(25)C(15)N(3)O(3)\n", + " DVFQQQTGG@K\n", + " DVFQQQTGG@K\n", + " 960.430110\n", + " 960.9865\n", + " H(60)C(41)N(12)O(15)\n", " 0.0\n", " \n", - " Chemical derivative\n", - " 2017\n", + " Other\n", + " 2085\n", + " \n", " 0.0\n", - " 295.189592\n", + " 960.430109\n", " 0.0\n", " 0.0\n", " \n", " \n", - " Andro-H2O@C\n", - " Andro-H2O@C\n", - " 332.198760\n", - " 332.4339\n", - " H(28)C(20)O(4)\n", + " iST-NHS_specific_cysteine_modification@C\n", + " iST-NHS_specific_cysteine_modification@C\n", + " 113.084064\n", + " 113.1576\n", + " H(11)C(6)N(1)O(1)\n", " 0.0\n", " \n", " Chemical derivative\n", - " 2025\n", + " 2086\n", + " \n", " 0.0\n", - " 332.198759\n", + " 113.084064\n", " 0.0\n", " 0.0\n", " \n", " \n", - " His+O(2)@H\n", - " His+O(2)@H\n", - " 169.048741\n", - " 169.1381\n", - " H(7)C(6)N(3)O(3)\n", + " Label:13C(2)15N(1)@G\n", + " Label:13C(2)15N(1)@G\n", + " 3.003745\n", + " 2.9787\n", + " C(-2)13C(2)N(-1)15N(1)\n", " 0.0\n", " \n", - " Post-translational\n", - " 2027\n", + " Isotopic label\n", + " 2088\n", + " \n", " 0.0\n", - " 169.048741\n", + " 3.003745\n", " 0.0\n", " 0.0\n", " \n", @@ -869,6 +881,7 @@ " \n", " Multiple\n", " 121\n", + " \n", " 1000000.0\n", " 114.042927\n", " 0.0\n", @@ -876,67 +889,123 @@ " \n", " \n", "\n", - "

    2685 rows × 12 columns

    \n", + "

    2772 rows × 13 columns

    \n", "" ], "text/plain": [ - " mod_name unimod_mass unimod_avge_mass \\\n", - "mod_name \n", - "Acetyl@T Acetyl@T 42.010565 42.0367 \n", - "Acetyl@Protein_N-term Acetyl@Protein_N-term 42.010565 42.0367 \n", - "Acetyl@S Acetyl@S 42.010565 42.0367 \n", - "Acetyl@C Acetyl@C 42.010565 42.0367 \n", - "Acetyl@Any_N-term Acetyl@Any_N-term 42.010565 42.0367 \n", - "... ... ... ... \n", - "TMTpro_zero@K TMTpro_zero@K 295.189592 295.3773 \n", - "TMTpro_zero@T TMTpro_zero@T 295.189592 295.3773 \n", - "Andro-H2O@C Andro-H2O@C 332.198760 332.4339 \n", - "His+O(2)@H His+O(2)@H 169.048741 169.1381 \n", - "GlyGly@K GlyGly@K 114.042927 114.1026 \n", + " mod_name \\\n", + "mod_name \n", + "Acetyl@T Acetyl@T \n", + "Acetyl@Protein_N-term Acetyl@Protein_N-term \n", + "Acetyl@S Acetyl@S \n", + "Acetyl@C Acetyl@C \n", + "Acetyl@Any_N-term Acetyl@Any_N-term \n", + "... ... \n", + "NQTGG@K NQTGG@K \n", + "DVFQQQTGG@K DVFQQQTGG@K \n", + "iST-NHS_specific_cysteine_modification@C iST-NHS_specific_cysteine_modification@C \n", + "Label:13C(2)15N(1)@G Label:13C(2)15N(1)@G \n", + "GlyGly@K GlyGly@K \n", + "\n", + " unimod_mass unimod_avge_mass \\\n", + "mod_name \n", + "Acetyl@T 42.010565 42.0367 \n", + "Acetyl@Protein_N-term 42.010565 42.0367 \n", + "Acetyl@S 42.010565 42.0367 \n", + "Acetyl@C 42.010565 42.0367 \n", + "Acetyl@Any_N-term 42.010565 42.0367 \n", + "... ... ... \n", + "NQTGG@K 457.192111 457.4384 \n", + "DVFQQQTGG@K 960.430110 960.9865 \n", + "iST-NHS_specific_cysteine_modification@C 113.084064 113.1576 \n", + "Label:13C(2)15N(1)@G 3.003745 2.9787 \n", + "GlyGly@K 114.042927 114.1026 \n", + "\n", + " composition \\\n", + "mod_name \n", + "Acetyl@T H(2)C(2)O(1) \n", + "Acetyl@Protein_N-term H(2)C(2)O(1) \n", + "Acetyl@S H(2)C(2)O(1) \n", + "Acetyl@C H(2)C(2)O(1) \n", + "Acetyl@Any_N-term H(2)C(2)O(1) \n", + "... ... \n", + "NQTGG@K H(27)C(17)N(7)O(8) \n", + "DVFQQQTGG@K H(60)C(41)N(12)O(15) \n", + "iST-NHS_specific_cysteine_modification@C H(11)C(6)N(1)O(1) \n", + "Label:13C(2)15N(1)@G C(-2)13C(2)N(-1)15N(1) \n", + "GlyGly@K H(6)C(4)N(2)O(2) \n", + "\n", + " unimod_modloss modloss_composition \\\n", + "mod_name \n", + "Acetyl@T 0.0 \n", + "Acetyl@Protein_N-term 0.0 \n", + "Acetyl@S 0.0 \n", + "Acetyl@C 0.0 \n", + "Acetyl@Any_N-term 0.0 \n", + "... ... ... \n", + "NQTGG@K 0.0 \n", + "DVFQQQTGG@K 0.0 \n", + "iST-NHS_specific_cysteine_modification@C 0.0 \n", + "Label:13C(2)15N(1)@G 0.0 \n", + "GlyGly@K 0.0 \n", + "\n", + " classification unimod_id \\\n", + "mod_name \n", + "Acetyl@T Post-translational 1 \n", + "Acetyl@Protein_N-term Post-translational 1 \n", + "Acetyl@S Post-translational 1 \n", + "Acetyl@C Post-translational 1 \n", + "Acetyl@Any_N-term Multiple 1 \n", + "... ... ... \n", + "NQTGG@K Other 2084 \n", + "DVFQQQTGG@K Other 2085 \n", + "iST-NHS_specific_cysteine_modification@C Chemical derivative 2086 \n", + "Label:13C(2)15N(1)@G Isotopic label 2088 \n", + "GlyGly@K Multiple 121 \n", "\n", - " composition unimod_modloss modloss_composition \\\n", - "mod_name \n", - "Acetyl@T H(2)C(2)O(1) 0.0 \n", - "Acetyl@Protein_N-term H(2)C(2)O(1) 0.0 \n", - "Acetyl@S H(2)C(2)O(1) 0.0 \n", - "Acetyl@C H(2)C(2)O(1) 0.0 \n", - "Acetyl@Any_N-term H(2)C(2)O(1) 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K H(25)C(15)N(3)O(3) 0.0 \n", - "TMTpro_zero@T H(25)C(15)N(3)O(3) 0.0 \n", - "Andro-H2O@C H(28)C(20)O(4) 0.0 \n", - "His+O(2)@H H(7)C(6)N(3)O(3) 0.0 \n", - "GlyGly@K H(6)C(4)N(2)O(2) 0.0 \n", + " smiles modloss_importance \\\n", + "mod_name \n", + "Acetyl@T 0.0 \n", + "Acetyl@Protein_N-term 0.0 \n", + "Acetyl@S 0.0 \n", + "Acetyl@C 0.0 \n", + "Acetyl@Any_N-term 0.0 \n", + "... ... ... \n", + "NQTGG@K 0.0 \n", + "DVFQQQTGG@K 0.0 \n", + "iST-NHS_specific_cysteine_modification@C 0.0 \n", + "Label:13C(2)15N(1)@G 0.0 \n", + "GlyGly@K 1000000.0 \n", "\n", - " classification unimod_id modloss_importance \\\n", - "mod_name \n", - "Acetyl@T Post-translational 1 0.0 \n", - "Acetyl@Protein_N-term Post-translational 1 0.0 \n", - "Acetyl@S Post-translational 1 0.0 \n", - "Acetyl@C Post-translational 1 0.0 \n", - "Acetyl@Any_N-term Multiple 1 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K Chemical derivative 2017 0.0 \n", - "TMTpro_zero@T Chemical derivative 2017 0.0 \n", - "Andro-H2O@C Chemical derivative 2025 0.0 \n", - "His+O(2)@H Post-translational 2027 0.0 \n", - "GlyGly@K Multiple 121 1000000.0 \n", + " mass modloss_original \\\n", + "mod_name \n", + "Acetyl@T 42.010565 0.0 \n", + "Acetyl@Protein_N-term 42.010565 0.0 \n", + "Acetyl@S 42.010565 0.0 \n", + "Acetyl@C 42.010565 0.0 \n", + "Acetyl@Any_N-term 42.010565 0.0 \n", + "... ... ... \n", + "NQTGG@K 457.192111 0.0 \n", + "DVFQQQTGG@K 960.430109 0.0 \n", + "iST-NHS_specific_cysteine_modification@C 113.084064 0.0 \n", + "Label:13C(2)15N(1)@G 3.003745 0.0 \n", + "GlyGly@K 114.042927 0.0 \n", "\n", - " mass modloss_original modloss \n", - "mod_name \n", - "Acetyl@T 42.010565 0.0 0.0 \n", - "Acetyl@Protein_N-term 42.010565 0.0 0.0 \n", - "Acetyl@S 42.010565 0.0 0.0 \n", - "Acetyl@C 42.010565 0.0 0.0 \n", - "Acetyl@Any_N-term 42.010565 0.0 0.0 \n", - "... ... ... ... \n", - "TMTpro_zero@K 295.189592 0.0 0.0 \n", - "TMTpro_zero@T 295.189592 0.0 0.0 \n", - "Andro-H2O@C 332.198759 0.0 0.0 \n", - "His+O(2)@H 169.048741 0.0 0.0 \n", - "GlyGly@K 114.042927 0.0 0.0 \n", + " modloss \n", + "mod_name \n", + "Acetyl@T 0.0 \n", + "Acetyl@Protein_N-term 0.0 \n", + "Acetyl@S 0.0 \n", + "Acetyl@C 0.0 \n", + "Acetyl@Any_N-term 0.0 \n", + "... ... \n", + "NQTGG@K 0.0 \n", + "DVFQQQTGG@K 0.0 \n", + "iST-NHS_specific_cysteine_modification@C 0.0 \n", + "Label:13C(2)15N(1)@G 0.0 \n", + "GlyGly@K 0.0 \n", "\n", - "[2685 rows x 12 columns]" + "[2772 rows x 13 columns]" ] }, "execution_count": 8, @@ -1026,7 +1095,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Multiple modification at a single site is supported, for example, in the following example, `K3` contains both `GlyGly@K` and `Dimethyl@K`:" + "Multiple modifications at a single site is supported, for example, in the following example, `K3` contains both `GlyGly@K` and `Dimethyl@K`:" ] }, { @@ -1323,9 +1392,14 @@ "source": [ "### Isotope distribution\n", "\n", - "`alphabase.constants.isotope.IsotopeDistribution` will calculate the isotope distribution and the mono-isotopic idx in the distribution for a given atom composition. \n", - "\n", - "What is the mono-isotopic idx (mono_idx)? For an atom, the `mono_idx` points to the highest abundance isotope, so the value is `round(mass of highest isotope - mass of first isotope)`." + "`alphabase.constants.isotope.IsotopeDistribution` will calculate the isotope distribution and the mono-isotopic idx in the distribution for a given atom composition. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For an atom, the mono-isotopic idx (`mono_idx`) points to the highest abundance isotope, so the value is `round(mass of highest isotope - mass of first isotope)`." ] }, { @@ -1490,7 +1564,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For example, `Fe`'s `mono_idx` is 2," + "For example, `Fe`'s `mono_idx` is 2 (mass from 53.94 to 55.93), " ] }, { @@ -1586,7 +1660,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `mono_idx` for most of the atom compositions is 0, no matter how big the compositions are." + "The `mono_idx` of unlabeled atom compositions is always 0, no matter how big the compositions are. This means `mono` isotope is not necessary to be the `highest` isotope peak, especially when the composition get larger. Here are three examples from small composition to large ones, we can see that the highest peaks move from 0 to 2." ] }, { @@ -1597,7 +1671,10 @@ { "data": { "text/plain": [ - "[('C', 100), ('H', 100), ('O', 50), ('Na', 1)]" + "('mono=0, highest=0',\n", + " array([5.53058051e-01, 3.06480210e-01, 1.06031073e-01, 2.73885413e-02,\n", + " 5.79597328e-03, 1.05055134e-03, 1.67897345e-04, 2.41173838e-05,\n", + " 3.15729577e-06, 3.80635657e-07]))" ] }, "execution_count": 23, @@ -1609,49 +1686,69 @@ "from alphabase.constants.isotope import IsotopeDistribution, parse_formula\n", "iso = IsotopeDistribution()\n", "\n", - "formula = 'C(100)H(100)O(50)Na(1)'\n", + "formula = 'C(50)H(50)O(20)Na(1)'\n", "formula = parse_formula(formula)\n", - "formula" + "dist, mono = iso.calc_formula_distribution(formula)\n", + "f\"mono={mono}, highest={dist.argmax()}\", dist" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 24, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('mono=0, highest=1',\n", + " array([3.21124792e-01, 3.53459703e-01, 2.05844502e-01, 8.38383715e-02,\n", + " 2.66913129e-02, 7.04911613e-03, 1.60206285e-03, 3.21190201e-04,\n", + " 5.78218885e-05, 9.47198919e-06]))" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "> `mono` isotope is not the `highest` isotope!!!" + "formula = 'C(100)H(100)O(20)Na(1)'\n", + "formula = parse_formula(formula)\n", + "dist, mono = iso.calc_formula_distribution(formula)\n", + "f\"mono={mono}, highest={dist.argmax()}\", dist" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0,\n", - " 1,\n", - " array([2.98521241e-01, 3.31991573e-01, 2.13532938e-01, 1.00604878e-01,\n", - " 3.82856126e-02, 1.23872292e-02, 3.51773755e-03, 8.95830236e-04,\n", - " 2.07763024e-04, 4.43944472e-05]))" + "('mono=0, highest=2',\n", + " array([0.10312113, 0.22700935, 0.25713731, 0.19936063, 0.11878142,\n", + " 0.05791123, 0.02402947, 0.00871637, 0.00281814, 0.00082412]))" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "formula = 'C(200)H(200)O(40)Na(1)'\n", + "formula = parse_formula(formula)\n", "dist, mono = iso.calc_formula_distribution(formula)\n", - "mono, dist.argmax(), dist" + "f\"mono={mono}, highest={dist.argmax()}\", dist" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, - "source": [ - "All these low-level functionalities have been integrated into DataFrame functionalities, see `tutorial_dev_dataframes.ipynb` or `Tutorial for Dev: Peptide and Fragment DataFrames`" - ] + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/tutorials/dataframe_structures.ipynb b/docs/tutorials/tutorial_dataframe_structures.ipynb similarity index 99% rename from docs/tutorials/dataframe_structures.ipynb rename to docs/tutorials/tutorial_dataframe_structures.ipynb index f95a50aa..12345579 100644 --- a/docs/tutorials/dataframe_structures.ipynb +++ b/docs/tutorials/tutorial_dataframe_structures.ipynb @@ -6,7 +6,7 @@ "source": [ "# Tutorial: Peptide and Fragment DataFrames\n", "\n", - "We use dataframe, a tabular-like data structure. " + "We use dataframe, a tabular-like data structure to represent peptides and fragments." ] }, { @@ -134,12 +134,12 @@ "source": [ "## Fragment DataFrame\n", "\n", - "Fragment is also orginized in dataframe structure. The column names of the dataframe represent the fragment type, wich schema `Type[_LossType]_z[n]`, where:\n", + "Fragment is also orginized in dataframe structure. The column names of the dataframe represent the fragment type, wich schema `Type[_LossType]_zn`, where:\n", " - `Type` can be `b,y,c,z`\n", " - `_LossType` can be `_modloss`, `_H2O`, or `_NH3`, this is optional.\n", - " - `z[n]` is the charge state. If precursor charge is less than `n`.\n", + " - `zn` is the charge state, for example `z1`.\n", "\n", - "For example:" + "Here are some examples:" ] }, { diff --git a/docs/tutorials/spectral_libraries.ipynb b/docs/tutorials/tutorial_spectral_libraries.ipynb similarity index 100% rename from docs/tutorials/spectral_libraries.ipynb rename to docs/tutorials/tutorial_spectral_libraries.ipynb From 3e6bbaca060c982efa6cec3036f2394ee2e2405a Mon Sep 17 00:00:00 2001 From: jalew188 Date: Fri, 19 Jul 2024 08:38:22 +0200 Subject: [PATCH 43/53] #187 add description for flat frag df --- ...ipynb => tutorial_basic_definitions.ipynb} | 0 .../tutorial_dataframe_structures.ipynb | 377 +++++++++++++++--- 2 files changed, 329 insertions(+), 48 deletions(-) rename docs/tutorials/{tutorial_basic_definations.ipynb => tutorial_basic_definitions.ipynb} (100%) diff --git a/docs/tutorials/tutorial_basic_definations.ipynb b/docs/tutorials/tutorial_basic_definitions.ipynb similarity index 100% rename from docs/tutorials/tutorial_basic_definations.ipynb rename to docs/tutorials/tutorial_basic_definitions.ipynb diff --git a/docs/tutorials/tutorial_dataframe_structures.ipynb b/docs/tutorials/tutorial_dataframe_structures.ipynb index 12345579..ac5f5fa0 100644 --- a/docs/tutorials/tutorial_dataframe_structures.ipynb +++ b/docs/tutorials/tutorial_dataframe_structures.ipynb @@ -6,7 +6,7 @@ "source": [ "# Tutorial: Peptide and Fragment DataFrames\n", "\n", - "We use dataframe, a tabular-like data structure to represent peptides and fragments." + "We use dataframe, a tabular-like data structure to represent peptides and fragments. The dataframe structure is easy to read from human's perspective, and efficient for input and output from machine's perspective." ] }, { @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -673,7 +673,7 @@ "33 175.118958 157.108383 0.000000 159.100235 " ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -703,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -732,14 +732,6 @@ " mod_sites\n", " charge\n", " nAA\n", - " precursor_mz\n", - " i_0\n", - " i_1\n", - " i_2\n", - " i_3\n", - " i_4\n", - " i_5\n", - " mono_isotope_idx\n", " frag_start_idx\n", " frag_stop_idx\n", " \n", @@ -752,14 +744,6 @@ " 2\n", " 1\n", " 8\n", - " 1019.461492\n", - " 0.544890\n", - " 0.294208\n", - " 0.116900\n", - " 0.034340\n", - " 0.008077\n", - " 0.001584\n", - " 0\n", " 0\n", " 7\n", " \n", @@ -770,14 +754,6 @@ " \n", " 2\n", " 9\n", - " 532.757692\n", - " 0.527839\n", - " 0.300826\n", - " 0.123018\n", - " 0.037359\n", - " 0.009104\n", - " 0.001854\n", - " 0\n", " 7\n", " 15\n", " \n", @@ -788,14 +764,6 @@ " 3;6\n", " 3\n", " 20\n", - " 808.337166\n", - " 0.271028\n", - " 0.323775\n", - " 0.225641\n", - " 0.115441\n", - " 0.047553\n", - " 0.016561\n", - " 0\n", " 15\n", " 34\n", " \n", @@ -809,18 +777,13 @@ "1 APDEFMNIK 2 9 \n", "2 WDSEFMNTIRAAAAKDDDDR Phospho@S;Oxidation@M 3;6 3 20 \n", "\n", - " precursor_mz i_0 i_1 i_2 i_3 i_4 i_5 \\\n", - "0 1019.461492 0.544890 0.294208 0.116900 0.034340 0.008077 0.001584 \n", - "1 532.757692 0.527839 0.300826 0.123018 0.037359 0.009104 0.001854 \n", - "2 808.337166 0.271028 0.323775 0.225641 0.115441 0.047553 0.016561 \n", - "\n", - " mono_isotope_idx frag_start_idx frag_stop_idx \n", - "0 0 0 7 \n", - "1 0 7 15 \n", - "2 0 15 34 " + " frag_start_idx frag_stop_idx \n", + "0 0 7 \n", + "1 7 15 \n", + "2 15 34 " ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -831,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -1143,7 +1106,7 @@ "33 175.118958 157.108383 0.000000 159.100235 " ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1154,6 +1117,324 @@ "stop = df['frag_stop_idx'].values[pep_id]\n", "frag_mz_df.iloc[start:stop]" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using on several fragment dataframes (e.g., m/z and intensity dataframes) may be not convinient in some situations, especially when we need to operate subsets of the dataframes. Therefore, alphabase also provides a flattened fragment dataframe strucutre to store all fragment information." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from alphabase.peptide.fragment import flatten_fragments\n", + "import numpy as np\n", + "\n", + "precursor_df, flat_frag_df = flatten_fragments(\n", + " precursor_df=df, \n", + " fragment_mz_df=frag_mz_df, \n", + " fragment_intensity_df=pd.DataFrame(\n", + " np.zeros_like(frag_mz_df.values),\n", + " columns=frag_mz_df.columns\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    sequencemodsmod_siteschargenAAfrag_start_idxfrag_stop_idxflat_frag_start_idxflat_frag_stop_idx
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    " + ], + "text/plain": [ + " sequence mods mod_sites charge nAA \\\n", + "0 ACDEFHIK Carbamidomethyl@C 2 1 8 \n", + "1 APDEFMNIK 2 9 \n", + "2 WDSEFMNTIRAAAAKDDDDR Phospho@S;Oxidation@M 3;6 3 20 \n", + "\n", + " frag_start_idx frag_stop_idx flat_frag_start_idx flat_frag_stop_idx \n", + "0 0 7 0 49 \n", + "1 7 15 49 113 \n", + "2 15 34 113 267 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "precursor_df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " mz intensity type loss_type charge number position\n", + "0 44.049477 0.0 97 0 1 1 0\n", + "1 72.044388 0.0 98 0 1 1 0\n", + "2 89.070938 0.0 99 0 1 1 0\n", + "3 974.403625 0.0 120 0 1 7 0\n", + "4 948.424377 0.0 121 0 1 7 0\n", + ".. ... ... ... ... ... ... ...\n", + "262 1124.946289 0.0 98 0 2 19 18\n", + "263 201.098221 0.0 120 0 1 1 18\n", + "264 175.118958 0.0 121 0 1 1 18\n", + "265 157.108383 0.0 121 18 1 1 18\n", + "266 159.100235 0.0 122 0 1 1 18\n", + "\n", + "[267 rows x 7 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flat_frag_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the flattened fragment dataframe, it contains `mz`, `intensity`, `type`, `loss_type`, `charge`, `number`, and `position` columns, other columns can be flexibly added. All columns are converted to numeric values for better processing in numpy and numba package. For instance , `type` is the ASCII code of `abc/xyz` ions, `a`=97, `b`=98, `c`=99, `x`=120, `y`=121, and `z`=122. Losses are also converted to numbers as well, therefore, Water loss becomes `18`, and phospho loss becomes `98`. \n", + "\n", + "And similar to `frag_start_idx` and `frag_stop_idx`, we use `flat_frag_start_idx` and `flat_frag_stop_idx` to keep the connection between the precursor dataframe and the flattened fragment dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 96b870bf268cd6b1dabf9174481ee23eea084e54 Mon Sep 17 00:00:00 2001 From: GeorgWa Date: Fri, 19 Jul 2024 13:59:01 +0200 Subject: [PATCH 44/53] new sage reader --- alphabase/psm_reader/sage_reader.py | 471 +++++++++++++++++++++---- nbs_tests/psm_reader/sage_reader.ipynb | 110 +++++- 2 files changed, 508 insertions(+), 73 deletions(-) diff --git a/alphabase/psm_reader/sage_reader.py b/alphabase/psm_reader/sage_reader.py index b74527b7..bbbf0d42 100644 --- a/alphabase/psm_reader/sage_reader.py +++ b/alphabase/psm_reader/sage_reader.py @@ -1,9 +1,13 @@ +import logging +import multiprocessing as mp import re import typing from functools import partial +# apply it with multiple cores import numpy as np import pandas as pd +from tqdm import tqdm from alphabase.constants.modification import MOD_DF from alphabase.psm_reader.psm_reader import ( @@ -13,22 +17,205 @@ ) -def sage_spec_idx_from_scannr(scannr: str) -> int: - """Extract the spectrum index from the scannr field in Sage output. +class SageModificationTranslation: + def __init__( + self, + custom_translation_df: pd.DataFrame = None, + ppm_tolerance: int = 10, + mp_process_num=10, + ): + """Translate Sage style modifications to alphabase style modifications. + By default, the translation is done by matching the observed mass and location to the UniMod database. + If a custom translation dataframe is provided, the translation will be done based on the custom translation dataframe first. + + Parameters + ---------- + custom_translation_df : pd.DataFrame + A custom translation dataframe with columns 'modification' and 'matched_mod_name'. + + ppm_tolerance : int + The ppm tolerance for matching the observed mass to the annotated modification mass. + + mp_process_num : int + The number of processes to use for translation. + + """ + + self.custom_translation_df = custom_translation_df + self.ppm_tolerance = ppm_tolerance + self.mp_process_num = mp_process_num + + # validate custom translation df + if self.custom_translation_df is not None: + valid = True + valid &= "modification" in self.custom_translation_df.columns + valid &= "matched_mod_name" in self.custom_translation_df.columns + assert valid, "Custom translation df must have columns 'modification' and 'matched_mod_name'." + + def __call__(self, psm_df: pd.DataFrame) -> pd.DataFrame: + """Translate modifications in the PSMs to alphabase style modifications. + 1. Discover all modifications in the PSMs. + 2. Annotate modifications from custom translation df, if provided. + 3. Annotate all remaining modifications from UniMod. + 4. Apply translation to PSMs. + 5. Drop PSMs with missing modifications. + + Parameters + ---------- + + psm_df : pd.DataFrame + The PSM dataframe with column 'modified_sequence'. + + Returns + ------- + pd.DataFrame + The PSM dataframe with columns 'mod_sites' and 'mods'. + + """ + + # 1. Discover all modifications in the PSMs + discovered_modifications_df = discover_modifications(psm_df) + translation_df = pd.DataFrame() + + # 2. Annotate modifications from custom translation df, if provided + if self.custom_translation_df is not None: + discovered_modifications_df = discovered_modifications_df.merge( + self.custom_translation_df, on="modification", how="left" + ) + for i, row in discovered_modifications_df[ # noqa + discovered_modifications_df["matched_mod_name"].isnull() + ].iterrows(): + logging.warning( + f"No modification found for mass {row['modification']} at position {row['previous_aa']} found in custom_translation_df, will be matched using UniMod" + ) + + translation_df = pd.concat( + [ + translation_df, + discovered_modifications_df[ + discovered_modifications_df["matched_mod_name"].notnull() + ], + ] + ) + discovered_modifications_df = discovered_modifications_df[ + discovered_modifications_df["matched_mod_name"].isnull() + ] + + # 3. Annotate all remaining modifications from UniMod + annotated_df = get_annotated_mod_df() + discovered_modifications_df["matched_mod_name"] = ( + discovered_modifications_df.apply( + lambda x: lookup_modification( + x["mass"], + x["previous_aa"], + x["is_nterm"], + x["is_cterm"], + annotated_df, + ppm_tolerance=self.ppm_tolerance, + ), + axis=1, + ) + ) + for i, row in discovered_modifications_df[ # noqa + discovered_modifications_df["matched_mod_name"].isnull() + ].iterrows(): + logging.warning( + f"UniMod lookup failed for mass {row['modification']} at position {row['previous_aa']}, will be removed." + ) + translation_df = pd.concat( + [ + translation_df, + discovered_modifications_df[ + discovered_modifications_df["matched_mod_name"].notnull() + ], + ] + ) + + # 4. Apply translation to PSMs + _psm_df = apply_translate_modifications_mp(psm_df, translation_df) + + # 5. Drop PSMs with missing modifications + is_null = _psm_df["mod_sites"].isnull() + _psm_df = _psm_df[~is_null] + if np.sum(is_null) > 0: + logging.warning( + f"Dropped {np.sum(is_null)} PSMs with missing modifications." + ) + + return _psm_df + + +def discover_modifications(psm_df: pd.DataFrame) -> pd.DataFrame: + """Discover all modifications in the PSMs. Parameters ---------- + psm_df : pd.DataFrame + The PSM dataframe with column 'modified_sequence'. - scannr : str - The scannr field in Sage output. + Returns + ------- + pd.DataFrame + A dataframe with columns 'modification', 'previous_aa', 'is_nterm', 'is_cterm', 'mass'. + + """ + + modifications = ( + psm_df["modified_sequence"].apply(match_modified_sequence).explode().unique() + ) + modifications = modifications[~pd.isnull(modifications)] + return pd.DataFrame( + list(modifications), + columns=["modification", "previous_aa", "is_nterm", "is_cterm", "mass"], + ) + + +def match_modified_sequence( + sequence: str, +) -> typing.List[typing.Tuple[str, str, bool, bool, float]]: + """Get all matches with the amino acid location. + Not able to resolve C-term modifications. + + P[-100.0]EPTIDE -> [('[-100.0]', 'P', False, False, -100.0)] + [-100.0]PEPTIDE -> [('[-100.0]', '', True, False, -100.0)] + PEPTIDE[-100.0] -> [('[-100.0]', 'E', False, True, -100.0)] + + Parameters + ---------- + + sequence : str + The sequence string. + + Returns + ------- + typing.List[typing.Tuple[str, str, bool, bool, float]] + A list of tuples with the matched modification. + Each match has the structure (match, previous_aa, is_nterm, is_cterm, mass) """ - return int(scannr.split("=")[-1]) + + matches = [] + for m in re.finditer(r"\[(\+|-)(\d+\.\d+)\]", sequence): + previous_char = sequence[m.start() - 1] if m.start() > 0 else "" + next_char = sequence[m.end()] if m.end() < len(sequence) else "" + + is_nterm = next_char == "-" + is_cterm = previous_char == "-" + + aa = previous_char if not (is_nterm | is_cterm) else "" + + mass = float(m.group().replace(r"[", "").replace(r"]", "")) + + matches += [(m.group(), aa, is_nterm, is_cterm, mass)] + + return matches def lookup_modification( mass_observed: float, previous_aa: str, + is_nterm: bool, + is_cterm: bool, mod_annotated_df: pd.DataFrame, ppm_tolerance: int = 10, ) -> str: @@ -44,6 +231,12 @@ def lookup_modification( previous_aa : str The previous amino acid. + is_nterm : bool + Whether the modification is N-terminal. + + is_cterm : bool + Whether the modification is C-terminal. + mod_annotated_df : pd.DataFrame The annotated modification dataframe. @@ -66,90 +259,242 @@ def lookup_modification( # get index of matches mass_match = ppm_distance <= ppm_tolerance - sequence_match = mod_annotated_df["location"] == previous_aa + sequence_match = mod_annotated_df["previous_aa"] == previous_aa filtered_mod_df = mod_annotated_df[mass_match & sequence_match] + if len(filtered_mod_df) == 0: - print(np.min(ppm_distance)) + logging.warning( + f"No modification found for mass {mass_observed} at position {previous_aa}" + ) return None - matched_mod = filtered_mod_df.sort_values(by="unimod_id").iloc[0] + matched_mod = filtered_mod_df.sort_values(by=["unimod_id", "localizer_rank"]).iloc[ + 0 + ] + return matched_mod.name - return matched_mod["mod_name_stripped"] - -def capture_modifications( - sequence: str, mod_annotated_df: pd.DataFrame, ppm_tolerance: int = 10 +def translate_modifications( + sequence: str, mod_translation_df: pd.DataFrame ) -> typing.Tuple[str, str]: - """Capture modifications from a sequence string. + """Translate modifications in the sequence to alphabase style modifications. Parameters ---------- - sequence : str - The modified sequence string. + The sequence string. - mod_annotated_df : pd.DataFrame + mod_translation_df : pd.DataFrame The annotated modification dataframe. - ppm_tolerance : int - The ppm tolerance for matching the observed mass to the annotated modification mass. - Returns ------- - typing.Tuple[str, str] - A tuple of two strings, the first string is the list of modification sites, and the second string is the list of modifications. + A tuple with the translated modification sites and names. """ - # get index of matches - matches = re.finditer(r"\[(\+|-)(\d+\.\d+)\]", sequence) + accumulated_non_sequence_chars = 0 + + mod_sites = [] + mod_names = [] + + for m in re.finditer(r"\[(\+|-)(\d+\.\d+)\]", sequence): + group = m.group() + + previous_char = sequence[m.start() - 1] if m.start() > 0 else "" + next_char = sequence[m.end()] if m.end() < len(sequence) else "" + + is_nterm = next_char == "-" + is_cterm = previous_char == "-" + + if is_nterm: + # real nterm mod with location 0 + matched_mod = mod_translation_df[ + (mod_translation_df["modification"] == group) + & (mod_translation_df["is_nterm"]) + ] + if len(matched_mod) == 0: + return None, None + + matched_mod_name = matched_mod.iloc[0]["matched_mod_name"] + mod_site = "0" + mod_tag_len = m.end() - m.start() + 1 + + elif is_cterm: + # real cterm mod with location -1 or side chain mod at the last aa + matched_mod = mod_translation_df[ + (mod_translation_df["modification"] == group) + & (mod_translation_df["is_cterm"]) + ] + if len(matched_mod) == 0: + return None, None + + matched_mod_name = matched_mod.iloc[0]["matched_mod_name"] + mod_site = ( + "-1" + if matched_mod.iloc[0]["is_cterm"] + else str(m.start() - accumulated_non_sequence_chars) + ) + mod_tag_len = m.end() - m.start() + 1 + + else: + # side chain mod + matched_mod = mod_translation_df[ + (mod_translation_df["modification"] == group) + & (mod_translation_df["previous_aa"] == previous_char) + ] + if len(matched_mod) == 0: + return None, None + + mod_tag_len = m.end() - m.start() + matched_mod_name = matched_mod.iloc[0]["matched_mod_name"] + mod_site = str(m.start() - accumulated_non_sequence_chars) - site_list = [] - mod_list = [] + accumulated_non_sequence_chars += mod_tag_len + mod_sites.append(mod_site) + mod_names.append(matched_mod_name) - error = False + return ";".join(mod_sites), ";".join(mod_names) - match_delta = 0 - for match in matches: - match_start, match_end = match.start(), match.end() - previous_aa = sequence[match_start - 1] if match_start > 0 else "Any_N-term" - mass_observed = float(match.group(2)) * (1 if match.group(1) == "+" else -1) +def apply_translate_modifications( + df: pd.DataFrame, mod_translation_df: pd.DataFrame +) -> pd.DataFrame: + """Apply the translation of modifications to the PSMs. - mod = lookup_modification( - mass_observed, previous_aa, mod_annotated_df, ppm_tolerance=ppm_tolerance + Parameters + ---------- + + df : pd.DataFrame + The PSM dataframe with column 'modified_sequence'. + + mod_translation_df : pd.DataFrame + The annotated modification dataframe. + + Returns + ------- + + pd.DataFrame + The PSM dataframe with columns 'mod_sites' and 'mods'. + + """ + + df["mod_sites"], df["mods"] = zip( + *df["modified_sequence"].apply( + lambda x: translate_modifications(x, mod_translation_df) ) - if mod is not None: - site_list.append(str(match_start - match_delta)) - mod_list.append(mod) + ) + return df - else: - error = True - print( - f"No modification found for mass {mass_observed} at position {match_start} with previous aa {previous_aa}" + +def _batchify_df(df: pd.DataFrame, mp_batch_size: int) -> typing.Generator: + """Internal funciton for applying translation modifications in parallel. + + Parameters + ---------- + df : pd.DataFrame + The PSM dataframe. + + mp_batch_size : int + The batch size for parallel processing. + + Returns + ------- + + typing.Generator + A generator for the batchified dataframe. + """ + + for i in range(0, len(df), mp_batch_size): + yield df.iloc[i : i + mp_batch_size, :] + + +def apply_translate_modifications_mp( + df: pd.DataFrame, + mod_translation_df: pd.DataFrame, + mp_batch_size: int = 50000, + mp_process_num: int = 10, + progress_bar: bool = True, +) -> pd.DataFrame: + """Apply translate modifications with multiprocessing + + Parameters + ---------- + + df : pd.DataFrame + The PSM dataframe. + + mod_translation_df : pd.DataFrame + Dataframe which instructs how to map modifications. + + mp_batch_size : int + The batch size for parallel processing. + + mp_process_num : int + The number of parallel processes + """ + + df_list = [] + with mp.get_context("spawn").Pool(mp_process_num) as p: + processing = p.imap( + partial( + apply_translate_modifications, mod_translation_df=mod_translation_df + ), + _batchify_df(df, mp_batch_size), + ) + if progress_bar: + df_list = list( + tqdm(processing, total=int(np.ceil(len(df) / mp_batch_size))) ) + else: + df_list = list(processing) - match_delta += match_end - match_start + return pd.concat(df_list, ignore_index=True) - if error: - return np.nan, np.nan - else: - return ";".join(site_list), ";".join(mod_list) +def get_annotated_mod_df() -> pd.DataFrame: + """Annotates the modification dataframe for annotation of sage output. + Due to the modified sequence based notation, + C-Terminal and sidechain modifications on the last AA could be confused. + + Returns + ------- + + pd.DataFrame + The annotated modification dataframe with columns 'mass', 'previous_aa', 'is_nterm', 'is_cterm', 'unimod_id', 'localizer_rank'. -def get_annotated_mod_df(): - """Annotates the modification dataframe with the location of the modification.""" + """ mod_annotated_df = MOD_DF.copy() - mod_annotated_df["location"] = ( + + mod_annotated_df["previous_aa"] = ( mod_annotated_df["mod_name"].str.split("@").str[1].str.split("^").str[0] ) - mod_annotated_df = mod_annotated_df.sort_values(by="mass").reset_index(drop=True) - mod_annotated_df["mod_name_stripped"] = mod_annotated_df["mod_name"].str.replace( - " ", "_" - ) - return mod_annotated_df + + # we use the length of the localizer "K", "Any_N-term", "Protein_N-term" as rank to prioritize Any N-term over Protein N-term + mod_annotated_df["localizer_rank"] = mod_annotated_df["previous_aa"].str.len() + mod_annotated_df.loc[mod_annotated_df["localizer_rank"] > 1, "previous_aa"] = "" + + mod_annotated_df["is_nterm"] = mod_annotated_df["mod_name"].str.contains("N-term") + mod_annotated_df["is_cterm"] = mod_annotated_df["mod_name"].str.contains("C-term") + + return mod_annotated_df[ + ["mass", "previous_aa", "is_nterm", "is_cterm", "unimod_id", "localizer_rank"] + ] + + +def sage_spec_idx_from_scannr(scannr: str) -> int: + """Extract the spectrum index from the scannr field in Sage output. + + Parameters + ---------- + + scannr : str + The scannr field in Sage output. + + """ + return int(scannr.split("=")[-1]) class SageReaderBase(PSMReaderBase): @@ -161,8 +506,13 @@ def __init__( fdr=0.01, keep_decoy=False, rt_unit="second", + custom_translation_df=None, + mp_process_num=10, **kwargs, ): + self.custom_translation_df = custom_translation_df + self.mp_process_num = mp_process_num + super().__init__( column_mapping=column_mapping, modification_mapping=modification_mapping, @@ -200,17 +550,12 @@ def _load_modifications(self, origin_df): pass def _translate_modifications(self): - mod_annotated_df = get_annotated_mod_df() - - self._psm_df["mod_sites"], self._psm_df["mods"] = zip( - *self.psm_df["modified_sequence"].apply( - partial( - capture_modifications, - mod_annotated_df=mod_annotated_df, - ppm_tolerance=10, - ) - ) + sage_translation = SageModificationTranslation( + custom_translation_df=self.custom_translation_df, + mp_process_num=self.mp_process_num, ) + self._psm_df = sage_translation(self._psm_df) + # drop modified_sequence self._psm_df.drop(columns=["modified_sequence"], inplace=True) diff --git a/nbs_tests/psm_reader/sage_reader.ipynb b/nbs_tests/psm_reader/sage_reader.ipynb index 51fd6bb0..afa41674 100644 --- a/nbs_tests/psm_reader/sage_reader.ipynb +++ b/nbs_tests/psm_reader/sage_reader.ipynb @@ -46,9 +46,28 @@ "metadata": {}, "outputs": [], "source": [ - "#| hide\n", - "annotated_mod_df = get_annotated_mod_df()\n", - "assert lookup_modification(57.021465, 'C', annotated_mod_df) == 'Carbamidomethyl@C'" + "test_df = pd.DataFrame({\n", + " 'modified_sequence': [\n", + " '[-100.0]-PEPTIDE',\n", + " 'PEPTIDE-[-100.0]',\n", + " 'PEPTIDE[-100.0]',\n", + " 'P[-100.0]EPTIDE',\n", + " 'PEPT[-100.0]IDE',\n", + " 'PE[-100.0]PTIDE[-100.0]P',\n", + " ],\n", + " 'expected_signature': [\n", + " [('[-100.0]', '', True, False, -100)],\n", + " [('[-100.0]', '', False, True, -100)],\n", + " [('[-100.0]', 'E', False, False, -100)],\n", + " [('[-100.0]', 'P', False, False, -100)],\n", + " [('[-100.0]', 'T', False, False, -100)],\n", + " [('[-100.0]', 'E', False, False, -100), ('[-100.0]', 'E', False, False, -100)],\n", + " ]\n", + "})\n", + "\n", + "test_df['observed_signature'] = test_df['modified_sequence'].apply(match_modified_sequence)\n", + "\n", + "assert test_df['observed_signature'].equals(test_df['expected_signature'])" ] }, { @@ -57,10 +76,8 @@ "metadata": {}, "outputs": [], "source": [ - "#| hide\n", - "modifications = capture_modifications('Q[-17.026548]DQSANEKNK[+42.010567]LEM[+15.9949]NK[+42.010567]', annotated_mod_df)\n", - "assert (modifications == ('1;10;13;15', 'Gln->pyro-Glu@Q^Any_N-term;Acetyl@K;Oxidation@M;Acetyl@K'\n", - " ), modifications)" + "mod_annotated_df = get_annotated_mod_df()\n", + "assert all(mod_annotated_df.columns == ['mass','previous_aa','is_nterm','is_cterm','unimod_id','localizer_rank'])" ] }, { @@ -68,6 +85,72 @@ "execution_count": 7, "metadata": {}, "outputs": [], + "source": [ + "assert lookup_modification(15.99490, 'M', False, False, mod_annotated_df) == 'Oxidation@M'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:No modification found for mass [+114.04293] at position found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+114.04293] at position K found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+15.9949] at position M found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+1337.0] at position found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass 1337.0 at position \n", + "WARNING:root:No modification found for mass [+114.04293] at position K found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+15.9949] at position M found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+1337.0] at position found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass 1337.0 at position \n", + "WARNING:root:UniMod lookup failed for mass [+1337.0] at position , will be removed.\n", + "100%|██████████| 1/1 [00:01<00:00, 1.82s/it]\n", + "WARNING:root:Dropped 1 PSMs with missing modifications.\n" + ] + } + ], + "source": [ + "df = pd.DataFrame({\n", + " 'modified_sequence': [\n", + " '[+114.04293]-MAGTK[+114.04293]',\n", + " '[+114.04293]-MAGTK[+114.04293]',\n", + " '[+114.04293]-M[+15.9949]K[+42.010567]LLAR',\n", + " '[+1337.0]-PEPTIDEK'\n", + " ]\n", + "})\n", + "\n", + "custom_translation_df = pd.DataFrame({\n", + " 'modification': ['[+42.010567]'],\n", + " 'matched_mod_name': ['ThisModDoesNotExist@K']\n", + "})\n", + "\n", + "sage_translation = SageModificationTranslation(\n", + " custom_translation_df=custom_translation_df\n", + " )\n", + "result_df = sage_translation(df)\n", + "\n", + "assert result_df['mod_sites'].equals(pd.Series([\n", + " '0;5',\n", + " '0;5',\n", + " '0;1;2'\n", + "]))\n", + "\n", + "assert result_df['mods'].equals(pd.Series([\n", + " 'GG@Protein_N-term;GG@K',\n", + " 'GG@Protein_N-term;GG@K',\n", + " 'GG@Protein_N-term;Oxidation@M;ThisModDoesNotExist@K'\n", + "]))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], "source": [ "#| hide\n", "from io import StringIO" @@ -75,9 +158,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:01<00:00, 1.84s/it]\n" + ] + }, { "data": { "text/html": [ @@ -298,7 +388,7 @@ "7 17 1.000000 821.387362 " ] }, - "execution_count": 8, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ From afa9e40e550b40af75800fe5275c321ed496f885 Mon Sep 17 00:00:00 2001 From: GeorgWa Date: Fri, 19 Jul 2024 14:32:01 +0200 Subject: [PATCH 45/53] clean nb --- nbs_tests/psm_reader/sage_reader.ipynb | 277 ++----------------------- 1 file changed, 13 insertions(+), 264 deletions(-) diff --git a/nbs_tests/psm_reader/sage_reader.ipynb b/nbs_tests/psm_reader/sage_reader.ipynb index afa41674..88729079 100644 --- a/nbs_tests/psm_reader/sage_reader.ipynb +++ b/nbs_tests/psm_reader/sage_reader.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -91,28 +91,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:No modification found for mass [+114.04293] at position found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+114.04293] at position K found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+15.9949] at position M found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+1337.0] at position found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass 1337.0 at position \n", - "WARNING:root:No modification found for mass [+114.04293] at position K found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+15.9949] at position M found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+1337.0] at position found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass 1337.0 at position \n", - "WARNING:root:UniMod lookup failed for mass [+1337.0] at position , will be removed.\n", - "100%|██████████| 1/1 [00:01<00:00, 1.82s/it]\n", - "WARNING:root:Dropped 1 PSMs with missing modifications.\n" - ] - } - ], + "outputs": [], "source": [ "df = pd.DataFrame({\n", " 'modified_sequence': [\n", @@ -148,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -158,241 +139,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:01<00:00, 1.84s/it]\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    sequencechargertraw_namescoreproteinsfdrdecoyspec_idxmod_sitesmodsnAArt_normprecursor_mz
    0VDNDENEHQLSLR30.15896220160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.061473sp|P06748|NPM_HUMAN0.000106False7846130.561446523.581497
    1YSGSEGSTQTLTK20.09430620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_12.mzML1.051350sp|P25815|S100P_HUMAN0.000106False4250131.000000679.825346
    2VDNDENEHQLSLR30.18803920160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.050966sp|P06748|NPM_HUMAN0.000106False9584130.664146523.581497
    3VDDYSQEWAAQTEK20.20664620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.058880sp|O95602|RPA1_HUMAN0.000106False10841141.000000835.370649
    4DCEDPEYKPLQGPPK30.15920020160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.054214sp|Q9HCK8|CHD8_HUMAN0.000106False79322Carbamidomethyl@C150.770401591.610177
    5ELGPLPDDDDMASPK20.28312920160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.051607sp|Q86U86|PB1_HUMAN0.000106False14771151.000000800.363978
    6VMQENSSSFSDLSER20.16621620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.044358sp|Q86TC9|MYPN_HUMAN0.000106False83752Oxidation@M150.804350866.378148
    7ITTGSSSAGTQSSTSNR20.05934020160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_11.mzML1.055879sp|O14974|MYPT1_HUMAN0.000106False1864171.000000821.387362
    \n", - "
    " - ], - "text/plain": [ - " sequence charge rt \\\n", - "0 VDNDENEHQLSLR 3 0.158962 \n", - "1 YSGSEGSTQTLTK 2 0.094306 \n", - "2 VDNDENEHQLSLR 3 0.188039 \n", - "3 VDDYSQEWAAQTEK 2 0.206646 \n", - "4 DCEDPEYKPLQGPPK 3 0.159200 \n", - "5 ELGPLPDDDDMASPK 2 0.283129 \n", - "6 VMQENSSSFSDLSER 2 0.166216 \n", - "7 ITTGSSSAGTQSSTSNR 2 0.059340 \n", - "\n", - " raw_name score \\\n", - "0 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.061473 \n", - "1 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_12.mzML 1.051350 \n", - "2 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.050966 \n", - "3 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.058880 \n", - "4 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.054214 \n", - "5 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.051607 \n", - "6 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.044358 \n", - "7 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_11.mzML 1.055879 \n", - "\n", - " proteins fdr decoy spec_idx mod_sites \\\n", - "0 sp|P06748|NPM_HUMAN 0.000106 False 7846 \n", - "1 sp|P25815|S100P_HUMAN 0.000106 False 4250 \n", - "2 sp|P06748|NPM_HUMAN 0.000106 False 9584 \n", - "3 sp|O95602|RPA1_HUMAN 0.000106 False 10841 \n", - "4 sp|Q9HCK8|CHD8_HUMAN 0.000106 False 7932 2 \n", - "5 sp|Q86U86|PB1_HUMAN 0.000106 False 14771 \n", - "6 sp|Q86TC9|MYPN_HUMAN 0.000106 False 8375 2 \n", - "7 sp|O14974|MYPT1_HUMAN 0.000106 False 1864 \n", - "\n", - " mods nAA rt_norm precursor_mz \n", - "0 13 0.561446 523.581497 \n", - "1 13 1.000000 679.825346 \n", - "2 13 0.664146 523.581497 \n", - "3 14 1.000000 835.370649 \n", - "4 Carbamidomethyl@C 15 0.770401 591.610177 \n", - "5 15 1.000000 800.363978 \n", - "6 Oxidation@M 15 0.804350 866.378148 \n", - "7 17 1.000000 821.387362 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#| hide\n", "txt = StringIO(\"\"\"filename\tscannr\tpeptide\tstripped_peptide\tproteins\tis_decoy\tcharge\trt\tion_mobility\tspectrum_q\tpeptide_q\tprotein_q\tsage_discriminant_score\n", @@ -413,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ From f5b44ef2295b60d85453c35858634aa17d30bc27 Mon Sep 17 00:00:00 2001 From: GeorgWa Date: Mon, 22 Jul 2024 18:32:30 +0200 Subject: [PATCH 46/53] review fixes --- alphabase/psm_reader/sage_reader.py | 125 ++++++++++++++----------- nbs_tests/psm_reader/sage_reader.ipynb | 18 ++-- 2 files changed, 84 insertions(+), 59 deletions(-) diff --git a/alphabase/psm_reader/sage_reader.py b/alphabase/psm_reader/sage_reader.py index bbbf0d42..6d717889 100644 --- a/alphabase/psm_reader/sage_reader.py +++ b/alphabase/psm_reader/sage_reader.py @@ -4,7 +4,6 @@ import typing from functools import partial -# apply it with multiple cores import numpy as np import pandas as pd from tqdm import tqdm @@ -25,6 +24,7 @@ def __init__( mp_process_num=10, ): """Translate Sage style modifications to alphabase style modifications. + A modified sequence like VM[+15.9949]QENSSSFSDLSER will be translated to mods: Oxidation@M, mod_sites: 2. By default, the translation is done by matching the observed mass and location to the UniMod database. If a custom translation dataframe is provided, the translation will be done based on the custom translation dataframe first. @@ -50,7 +50,10 @@ def __init__( valid = True valid &= "modification" in self.custom_translation_df.columns valid &= "matched_mod_name" in self.custom_translation_df.columns - assert valid, "Custom translation df must have columns 'modification' and 'matched_mod_name'." + if not valid: + raise ValueError( + "Custom translation df must have columns 'modification' and 'matched_mod_name'." + ) def __call__(self, psm_df: pd.DataFrame) -> pd.DataFrame: """Translate modifications in the PSMs to alphabase style modifications. @@ -62,7 +65,6 @@ def __call__(self, psm_df: pd.DataFrame) -> pd.DataFrame: Parameters ---------- - psm_df : pd.DataFrame The PSM dataframe with column 'modified_sequence'. @@ -70,19 +72,45 @@ def __call__(self, psm_df: pd.DataFrame) -> pd.DataFrame: ------- pd.DataFrame The PSM dataframe with columns 'mod_sites' and 'mods'. - """ # 1. Discover all modifications in the PSMs - discovered_modifications_df = discover_modifications(psm_df) + discovered_modifications_df = _discover_modifications(psm_df) translation_df = pd.DataFrame() # 2. Annotate modifications from custom translation df, if provided + discovered_modifications_df, translation_df = ( + self._annotate_from_custom_translation( + discovered_modifications_df, translation_df + ) + ) + + # 3. Annotate all remaining modifications from UniMod + translation_df = self._annotate_from_unimod( + discovered_modifications_df, translation_df + ) + + # 4. Apply translation to PSMs + translated_psm_df = _apply_translate_modifications_mp(psm_df, translation_df) + + # 5. Drop PSMs with missing modifications + is_null = translated_psm_df["mod_sites"].isnull() + translated_psm_df = translated_psm_df[~is_null] + if np.sum(is_null) > 0: + logging.warning( + f"Dropped {np.sum(is_null)} PSMs with missing modifications." + ) + + return translated_psm_df + + def _annotate_from_custom_translation( + self, discovered_modifications_df: pd.DataFrame, translation_df: pd.DataFrame + ) -> typing.Tuple[pd.DataFrame, pd.DataFrame]: if self.custom_translation_df is not None: discovered_modifications_df = discovered_modifications_df.merge( self.custom_translation_df, on="modification", how="left" ) - for i, row in discovered_modifications_df[ # noqa + for _, row in discovered_modifications_df[ discovered_modifications_df["matched_mod_name"].isnull() ].iterrows(): logging.warning( @@ -101,22 +129,24 @@ def __call__(self, psm_df: pd.DataFrame) -> pd.DataFrame: discovered_modifications_df["matched_mod_name"].isnull() ] - # 3. Annotate all remaining modifications from UniMod - annotated_df = get_annotated_mod_df() + return discovered_modifications_df, translation_df + + def _annotate_from_unimod( + self, discovered_modifications_df: pd.DataFrame, translation_df: pd.DataFrame + ) -> pd.DataFrame: + annotated_df = _get_annotated_mod_df() discovered_modifications_df["matched_mod_name"] = ( discovered_modifications_df.apply( - lambda x: lookup_modification( + lambda x: _lookup_modification( x["mass"], x["previous_aa"], - x["is_nterm"], - x["is_cterm"], annotated_df, ppm_tolerance=self.ppm_tolerance, ), axis=1, ) ) - for i, row in discovered_modifications_df[ # noqa + for _, row in discovered_modifications_df[ discovered_modifications_df["matched_mod_name"].isnull() ].iterrows(): logging.warning( @@ -131,21 +161,10 @@ def __call__(self, psm_df: pd.DataFrame) -> pd.DataFrame: ] ) - # 4. Apply translation to PSMs - _psm_df = apply_translate_modifications_mp(psm_df, translation_df) - - # 5. Drop PSMs with missing modifications - is_null = _psm_df["mod_sites"].isnull() - _psm_df = _psm_df[~is_null] - if np.sum(is_null) > 0: - logging.warning( - f"Dropped {np.sum(is_null)} PSMs with missing modifications." - ) - - return _psm_df + return translation_df -def discover_modifications(psm_df: pd.DataFrame) -> pd.DataFrame: +def _discover_modifications(psm_df: pd.DataFrame) -> pd.DataFrame: """Discover all modifications in the PSMs. Parameters @@ -161,7 +180,7 @@ def discover_modifications(psm_df: pd.DataFrame) -> pd.DataFrame: """ modifications = ( - psm_df["modified_sequence"].apply(match_modified_sequence).explode().unique() + psm_df["modified_sequence"].apply(_match_modified_sequence).explode().unique() ) modifications = modifications[~pd.isnull(modifications)] return pd.DataFrame( @@ -170,15 +189,14 @@ def discover_modifications(psm_df: pd.DataFrame) -> pd.DataFrame: ) -def match_modified_sequence( +def _match_modified_sequence( sequence: str, ) -> typing.List[typing.Tuple[str, str, bool, bool, float]]: """Get all matches with the amino acid location. - Not able to resolve C-term modifications. P[-100.0]EPTIDE -> [('[-100.0]', 'P', False, False, -100.0)] - [-100.0]PEPTIDE -> [('[-100.0]', '', True, False, -100.0)] - PEPTIDE[-100.0] -> [('[-100.0]', 'E', False, True, -100.0)] + [-100.0]-PEPTIDE -> [('[-100.0]', '', True, False, -100.0)] + PEPTIDE-[-100.0] -> [('[-100.0]', 'E', False, True, -100.0)] Parameters ---------- @@ -195,6 +213,8 @@ def match_modified_sequence( """ matches = [] + # Matches the square bracket modification pattern from modified sequences + # [-100.0]-PEPTIDE -> [('[-100.0]', '', True, False, -100.0)] for m in re.finditer(r"\[(\+|-)(\d+\.\d+)\]", sequence): previous_char = sequence[m.start() - 1] if m.start() > 0 else "" next_char = sequence[m.end()] if m.end() < len(sequence) else "" @@ -211,11 +231,9 @@ def match_modified_sequence( return matches -def lookup_modification( +def _lookup_modification( mass_observed: float, previous_aa: str, - is_nterm: bool, - is_cterm: bool, mod_annotated_df: pd.DataFrame, ppm_tolerance: int = 10, ) -> str: @@ -231,12 +249,6 @@ def lookup_modification( previous_aa : str The previous amino acid. - is_nterm : bool - Whether the modification is N-terminal. - - is_cterm : bool - Whether the modification is C-terminal. - mod_annotated_df : pd.DataFrame The annotated modification dataframe. @@ -269,15 +281,20 @@ def lookup_modification( ) return None + if len(filtered_mod_df) > 1: + logging.warning( + f"Multiple modifications found for mass {mass_observed} at position {previous_aa}, will use the one with the lowest localizer rank. Please use the custom translation df to resolve this." + ) + matched_mod = filtered_mod_df.sort_values(by=["unimod_id", "localizer_rank"]).iloc[ 0 ] return matched_mod.name -def translate_modifications( +def _translate_modifications( sequence: str, mod_translation_df: pd.DataFrame -) -> typing.Tuple[str, str]: +) -> typing.Tuple[typing.Optional[str], typing.Optional[str]]: """Translate modifications in the sequence to alphabase style modifications. Parameters @@ -348,9 +365,9 @@ def translate_modifications( if len(matched_mod) == 0: return None, None - mod_tag_len = m.end() - m.start() matched_mod_name = matched_mod.iloc[0]["matched_mod_name"] mod_site = str(m.start() - accumulated_non_sequence_chars) + mod_tag_len = m.end() - m.start() accumulated_non_sequence_chars += mod_tag_len mod_sites.append(mod_site) @@ -359,7 +376,7 @@ def translate_modifications( return ";".join(mod_sites), ";".join(mod_names) -def apply_translate_modifications( +def _apply_translate_modifications( df: pd.DataFrame, mod_translation_df: pd.DataFrame ) -> pd.DataFrame: """Apply the translation of modifications to the PSMs. @@ -383,7 +400,7 @@ def apply_translate_modifications( df["mod_sites"], df["mods"] = zip( *df["modified_sequence"].apply( - lambda x: translate_modifications(x, mod_translation_df) + lambda x: _translate_modifications(x, mod_translation_df) ) ) return df @@ -411,7 +428,7 @@ def _batchify_df(df: pd.DataFrame, mp_batch_size: int) -> typing.Generator: yield df.iloc[i : i + mp_batch_size, :] -def apply_translate_modifications_mp( +def _apply_translate_modifications_mp( df: pd.DataFrame, mod_translation_df: pd.DataFrame, mp_batch_size: int = 50000, @@ -440,7 +457,7 @@ def apply_translate_modifications_mp( with mp.get_context("spawn").Pool(mp_process_num) as p: processing = p.imap( partial( - apply_translate_modifications, mod_translation_df=mod_translation_df + _apply_translate_modifications, mod_translation_df=mod_translation_df ), _batchify_df(df, mp_batch_size), ) @@ -454,7 +471,7 @@ def apply_translate_modifications_mp( return pd.concat(df_list, ignore_index=True) -def get_annotated_mod_df() -> pd.DataFrame: +def _get_annotated_mod_df() -> pd.DataFrame: """Annotates the modification dataframe for annotation of sage output. Due to the modified sequence based notation, C-Terminal and sidechain modifications on the last AA could be confused. @@ -484,17 +501,17 @@ def get_annotated_mod_df() -> pd.DataFrame: ] -def sage_spec_idx_from_scannr(scannr: str) -> int: - """Extract the spectrum index from the scannr field in Sage output. +def _sage_spec_idx_from_scan_nr(scan_nr: str) -> int: + """Extract the spectrum index from the scan_nr field in Sage output. Parameters ---------- - scannr : str - The scannr field in Sage output. + scan_nr : str + The scan_nr field in Sage output. """ - return int(scannr.split("=")[-1]) + return int(scan_nr.split("=")[-1]) class SageReaderBase(PSMReaderBase): @@ -532,7 +549,9 @@ def _load_file(self, filename): raise NotImplementedError def _transform_table(self, origin_df): - self.psm_df["spec_idx"] = self.psm_df["scannr"].apply(sage_spec_idx_from_scannr) + self.psm_df["spec_idx"] = self.psm_df["scannr"].apply( + _sage_spec_idx_from_scan_nr + ) self.psm_df.drop(columns=["scannr"], inplace=True) def _translate_decoy(self, origin_df): diff --git a/nbs_tests/psm_reader/sage_reader.ipynb b/nbs_tests/psm_reader/sage_reader.ipynb index 88729079..fc4276d9 100644 --- a/nbs_tests/psm_reader/sage_reader.ipynb +++ b/nbs_tests/psm_reader/sage_reader.ipynb @@ -26,7 +26,13 @@ "metadata": {}, "outputs": [], "source": [ - "from alphabase.psm_reader.sage_reader import *\n", + "from alphabase.psm_reader.sage_reader import (\n", + " SageModificationTranslation,\n", + " _sage_spec_idx_from_scan_nr,\n", + " _match_modified_sequence,\n", + " _get_annotated_mod_df,\n", + " _lookup_modification,\n", + ")\n", "register_readers()" ] }, @@ -37,7 +43,7 @@ "outputs": [], "source": [ "#| hide\n", - "assert sage_spec_idx_from_scannr('controllerType=0 controllerNumber=1 scan=7846') == 7846" + "assert _sage_spec_idx_from_scan_nr('controllerType=0 controllerNumber=1 scan=7846') == 7846" ] }, { @@ -65,7 +71,7 @@ " ]\n", "})\n", "\n", - "test_df['observed_signature'] = test_df['modified_sequence'].apply(match_modified_sequence)\n", + "test_df['observed_signature'] = test_df['modified_sequence'].apply(_match_modified_sequence)\n", "\n", "assert test_df['observed_signature'].equals(test_df['expected_signature'])" ] @@ -76,7 +82,7 @@ "metadata": {}, "outputs": [], "source": [ - "mod_annotated_df = get_annotated_mod_df()\n", + "mod_annotated_df = _get_annotated_mod_df()\n", "assert all(mod_annotated_df.columns == ['mass','previous_aa','is_nterm','is_cterm','unimod_id','localizer_rank'])" ] }, @@ -86,7 +92,7 @@ "metadata": {}, "outputs": [], "source": [ - "assert lookup_modification(15.99490, 'M', False, False, mod_annotated_df) == 'Oxidation@M'" + "assert _lookup_modification(15.99490, 'M', mod_annotated_df) == 'Oxidation@M'" ] }, { @@ -199,7 +205,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.7" } }, "nbformat": 4, From e31cf018c6fa47cb2499592fb32a9b51224da1ba Mon Sep 17 00:00:00 2001 From: GeorgWa Date: Mon, 22 Jul 2024 18:52:20 +0200 Subject: [PATCH 47/53] missing test import --- nbs_tests/psm_reader/sage_reader.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/nbs_tests/psm_reader/sage_reader.ipynb b/nbs_tests/psm_reader/sage_reader.ipynb index fc4276d9..db62d9db 100644 --- a/nbs_tests/psm_reader/sage_reader.ipynb +++ b/nbs_tests/psm_reader/sage_reader.ipynb @@ -32,6 +32,7 @@ " _match_modified_sequence,\n", " _get_annotated_mod_df,\n", " _lookup_modification,\n", + " register_readers,\n", ")\n", "register_readers()" ] From 7f56c7a9b1960c8ec742ecd99f958123b95db4a3 Mon Sep 17 00:00:00 2001 From: GeorgWa Date: Tue, 23 Jul 2024 10:54:31 +0200 Subject: [PATCH 48/53] fix tests --- nbs_tests/psm_reader/sage_reader.ipynb | 282 +++++++++++++++++++++++-- 1 file changed, 268 insertions(+), 14 deletions(-) diff --git a/nbs_tests/psm_reader/sage_reader.ipynb b/nbs_tests/psm_reader/sage_reader.ipynb index db62d9db..09a60452 100644 --- a/nbs_tests/psm_reader/sage_reader.ipynb +++ b/nbs_tests/psm_reader/sage_reader.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,21 +11,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#| hide\n", "%reload_ext autoreload\n", - "%autoreload 2" + "%autoreload 2\n", + "import pandas as pd\n", + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ + "from alphabase.psm_reader import psm_reader_provider\n", "from alphabase.psm_reader.sage_reader import (\n", " SageModificationTranslation,\n", " _sage_spec_idx_from_scan_nr,\n", @@ -39,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -98,9 +101,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:No modification found for mass [+114.04293] at position found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+114.04293] at position K found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+15.9949] at position M found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+114.04293] at position K found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+15.9949] at position M found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:No modification found for mass [+1337.0] at position found in custom_translation_df, will be matched using UniMod\n", + "WARNING:root:Multiple modifications found for mass 114.04293 at position , will use the one with the lowest localizer rank. Please use the custom translation df to resolve this.\n", + "WARNING:root:Multiple modifications found for mass 114.04293 at position K, will use the one with the lowest localizer rank. Please use the custom translation df to resolve this.\n", + "WARNING:root:No modification found for mass 1337.0 at position \n", + "WARNING:root:UniMod lookup failed for mass [+1337.0] at position , will be removed.\n", + "100%|██████████| 1/1 [00:02<00:00, 2.50s/it]\n", + "WARNING:root:Dropped 1 PSMs with missing modifications.\n" + ] + } + ], "source": [ "df = pd.DataFrame({\n", " 'modified_sequence': [\n", @@ -136,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -146,9 +168,241 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:02<00:00, 2.52s/it]\n" + ] + }, + { + "data": { + "text/html": [ + "
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    sequencechargertraw_namescoreproteinsfdrdecoyspec_idxmod_sitesmodsnAArt_normprecursor_mz
    0VDNDENEHQLSLR30.15896220160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.061473sp|P06748|NPM_HUMAN0.000106False7846130.561446523.581497
    1YSGSEGSTQTLTK20.09430620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_12.mzML1.051350sp|P25815|S100P_HUMAN0.000106False4250131.000000679.825346
    2VDNDENEHQLSLR30.18803920160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.050966sp|P06748|NPM_HUMAN0.000106False9584130.664146523.581497
    3VDDYSQEWAAQTEK20.20664620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.058880sp|O95602|RPA1_HUMAN0.000106False10841141.000000835.370649
    4DCEDPEYKPLQGPPK30.15920020160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.054214sp|Q9HCK8|CHD8_HUMAN0.000106False79322Carbamidomethyl@C150.770401591.610177
    5ELGPLPDDDDMASPK20.28312920160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.051607sp|Q86U86|PB1_HUMAN0.000106False14771151.000000800.363978
    6VMQENSSSFSDLSER20.16621620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.044358sp|Q86TC9|MYPN_HUMAN0.000106False83752Oxidation@M150.804350866.378148
    7ITTGSSSAGTQSSTSNR20.05934020160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_11.mzML1.055879sp|O14974|MYPT1_HUMAN0.000106False1864171.000000821.387362
    \n", + "
    " + ], + "text/plain": [ + " sequence charge rt \\\n", + "0 VDNDENEHQLSLR 3 0.158962 \n", + "1 YSGSEGSTQTLTK 2 0.094306 \n", + "2 VDNDENEHQLSLR 3 0.188039 \n", + "3 VDDYSQEWAAQTEK 2 0.206646 \n", + "4 DCEDPEYKPLQGPPK 3 0.159200 \n", + "5 ELGPLPDDDDMASPK 2 0.283129 \n", + "6 VMQENSSSFSDLSER 2 0.166216 \n", + "7 ITTGSSSAGTQSSTSNR 2 0.059340 \n", + "\n", + " raw_name score \\\n", + "0 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.061473 \n", + "1 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_12.mzML 1.051350 \n", + "2 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.050966 \n", + "3 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.058880 \n", + "4 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.054214 \n", + "5 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.051607 \n", + "6 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.044358 \n", + "7 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_11.mzML 1.055879 \n", + "\n", + " proteins fdr decoy spec_idx mod_sites \\\n", + "0 sp|P06748|NPM_HUMAN 0.000106 False 7846 \n", + "1 sp|P25815|S100P_HUMAN 0.000106 False 4250 \n", + "2 sp|P06748|NPM_HUMAN 0.000106 False 9584 \n", + "3 sp|O95602|RPA1_HUMAN 0.000106 False 10841 \n", + "4 sp|Q9HCK8|CHD8_HUMAN 0.000106 False 7932 2 \n", + "5 sp|Q86U86|PB1_HUMAN 0.000106 False 14771 \n", + "6 sp|Q86TC9|MYPN_HUMAN 0.000106 False 8375 2 \n", + "7 sp|O14974|MYPT1_HUMAN 0.000106 False 1864 \n", + "\n", + " mods nAA rt_norm precursor_mz \n", + "0 13 0.561446 523.581497 \n", + "1 13 1.000000 679.825346 \n", + "2 13 0.664146 523.581497 \n", + "3 14 1.000000 835.370649 \n", + "4 Carbamidomethyl@C 15 0.770401 591.610177 \n", + "5 15 1.000000 800.363978 \n", + "6 Oxidation@M 15 0.804350 866.378148 \n", + "7 17 1.000000 821.387362 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#| hide\n", "txt = StringIO(\"\"\"filename\tscannr\tpeptide\tstripped_peptide\tproteins\tis_decoy\tcharge\trt\tion_mobility\tspectrum_q\tpeptide_q\tprotein_q\tsage_discriminant_score\n", @@ -169,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ From eb4745ca286340516a57d403dd6880d05565581a Mon Sep 17 00:00:00 2001 From: GeorgWa Date: Tue, 23 Jul 2024 11:27:25 +0200 Subject: [PATCH 49/53] added docstring --- alphabase/psm_reader/sage_reader.py | 37 ++++ nbs_tests/psm_reader/sage_reader.ipynb | 277 ++----------------------- 2 files changed, 50 insertions(+), 264 deletions(-) diff --git a/alphabase/psm_reader/sage_reader.py b/alphabase/psm_reader/sage_reader.py index 6d717889..5edb3804 100644 --- a/alphabase/psm_reader/sage_reader.py +++ b/alphabase/psm_reader/sage_reader.py @@ -106,6 +106,26 @@ def __call__(self, psm_df: pd.DataFrame) -> pd.DataFrame: def _annotate_from_custom_translation( self, discovered_modifications_df: pd.DataFrame, translation_df: pd.DataFrame ) -> typing.Tuple[pd.DataFrame, pd.DataFrame]: + """Annotate modifications from custom translation df, if provided. + Discovered modifications are first matched using the custom translation dataframe. + If no match is found, the modifications are returned for matching using UniMod. + + Parameters + ---------- + + discovered_modifications_df : pd.DataFrame + The discovered modifications dataframe. + + translation_df : pd.DataFrame + The translation dataframe. + + Returns + ------- + + typing.Tuple[pd.DataFrame, pd.DataFrame] + The updated discovered modifications dataframe and translation dataframe. + + """ if self.custom_translation_df is not None: discovered_modifications_df = discovered_modifications_df.merge( self.custom_translation_df, on="modification", how="left" @@ -134,6 +154,23 @@ def _annotate_from_custom_translation( def _annotate_from_unimod( self, discovered_modifications_df: pd.DataFrame, translation_df: pd.DataFrame ) -> pd.DataFrame: + """Annotate all remaining modifications from UniMod. + UniMod modification are used from the global MOD_DF. + + Parameters + ---------- + discovered_modifications_df : pd.DataFrame + The discovered modifications dataframe. + + translation_df : pd.DataFrame + The translation dataframe. + + Returns + ------- + pd.DataFrame + The updated translation dataframe. + """ + annotated_df = _get_annotated_mod_df() discovered_modifications_df["matched_mod_name"] = ( discovered_modifications_df.apply( diff --git a/nbs_tests/psm_reader/sage_reader.ipynb b/nbs_tests/psm_reader/sage_reader.ipynb index 09a60452..2646aba0 100644 --- a/nbs_tests/psm_reader/sage_reader.ipynb +++ b/nbs_tests/psm_reader/sage_reader.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -101,28 +101,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:No modification found for mass [+114.04293] at position found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+114.04293] at position K found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+15.9949] at position M found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+114.04293] at position K found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+15.9949] at position M found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:No modification found for mass [+1337.0] at position found in custom_translation_df, will be matched using UniMod\n", - "WARNING:root:Multiple modifications found for mass 114.04293 at position , will use the one with the lowest localizer rank. Please use the custom translation df to resolve this.\n", - "WARNING:root:Multiple modifications found for mass 114.04293 at position K, will use the one with the lowest localizer rank. Please use the custom translation df to resolve this.\n", - "WARNING:root:No modification found for mass 1337.0 at position \n", - "WARNING:root:UniMod lookup failed for mass [+1337.0] at position , will be removed.\n", - "100%|██████████| 1/1 [00:02<00:00, 2.50s/it]\n", - "WARNING:root:Dropped 1 PSMs with missing modifications.\n" - ] - } - ], + "outputs": [], "source": [ "df = pd.DataFrame({\n", " 'modified_sequence': [\n", @@ -158,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,241 +149,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:02<00:00, 2.52s/it]\n" - ] - }, - { - "data": { - "text/html": [ - "
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    sequencechargertraw_namescoreproteinsfdrdecoyspec_idxmod_sitesmodsnAArt_normprecursor_mz
    0VDNDENEHQLSLR30.15896220160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.061473sp|P06748|NPM_HUMAN0.000106False7846130.561446523.581497
    1YSGSEGSTQTLTK20.09430620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_12.mzML1.051350sp|P25815|S100P_HUMAN0.000106False4250131.000000679.825346
    2VDNDENEHQLSLR30.18803920160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.050966sp|P06748|NPM_HUMAN0.000106False9584130.664146523.581497
    3VDDYSQEWAAQTEK20.20664620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.058880sp|O95602|RPA1_HUMAN0.000106False10841141.000000835.370649
    4DCEDPEYKPLQGPPK30.15920020160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.054214sp|Q9HCK8|CHD8_HUMAN0.000106False79322Carbamidomethyl@C150.770401591.610177
    5ELGPLPDDDDMASPK20.28312920160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML1.051607sp|Q86U86|PB1_HUMAN0.000106False14771151.000000800.363978
    6VMQENSSSFSDLSER20.16621620160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML1.044358sp|Q86TC9|MYPN_HUMAN0.000106False83752Oxidation@M150.804350866.378148
    7ITTGSSSAGTQSSTSNR20.05934020160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_11.mzML1.055879sp|O14974|MYPT1_HUMAN0.000106False1864171.000000821.387362
    \n", - "
    " - ], - "text/plain": [ - " sequence charge rt \\\n", - "0 VDNDENEHQLSLR 3 0.158962 \n", - "1 YSGSEGSTQTLTK 2 0.094306 \n", - "2 VDNDENEHQLSLR 3 0.188039 \n", - "3 VDDYSQEWAAQTEK 2 0.206646 \n", - "4 DCEDPEYKPLQGPPK 3 0.159200 \n", - "5 ELGPLPDDDDMASPK 2 0.283129 \n", - "6 VMQENSSSFSDLSER 2 0.166216 \n", - "7 ITTGSSSAGTQSSTSNR 2 0.059340 \n", - "\n", - " raw_name score \\\n", - "0 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.061473 \n", - "1 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_12.mzML 1.051350 \n", - "2 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.050966 \n", - "3 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.058880 \n", - "4 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.054214 \n", - "5 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_10.mzML 1.051607 \n", - "6 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_13.mzML 1.044358 \n", - "7 20160107_QE5_UPLC1_AKP_Hep2_R1_Pro46F_11.mzML 1.055879 \n", - "\n", - " proteins fdr decoy spec_idx mod_sites \\\n", - "0 sp|P06748|NPM_HUMAN 0.000106 False 7846 \n", - "1 sp|P25815|S100P_HUMAN 0.000106 False 4250 \n", - "2 sp|P06748|NPM_HUMAN 0.000106 False 9584 \n", - "3 sp|O95602|RPA1_HUMAN 0.000106 False 10841 \n", - "4 sp|Q9HCK8|CHD8_HUMAN 0.000106 False 7932 2 \n", - "5 sp|Q86U86|PB1_HUMAN 0.000106 False 14771 \n", - "6 sp|Q86TC9|MYPN_HUMAN 0.000106 False 8375 2 \n", - "7 sp|O14974|MYPT1_HUMAN 0.000106 False 1864 \n", - "\n", - " mods nAA rt_norm precursor_mz \n", - "0 13 0.561446 523.581497 \n", - "1 13 1.000000 679.825346 \n", - "2 13 0.664146 523.581497 \n", - "3 14 1.000000 835.370649 \n", - "4 Carbamidomethyl@C 15 0.770401 591.610177 \n", - "5 15 1.000000 800.363978 \n", - "6 Oxidation@M 15 0.804350 866.378148 \n", - "7 17 1.000000 821.387362 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#| hide\n", "txt = StringIO(\"\"\"filename\tscannr\tpeptide\tstripped_peptide\tproteins\tis_decoy\tcharge\trt\tion_mobility\tspectrum_q\tpeptide_q\tprotein_q\tsage_discriminant_score\n", @@ -423,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ From 1d409eb977f2eb63301d95766cbd06a2ece5943c Mon Sep 17 00:00:00 2001 From: jalew188 Date: Wed, 24 Jul 2024 11:54:39 +0200 Subject: [PATCH 50/53] #208 comment protein-level decoy, as no packages use it yet. --- alphabase/protein/fasta.py | 2 +- alphabase/protein/protein_level_decoy.py | 7 +- alphabase/spectral_library/base.py | 4 +- alphabase/spectral_library/decoy.py | 8 +- docs/notebooks.rst | 6 +- .../tutorial_spectral_libraries.ipynb | 3312 +++++++++-------- tests/run_tests.sh | 12 +- 7 files changed, 1743 insertions(+), 1608 deletions(-) diff --git a/alphabase/protein/fasta.py b/alphabase/protein/fasta.py index 7eb77395..058b9460 100644 --- a/alphabase/protein/fasta.py +++ b/alphabase/protein/fasta.py @@ -745,7 +745,7 @@ def __init__( decoy : str, optional Decoy type (see :meth:`alphabase.spectral_library.base.append_decoy_sequence()`) - - `protein_reverse`: Reverse on target protein sequences + # - `protein_reverse`: Reverse on target protein sequences - `pseudo_reverse`: Pseudo-reverse on target peptide sequences - `diann`: DiaNN-like decoy - None: no decoy diff --git a/alphabase/protein/protein_level_decoy.py b/alphabase/protein/protein_level_decoy.py index 3727c11a..0ea12d76 100644 --- a/alphabase/protein/protein_level_decoy.py +++ b/alphabase/protein/protein_level_decoy.py @@ -1,7 +1,7 @@ import pandas as pd from alphabase.protein.fasta import SpecLibFasta -from alphabase.spectral_library.decoy import SpecLibDecoy, decoy_lib_provider +from alphabase.spectral_library.decoy import SpecLibDecoy class ProteinReverseDecoy(SpecLibDecoy): @@ -71,5 +71,6 @@ def _append_protein_df_to_target_lib(self): ) -def register_decoy(): - decoy_lib_provider.register("protein_reverse", ProteinReverseDecoy) +# remove "protein_reverse" decoy due to conflicting with DecoyGenerator +# def register_decoy(): +# decoy_lib_provider.register("protein_reverse", ProteinReverseDecoy) diff --git a/alphabase/spectral_library/base.py b/alphabase/spectral_library/base.py index bfc94cd3..671b333f 100644 --- a/alphabase/spectral_library/base.py +++ b/alphabase/spectral_library/base.py @@ -324,10 +324,10 @@ def append_decoy_sequence(self): ``` """ # register 'protein_reverse' to the decoy_lib_provider - from alphabase.protein.protein_level_decoy import register_decoy + # from alphabase.protein.protein_level_decoy import register_decoy from alphabase.spectral_library.decoy import decoy_lib_provider - register_decoy() + # register_decoy() decoy_lib = decoy_lib_provider.get_decoy_lib(self.decoy, self) if decoy_lib is None: diff --git a/alphabase/spectral_library/decoy.py b/alphabase/spectral_library/decoy.py index 76a02409..2389ad23 100644 --- a/alphabase/spectral_library/decoy.py +++ b/alphabase/spectral_library/decoy.py @@ -34,8 +34,8 @@ def _decoy(self, sequence: str) -> str: class DIANNDecoyGenerator(BaseDecoyGenerator): def __init__( self, - raw_AAs: str = "GAVLIFMPWSCTYHKRQENDBJOUXZ", - mutated_AAs: str = "LLLVVLLLLTSSSSLLNDQEVVVVVV", + raw_AAs: str = "GAVLIFMPWSCTYHKRQENDBJOUXZsty", + mutated_AAs: str = "LLLVVLLLLTSSSSLLNDQEVVVVVVtss", ): """ DiaNN-like decoy peptide generator @@ -45,11 +45,11 @@ def __init__( raw_AAs : str, optional AAs those DiaNN decoy from. - Defaults to 'GAVLIFMPWSCTYHKRQENDBJOUXZ'. + Defaults to 'GAVLIFMPWSCTYHKRQENDBJOUXZsty'. mutated_AAs : str, optional AAs those DiaNN decoy to. - Defaults to 'LLLVVLLLLTSSSSLLNDQEVVVVVV'. + Defaults to 'LLLVVLLLLTSSSSLLNDQEVVVVVVtss'. """ self.raw_AAs = raw_AAs diff --git a/docs/notebooks.rst b/docs/notebooks.rst index b0510a06..6fc1c3d8 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -6,9 +6,9 @@ Tutorials and notebooks about how to use AlphaBase. .. toctree:: :maxdepth: 2 - nbs/tutorial_dev_basic_definations - nbs/tutorial_dev_dataframes - nbs/tutorial_dev_spectral_libraries + tutorials/tutorial_basic_definitions + tutorials/tutorial_dataframe_structures + tutorials/tutorial_spectral_libraries nbs/library_from_fasta nbs/psm_readers nbs/library_reader diff --git a/docs/tutorials/tutorial_spectral_libraries.ipynb b/docs/tutorials/tutorial_spectral_libraries.ipynb index fa546b85..1ce72cd2 100644 --- a/docs/tutorials/tutorial_spectral_libraries.ipynb +++ b/docs/tutorials/tutorial_spectral_libraries.ipynb @@ -9,27 +9,6 @@ "This notebook introduces functionalities for spectral libraries to developers." ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.0.1'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import alphabase\n", - "alphabase.__version__" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -50,9 +29,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], "source": [ "from alphabase.protein.fasta import SpecLibFasta\n", "\n", @@ -91,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -110,7 +97,7 @@ " 'protein_id': 'xx',\n", " 'full_name': 'xx_xx',\n", " 'gene_name': 'x_x',\n", - " 'sequence': 'MACDESTYKxKFGHIKLMNPQRST'\n", + " 'sequence': 'MACDESTYKXKFGHIKLMNPQRST'\n", " },\n", "}" ] @@ -124,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -161,7 +148,7 @@ " \n", " \n", " 0\n", - " xKFGHIK\n", + " XKFGHIK\n", " 1\n", " 1\n", " False\n", @@ -205,7 +192,7 @@ " \n", " \n", " 4\n", - " ACDESTYKxK\n", + " ACDESTYKXK\n", " 1\n", " 1\n", " True\n", @@ -227,7 +214,7 @@ " \n", " \n", " 6\n", - " MACDESTYKxK\n", + " MACDESTYKXK\n", " 1\n", " 1\n", " True\n", @@ -238,7 +225,7 @@ " \n", " \n", " 7\n", - " xKFGHIKLMNPQR\n", + " XKFGHIKLMNPQR\n", " 1\n", " 2\n", " False\n", @@ -260,7 +247,7 @@ " \n", " \n", " 9\n", - " ACDESTYKxKFGHIK\n", + " ACDESTYKXKFGHIK\n", " 1\n", " 2\n", " True\n", @@ -271,7 +258,7 @@ " \n", " \n", " 10\n", - " MACDESTYKxKFGHIK\n", + " MACDESTYKXKFGHIK\n", " 1\n", " 2\n", " True\n", @@ -286,17 +273,17 @@ ], "text/plain": [ " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", - "0 xKFGHIK 1 1 False \n", + "0 XKFGHIK 1 1 False \n", "1 LMNPQRST 1 1 False \n", "2 ACDESTYK 1 0 True \n", "3 MACDESTYK 1 0 True \n", - "4 ACDESTYKxK 1 1 True \n", + "4 ACDESTYKXK 1 1 True \n", "5 FGHIKLMNPQR 0;1 1 True \n", - "6 MACDESTYKxK 1 1 True \n", - "7 xKFGHIKLMNPQR 1 2 False \n", + "6 MACDESTYKXK 1 1 True \n", + "7 XKFGHIKLMNPQR 1 2 False \n", "8 FGHIKLMNPQRST 1 2 False \n", - "9 ACDESTYKxKFGHIK 1 2 True \n", - "10 MACDESTYKxKFGHIK 1 2 True \n", + "9 ACDESTYKXKFGHIK 1 2 True \n", + "10 MACDESTYKXKFGHIK 1 2 True \n", "\n", " is_prot_cterm mods mod_sites nAA \n", "0 False 7 \n", @@ -312,7 +299,7 @@ "10 False 16 " ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -324,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -367,7 +354,7 @@ " xx\n", " xx_xx\n", " x_x\n", - " MACDESTYKxKFGHIKLMNPQRST\n", + " MACDESTYKXKFGHIKLMNPQRST\n", " \n", " \n", "\n", @@ -376,10 +363,10 @@ "text/plain": [ " protein_id full_name gene_name sequence\n", "0 yy yy_yy y_y FGHIKLMNPQR\n", - "1 xx xx_xx x_x MACDESTYKxKFGHIKLMNPQRST" + "1 xx xx_xx x_x MACDESTYKXKFGHIKLMNPQRST" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -401,12 +388,12 @@ "- `diann`: DiaNN-like decoy\n", "- None: no decoy. \n", " \n", - "Let's take `protein_reverse` as an example:" + "Let's take `diann` as an example:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -444,56 +431,56 @@ " \n", " \n", " 20\n", - " ACDESTYKxKFGHIK\n", + " MACDESTYKXKFGHIK\n", " 1\n", " 2\n", " True\n", " False\n", " \n", " \n", - " 15\n", + " 16\n", " 0\n", " \n", " \n", " 10\n", - " QPNMLKIHGF\n", - " 2\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", - " False\n", + " True\n", " True\n", " \n", " \n", - " 10\n", - " 1\n", + " 11\n", + " 0\n", " \n", " \n", " 14\n", - " FGHIKLMNPQR\n", - " 0;1\n", + " FLHIKLMNPQRTT\n", " 1\n", - " True\n", + " 2\n", + " False\n", " True\n", " \n", " \n", - " 11\n", - " 0\n", + " 13\n", + " 1\n", " \n", " \n", " 13\n", - " MACDESTYKxK\n", - " 1\n", + " FLHIKLMNPNR\n", + " 0;1\n", " 1\n", " True\n", - " False\n", + " True\n", " \n", " \n", " 11\n", - " 0\n", + " 1\n", " \n", " \n", " 1\n", - " IHGFKxK\n", - " 3\n", + " XLFGHVK\n", + " 1\n", " 1\n", " False\n", " False\n", @@ -507,126 +494,32 @@ "" ], "text/plain": [ - " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", - "20 ACDESTYKxKFGHIK 1 2 True \n", - "10 QPNMLKIHGF 2 1 False \n", - "14 FGHIKLMNPQR 0;1 1 True \n", - "13 MACDESTYKxK 1 1 True \n", - "1 IHGFKxK 3 1 False \n", + " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", + "20 MACDESTYKXKFGHIK 1 2 True \n", + "10 FGHIKLMNPQR 0;1 1 True \n", + "14 FLHIKLMNPQRTT 1 2 False \n", + "13 FLHIKLMNPNR 0;1 1 True \n", + "1 XLFGHVK 1 1 False \n", "\n", " is_prot_cterm mods mod_sites nAA decoy \n", - "20 False 15 0 \n", - "10 True 10 1 \n", - "14 True 11 0 \n", - "13 False 11 0 \n", + "20 False 16 0 \n", + "10 True 11 0 \n", + "14 True 13 1 \n", + "13 True 11 1 \n", "1 False 7 1 " ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fasta_lib.decoy = 'protein_reverse'\n", + "fasta_lib.decoy = 'diann'\n", "fasta_lib.append_decoy_sequence()\n", "fasta_lib.precursor_df.sample(5, random_state=0)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As `protein_reverse` is a protein-level decoy, the `protein_df` is changed too:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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    " - ], - "text/plain": [ - " protein_id full_name gene_name sequence decoy\n", - "0 yy yy_yy y_y FGHIKLMNPQR 0\n", - "1 xx xx_xx x_x MACDESTYKxKFGHIKLMNPQRST 0\n", - "2 REV_yy REV_yy_yy REV_y_y RQPNMLKIHGF 1\n", - "3 REV_xx REV_xx_xx REV_x_x TSRQPNMLKIHGFKxKYTSEDCAM 1" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fasta_lib.protein_df" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -638,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -676,63 +569,63 @@ " \n", " \n", " 35\n", - " MACDESTYKxK\n", - " 1\n", + " FLHIKLMNPNR\n", + " 0;1\n", " 1\n", " True\n", - " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term\n", - " 3;0\n", + " True\n", + " Acetyl@Protein_N-term;Oxidation@M\n", + " 0;7\n", " 11\n", - " 0\n", + " 1\n", " \n", " \n", " 34\n", - " MACDESTYKxK\n", - " 1\n", + " FLHIKLMNPNR\n", + " 0;1\n", " 1\n", " True\n", - " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat...\n", - " 3;0;1\n", + " True\n", + " \n", + " \n", " 11\n", - " 0\n", + " 1\n", " \n", " \n", - " 42\n", - " xKFGHIKLMNPQR\n", + " 41\n", + " FGHIKLMNPQRST\n", " 1\n", " 2\n", " False\n", - " False\n", + " True\n", " Oxidation@M\n", - " 9\n", + " 7\n", " 13\n", " 0\n", " \n", " \n", " 27\n", - " QPNMLKIHGFK\n", - " 3\n", + " MACDESTYKXK\n", " 1\n", + " 1\n", + " True\n", " False\n", - " False\n", - " \n", - " \n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", " 11\n", - " 1\n", + " 0\n", " \n", " \n", " 11\n", - " YTSEDCAM\n", - " 3\n", + " MACDESTYK\n", + " 1\n", " 0\n", - " False\n", " True\n", - " Carbamidomethyl@C\n", - " 6\n", - " 8\n", - " 1\n", + " False\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", + " 9\n", + " 0\n", " \n", " \n", "\n", @@ -740,21 +633,21 @@ ], "text/plain": [ " sequence protein_idxes miss_cleavage is_prot_nterm is_prot_cterm \\\n", - "35 MACDESTYKxK 1 1 True False \n", - "34 MACDESTYKxK 1 1 True False \n", - "42 xKFGHIKLMNPQR 1 2 False False \n", - "27 QPNMLKIHGFK 3 1 False False \n", - "11 YTSEDCAM 3 0 False True \n", + "35 FLHIKLMNPNR 0;1 1 True True \n", + "34 FLHIKLMNPNR 0;1 1 True True \n", + "41 FGHIKLMNPQRST 1 2 False True \n", + "27 MACDESTYKXK 1 1 True False \n", + "11 MACDESTYK 1 0 True False \n", "\n", " mods mod_sites nAA decoy \n", - "35 Carbamidomethyl@C;Acetyl@Protein_N-term 3;0 11 0 \n", - "34 Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat... 3;0;1 11 0 \n", - "42 Oxidation@M 9 13 0 \n", - "27 11 1 \n", - "11 Carbamidomethyl@C 6 8 1 " + "35 Acetyl@Protein_N-term;Oxidation@M 0;7 11 1 \n", + "34 11 1 \n", + "41 Oxidation@M 7 13 0 \n", + "27 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 11 0 \n", + "11 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 9 0 " ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -779,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -816,168 +709,168 @@ " \n", " \n", " \n", - " 28\n", - " QPNMLKIHGFKxK\n", - " 3\n", + " 45\n", + " MACDESTYKXKFGHIK\n", + " 1\n", " 2\n", + " True\n", " False\n", - " False\n", - " Oxidation@M;GlyGly@K\n", - " 4;6\n", - " 13\n", - " 1\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K\n", + " 0;3;9\n", + " 16\n", + " 0\n", " \n", " \n", - " 37\n", - " IHGFKxKYTSEDCAM\n", - " 3\n", + " 33\n", + " ASDESTYKXKFGHVK\n", + " 1\n", " 2\n", - " False\n", " True\n", - " Carbamidomethyl@C;Oxidation@M;GlyGly@K\n", - " 13;15;7\n", + " False\n", + " Acetyl@Protein_N-term;GlyGly@K\n", + " 0;8\n", " 15\n", " 1\n", " \n", " \n", - " 11\n", - " RQPNMLKIHGF\n", - " 2\n", + " 40\n", + " MACDESTYKXKFGHIK\n", + " 1\n", " 2\n", " True\n", + " False\n", + " Oxidation@M;Carbamidomethyl@C;GlyGly@K\n", + " 1;3;11\n", + " 16\n", + " 0\n", + " \n", + " \n", + " 26\n", + " FGHIKLMNPQRST\n", + " 1\n", + " 2\n", + " False\n", " True\n", " GlyGly@K\n", - " 7\n", - " 11\n", - " 1\n", + " 5\n", + " 13\n", + " 0\n", " \n", " \n", - " 34\n", - " TSRQPNMLKIHGFK\n", - " 3\n", - " 2\n", + " 11\n", + " MACDESTYKXK\n", + " 1\n", + " 1\n", " True\n", " False\n", - " Acetyl@Protein_N-term;Oxidation@M;GlyGly@K\n", - " 0;7;9\n", - " 14\n", - " 1\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3;9\n", + " 11\n", + " 0\n", " \n", " \n", " 2\n", - " ACDESTYKxK\n", + " ACDESTYKXK\n", " 1\n", " 1\n", " True\n", " False\n", - " Carbamidomethyl@C;GlyGly@K\n", - " 2;8\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K\n", + " 0;2;8\n", " 10\n", " 0\n", " \n", " \n", - " 30\n", - " QPNMLKIHGFKxK\n", - " 3\n", + " 32\n", + " ASDESTYKXKFGHVK\n", + " 1\n", " 2\n", - " False\n", + " True\n", " False\n", " GlyGly@K\n", - " 6\n", - " 13\n", + " 10\n", + " 15\n", " 1\n", " \n", " \n", - " 40\n", - " ACDESTYKxKFGHIK\n", + " 43\n", + " MACDESTYKXKFGHIK\n", " 1\n", " 2\n", " True\n", " False\n", - " Carbamidomethyl@C;GlyGly@K\n", - " 2;8\n", - " 15\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3;9\n", + " 16\n", " 0\n", " \n", " \n", - " 32\n", - " TSRQPNMLKIHGFK\n", - " 3\n", + " 46\n", + " MACDESTYKXKFGHIK\n", + " 1\n", " 2\n", " True\n", " False\n", - " Oxidation@M;GlyGly@K\n", - " 7;9\n", - " 14\n", - " 1\n", - " \n", + " Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K\n", + " 0;3;11\n", + " 16\n", + " 0\n", + " \n", " \n", - " 26\n", - " xKFGHIKLMNPQR\n", + " 30\n", + " XKFGHIKLMNPQR\n", " 1\n", " 2\n", " False\n", " False\n", " GlyGly@K\n", - " 2\n", + " 7\n", " 13\n", " 0\n", " \n", - " \n", - " 4\n", - " xKYTSEDCAM\n", - " 3\n", - " 1\n", - " False\n", - " True\n", - " Carbamidomethyl@C;Oxidation@M;GlyGly@K\n", - " 8;10;2\n", - " 10\n", - " 1\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", - "28 QPNMLKIHGFKxK 3 2 False \n", - "37 IHGFKxKYTSEDCAM 3 2 False \n", - "11 RQPNMLKIHGF 2 2 True \n", - "34 TSRQPNMLKIHGFK 3 2 True \n", - "2 ACDESTYKxK 1 1 True \n", - "30 QPNMLKIHGFKxK 3 2 False \n", - "40 ACDESTYKxKFGHIK 1 2 True \n", - "32 TSRQPNMLKIHGFK 3 2 True \n", - "26 xKFGHIKLMNPQR 1 2 False \n", - "4 xKYTSEDCAM 3 1 False \n", + " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", + "45 MACDESTYKXKFGHIK 1 2 True \n", + "33 ASDESTYKXKFGHVK 1 2 True \n", + "40 MACDESTYKXKFGHIK 1 2 True \n", + "26 FGHIKLMNPQRST 1 2 False \n", + "11 MACDESTYKXK 1 1 True \n", + "2 ACDESTYKXK 1 1 True \n", + "32 ASDESTYKXKFGHVK 1 2 True \n", + "43 MACDESTYKXKFGHIK 1 2 True \n", + "46 MACDESTYKXKFGHIK 1 2 True \n", + "30 XKFGHIKLMNPQR 1 2 False \n", "\n", - " is_prot_cterm mods mod_sites nAA \\\n", - "28 False Oxidation@M;GlyGly@K 4;6 13 \n", - "37 True Carbamidomethyl@C;Oxidation@M;GlyGly@K 13;15;7 15 \n", - "11 True GlyGly@K 7 11 \n", - "34 False Acetyl@Protein_N-term;Oxidation@M;GlyGly@K 0;7;9 14 \n", - "2 False Carbamidomethyl@C;GlyGly@K 2;8 10 \n", - "30 False GlyGly@K 6 13 \n", - "40 False Carbamidomethyl@C;GlyGly@K 2;8 15 \n", - "32 False Oxidation@M;GlyGly@K 7;9 14 \n", - "26 False GlyGly@K 2 13 \n", - "4 True Carbamidomethyl@C;Oxidation@M;GlyGly@K 8;10;2 10 \n", + " is_prot_cterm mods \\\n", + "45 False Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K \n", + "33 False Acetyl@Protein_N-term;GlyGly@K \n", + "40 False Oxidation@M;Carbamidomethyl@C;GlyGly@K \n", + "26 True GlyGly@K \n", + "11 False Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... \n", + "2 False Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K \n", + "32 False GlyGly@K \n", + "43 False Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... \n", + "46 False Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K \n", + "30 False GlyGly@K \n", "\n", - " decoy \n", - "28 1 \n", - "37 1 \n", - "11 1 \n", - "34 1 \n", - "2 0 \n", - "30 1 \n", - "40 0 \n", - "32 1 \n", - "26 0 \n", - "4 1 " + " mod_sites nAA decoy \n", + "45 0;3;9 16 0 \n", + "33 0;8 15 1 \n", + "40 1;3;11 16 0 \n", + "26 5 13 0 \n", + "11 0;1;3;9 11 0 \n", + "2 0;2;8 10 0 \n", + "32 10 15 1 \n", + "43 0;1;3;9 16 0 \n", + "46 0;3;11 16 0 \n", + "30 7 13 0 " ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1002,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1040,178 +933,178 @@ " \n", " \n", " \n", - " 26\n", - " xKFGHIKLMNPQR\n", + " 85\n", + " XKFGHIKLMNPQR\n", " 1\n", " 2\n", " False\n", " False\n", - " GlyGly@K;Dimethyl@Any_N-term;Dimethyl@K;Dimeth...\n", - " 2;0;2;7\n", + " GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H...\n", + " 7;0;2;7\n", " 13\n", " 0\n", - " 0\n", + " 4\n", " \n", " \n", - " 61\n", - " QPNMLKIHGFK\n", - " 3\n", + " 10\n", + " MACDESTYKXK\n", " 1\n", + " 1\n", + " True\n", " False\n", - " False\n", - " GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H...\n", - " 6;0;6;11\n", + " Carbamidomethyl@C;GlyGly@K;Dimethyl@Any_N-term...\n", + " 3;9;0;9;11\n", + " 11\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 75\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term;GlyGly@K;Dimethyl:2H(4)@K\n", + " 0;5;5\n", " 11\n", " 1\n", " 4\n", " \n", " \n", " 2\n", - " ACDESTYKxK\n", + " ACDESTYKXK\n", " 1\n", " 1\n", " True\n", " False\n", - " Carbamidomethyl@C;GlyGly@K;Dimethyl@Any_N-term...\n", - " 2;8;0;8;10\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly...\n", + " 0;2;8;8;10\n", " 10\n", " 0\n", " 0\n", " \n", " \n", - " 62\n", - " RQPNMLKIHGF\n", - " 2\n", + " 24\n", + " XLFGHIKLMNPNR\n", + " 1\n", " 2\n", - " True\n", - " True\n", - " Oxidation@M;GlyGly@K;Dimethyl:2H(4)@Any_N-term...\n", - " 5;7;0;7\n", - " 11\n", + " False\n", + " False\n", + " GlyGly@K;Dimethyl@Any_N-term;Dimethyl@K\n", + " 7;0;7\n", + " 13\n", " 1\n", - " 4\n", + " 0\n", " \n", " \n", - " 85\n", - " TSRQPNMLKIHGFK\n", - " 3\n", + " 101\n", + " MACDESTYKXKFGHIK\n", + " 1\n", " 2\n", " True\n", " False\n", - " GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H...\n", - " 9;0;9;14\n", - " 14\n", - " 1\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly...\n", + " 0;3;11;9;11;16\n", + " 16\n", + " 0\n", " 4\n", " \n", " \n", - " 48\n", - " MACDESTYKxKFGHIK\n", + " 109\n", + " MLCDESTYKXKFGHVK\n", " 1\n", " 2\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat...\n", - " 3;0;1;9;9;11;16\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly...\n", + " 0;3;11;9;11;16\n", " 16\n", - " 0\n", - " 0\n", + " 1\n", + " 4\n", " \n", " \n", - " 16\n", - " MACDESTYKxK\n", - " 1\n", + " 7\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", " True\n", - " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat...\n", - " 3;0;1;9;9;11\n", + " True\n", + " Acetyl@Protein_N-term;Oxidation@M;GlyGly@K;Dim...\n", + " 0;7;5;5\n", " 11\n", " 0\n", " 0\n", " \n", " \n", - " 99\n", - " MACDESTYKxKFGHIK\n", + " 16\n", + " MLCDESTYKVK\n", " 1\n", - " 2\n", - " True\n", - " False\n", - " Carbamidomethyl@C;GlyGly@K;Dimethyl:2H(4)@Any ...\n", - " 3;11;0;9;11;16\n", - " 16\n", - " 0\n", - " 4\n", - " \n", - " \n", - " 56\n", - " xKYTSEDCAM\n", - " 3\n", " 1\n", - " False\n", " True\n", - " Carbamidomethyl@C;Oxidation@M;GlyGly@K;Dimethy...\n", - " 8;10;2;0;2\n", - " 10\n", + " False\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly...\n", + " 0;3;9;9;11\n", + " 11\n", " 1\n", - " 4\n", + " 0\n", " \n", " \n", - " 45\n", - " MACDESTYKxKFGHIK\n", + " 91\n", + " ACDESTYKXKFGHIK\n", " 1\n", " 2\n", " True\n", " False\n", - " Carbamidomethyl@C;Oxidation@M;GlyGly@K;Dimethy...\n", - " 3;1;11;0;9;11;16\n", - " 16\n", - " 0\n", + " Carbamidomethyl@C;GlyGly@K;Dimethyl:2H(4)@Any_...\n", + " 2;10;0;8;10;15\n", + " 15\n", " 0\n", + " 4\n", " \n", " \n", "\n", "" ], "text/plain": [ - " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", - "26 xKFGHIKLMNPQR 1 2 False \n", - "61 QPNMLKIHGFK 3 1 False \n", - "2 ACDESTYKxK 1 1 True \n", - "62 RQPNMLKIHGF 2 2 True \n", - "85 TSRQPNMLKIHGFK 3 2 True \n", - "48 MACDESTYKxKFGHIK 1 2 True \n", - "16 MACDESTYKxK 1 1 True \n", - "99 MACDESTYKxKFGHIK 1 2 True \n", - "56 xKYTSEDCAM 3 1 False \n", - "45 MACDESTYKxKFGHIK 1 2 True \n", + " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", + "85 XKFGHIKLMNPQR 1 2 False \n", + "10 MACDESTYKXK 1 1 True \n", + "75 FLHIKLMNPNR 0;1 1 True \n", + "2 ACDESTYKXK 1 1 True \n", + "24 XLFGHIKLMNPNR 1 2 False \n", + "101 MACDESTYKXKFGHIK 1 2 True \n", + "109 MLCDESTYKXKFGHVK 1 2 True \n", + "7 FGHIKLMNPQR 0;1 1 True \n", + "16 MLCDESTYKVK 1 1 True \n", + "91 ACDESTYKXKFGHIK 1 2 True \n", "\n", - " is_prot_cterm mods \\\n", - "26 False GlyGly@K;Dimethyl@Any_N-term;Dimethyl@K;Dimeth... \n", - "61 False GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H... \n", - "2 False Carbamidomethyl@C;GlyGly@K;Dimethyl@Any_N-term... \n", - "62 True Oxidation@M;GlyGly@K;Dimethyl:2H(4)@Any_N-term... \n", - "85 False GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H... \n", - "48 False Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat... \n", - "16 False Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat... \n", - "99 False Carbamidomethyl@C;GlyGly@K;Dimethyl:2H(4)@Any ... \n", - "56 True Carbamidomethyl@C;Oxidation@M;GlyGly@K;Dimethy... \n", - "45 False Carbamidomethyl@C;Oxidation@M;GlyGly@K;Dimethy... \n", + " is_prot_cterm mods \\\n", + "85 False GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H... \n", + "10 False Carbamidomethyl@C;GlyGly@K;Dimethyl@Any_N-term... \n", + "75 True Acetyl@Protein_N-term;GlyGly@K;Dimethyl:2H(4)@K \n", + "2 False Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly... \n", + "24 False GlyGly@K;Dimethyl@Any_N-term;Dimethyl@K \n", + "101 False Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly... \n", + "109 False Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly... \n", + "7 True Acetyl@Protein_N-term;Oxidation@M;GlyGly@K;Dim... \n", + "16 False Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly... \n", + "91 False Carbamidomethyl@C;GlyGly@K;Dimethyl:2H(4)@Any_... \n", "\n", - " mod_sites nAA decoy labeling_channel \n", - "26 2;0;2;7 13 0 0 \n", - "61 6;0;6;11 11 1 4 \n", - "2 2;8;0;8;10 10 0 0 \n", - "62 5;7;0;7 11 1 4 \n", - "85 9;0;9;14 14 1 4 \n", - "48 3;0;1;9;9;11;16 16 0 0 \n", - "16 3;0;1;9;9;11 11 0 0 \n", - "99 3;11;0;9;11;16 16 0 4 \n", - "56 8;10;2;0;2 10 1 4 \n", - "45 3;1;11;0;9;11;16 16 0 0 " + " mod_sites nAA decoy labeling_channel \n", + "85 7;0;2;7 13 0 4 \n", + "10 3;9;0;9;11 11 0 0 \n", + "75 0;5;5 11 1 4 \n", + "2 0;2;8;8;10 10 0 0 \n", + "24 7;0;7 13 1 0 \n", + "101 0;3;11;9;11;16 16 0 4 \n", + "109 0;3;11;9;11;16 16 1 4 \n", + "7 0;7;5;5 11 0 0 \n", + "16 0;3;9;9;11 11 1 0 \n", + "91 2;10;0;8;10;15 15 0 4 " ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1234,7 +1127,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1273,103 +1166,103 @@ " \n", " \n", " \n", - " 65\n", - " FGHIKLMNPQR\n", - " 0;1\n", + " 122\n", + " MACDESTYKXKFGHIK\n", " 1\n", + " 2\n", " True\n", - " True\n", - " Acetyl@Protein_N-term;GlyGly@K;Dimethyl@K\n", - " 0;5;5\n", - " 11\n", + " False\n", + " Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy...\n", + " 1;3;11;0;9;11;16\n", + " 16\n", " 0\n", " 0\n", " 4\n", " \n", " \n", - " 225\n", - " FGHIKLMNPQRST\n", + " 66\n", + " FLHIKLMNPQRTT\n", " 1\n", " 2\n", " False\n", " True\n", - " GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H...\n", + " GlyGly@K;Dimethyl@Any_N-term;Dimethyl@K\n", " 5;0;5\n", " 13\n", + " 1\n", " 0\n", - " 4\n", " 2\n", " \n", " \n", - " 188\n", - " RQPNMLKIHGF\n", - " 2\n", + " 142\n", + " MLCDESTYKXKFGHVK\n", + " 1\n", " 2\n", " True\n", - " True\n", - " Oxidation@M;GlyGly@K;Dimethyl:2H(4)@Any_N-term...\n", - " 5;7;0;7\n", - " 11\n", + " False\n", + " Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy...\n", + " 1;3;9;0;9;11;16\n", + " 16\n", " 1\n", - " 4\n", - " 4\n", + " 0\n", + " 3\n", " \n", " \n", - " 200\n", - " MACDESTYKxK\n", + " 246\n", + " XKFGHIKLMNPQR\n", " 1\n", - " 1\n", - " True\n", + " 2\n", " False\n", - " Carbamidomethyl@C;Oxidation@M;GlyGly@K;Dimethy...\n", - " 3;1;9;0;9;11\n", - " 11\n", + " False\n", + " Oxidation@M;GlyGly@K;Dimethyl:2H(4)@Any_N-term...\n", + " 9;2;0;2;7\n", + " 13\n", " 0\n", " 4\n", - " 4\n", + " 2\n", " \n", " \n", - " 108\n", - " IHGFKxKYTSEDCAM\n", - " 3\n", + " 146\n", + " MLCDESTYKXKFGHVK\n", + " 1\n", " 2\n", - " False\n", " True\n", - " Carbamidomethyl@C;Oxidation@M;GlyGly@K;Dimethy...\n", - " 13;15;5;0;5;7\n", - " 15\n", + " False\n", + " Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy...\n", + " 1;3;11;0;9;11;16\n", + " 16\n", " 1\n", " 0\n", - " 2\n", + " 4\n", " \n", " \n", "\n", "" ], "text/plain": [ - " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", - "65 FGHIKLMNPQR 0;1 1 True \n", - "225 FGHIKLMNPQRST 1 2 False \n", - "188 RQPNMLKIHGF 2 2 True \n", - "200 MACDESTYKxK 1 1 True \n", - "108 IHGFKxKYTSEDCAM 3 2 False \n", + " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", + "122 MACDESTYKXKFGHIK 1 2 True \n", + "66 FLHIKLMNPQRTT 1 2 False \n", + "142 MLCDESTYKXKFGHVK 1 2 True \n", + "246 XKFGHIKLMNPQR 1 2 False \n", + "146 MLCDESTYKXKFGHVK 1 2 True \n", "\n", " is_prot_cterm mods \\\n", - "65 True Acetyl@Protein_N-term;GlyGly@K;Dimethyl@K \n", - "225 True GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H... \n", - "188 True Oxidation@M;GlyGly@K;Dimethyl:2H(4)@Any_N-term... \n", - "200 False Carbamidomethyl@C;Oxidation@M;GlyGly@K;Dimethy... \n", - "108 True Carbamidomethyl@C;Oxidation@M;GlyGly@K;Dimethy... \n", + "122 False Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy... \n", + "66 True GlyGly@K;Dimethyl@Any_N-term;Dimethyl@K \n", + "142 False Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy... \n", + "246 False Oxidation@M;GlyGly@K;Dimethyl:2H(4)@Any_N-term... \n", + "146 False Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy... \n", "\n", - " mod_sites nAA decoy labeling_channel charge \n", - "65 0;5;5 11 0 0 4 \n", - "225 5;0;5 13 0 4 2 \n", - "188 5;7;0;7 11 1 4 4 \n", - "200 3;1;9;0;9;11 11 0 4 4 \n", - "108 13;15;5;0;5;7 15 1 0 2 " + " mod_sites nAA decoy labeling_channel charge \n", + "122 1;3;11;0;9;11;16 16 0 0 4 \n", + "66 5;0;5 13 1 0 2 \n", + "142 1;3;9;0;9;11;16 16 1 0 3 \n", + "246 9;2;0;2;7 13 0 4 2 \n", + "146 1;3;11;0;9;11;16 16 1 0 4 " ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1390,7 +1283,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1418,7 +1311,6 @@ " full_name\n", " gene_name\n", " sequence\n", - " decoy\n", " \n", " \n", " \n", @@ -1428,45 +1320,25 @@ " yy_yy\n", " y_y\n", " FGHIKLMNPQR\n", - " 0\n", " \n", " \n", " 1\n", " xx\n", " xx_xx\n", " x_x\n", - " MACDESTYKxKFGHIKLMNPQRST\n", - " 0\n", - " \n", - " \n", - " 2\n", - " REV_yy\n", - " REV_yy_yy\n", - " REV_y_y\n", - " RQPNMLKIHGF\n", - " 1\n", - " \n", - " \n", - " 3\n", - " REV_xx\n", - " REV_xx_xx\n", - " REV_x_x\n", - " TSRQPNMLKIHGFKxKYTSEDCAM\n", - " 1\n", + " MACDESTYKXKFGHIKLMNPQRST\n", " \n", " \n", "\n", "" ], "text/plain": [ - " protein_id full_name gene_name sequence decoy\n", - "0 yy yy_yy y_y FGHIKLMNPQR 0\n", - "1 xx xx_xx x_x MACDESTYKxKFGHIKLMNPQRST 0\n", - "2 REV_yy REV_yy_yy REV_y_y RQPNMLKIHGF 1\n", - "3 REV_xx REV_xx_xx REV_x_x TSRQPNMLKIHGFKxKYTSEDCAM 1" + " protein_id full_name gene_name sequence\n", + "0 yy yy_yy y_y FGHIKLMNPQR\n", + "1 xx xx_xx x_x MACDESTYKXKFGHIKLMNPQRST" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1480,7 +1352,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1514,201 +1386,890 @@ " nAA\n", " decoy\n", " charge\n", + " precursor_mz\n", " \n", " \n", " \n", " \n", " 0\n", - " xKFGHIK\n", + " LMNPQRST\n", " 1\n", " 1\n", " False\n", - " False\n", - " \n", - " \n", - " 7\n", + " True\n", + " Oxidation@M\n", + " 2\n", + " 8\n", " 0\n", " 2\n", + " 481.739834\n", " \n", " \n", " 1\n", - " xKFGHIK\n", + " LMNPQRST\n", " 1\n", " 1\n", " False\n", - " False\n", + " True\n", " \n", " \n", - " 7\n", + " 8\n", " 0\n", - " 3\n", + " 2\n", + " 473.742377\n", " \n", " \n", " 2\n", - " xKFGHIK\n", - " 1\n", + " ACDESTYK\n", " 1\n", + " 0\n", + " True\n", " False\n", - " False\n", - " \n", - " \n", - " 7\n", + " Carbamidomethyl@C\n", + " 2\n", + " 8\n", " 0\n", - " 4\n", + " 2\n", + " 487.200207\n", " \n", " \n", " 3\n", - " IHGFKxK\n", - " 3\n", + " ACDESTYK\n", " 1\n", + " 0\n", + " True\n", " False\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;2\n", + " 8\n", + " 0\n", + " 2\n", + " 508.205490\n", + " \n", + " \n", + " 4\n", + " LLNPQRTT\n", + " 1\n", + " 1\n", " False\n", + " True\n", " \n", " \n", - " 7\n", + " 8\n", " 1\n", " 2\n", + " 471.771991\n", " \n", " \n", - " 4\n", - " IHGFKxK\n", - " 3\n", + " 5\n", + " ASDESTSK\n", " 1\n", - " False\n", + " 0\n", + " True\n", " False\n", " \n", " \n", - " 7\n", + " 8\n", " 1\n", - " 3\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 2\n", + " 412.685247\n", " \n", " \n", - " 169\n", - " MACDESTYKxKFGHIK\n", + " 6\n", + " ASDESTSK\n", " 1\n", - " 2\n", + " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat...\n", - " 3;0;1\n", - " 16\n", + " Acetyl@Protein_N-term\n", " 0\n", - " 3\n", + " 8\n", + " 1\n", + " 2\n", + " 433.690529\n", " \n", " \n", - " 170\n", - " MACDESTYKxKFGHIK\n", + " 7\n", + " MACDESTYK\n", " 1\n", - " 2\n", + " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat...\n", - " 3;0;1\n", - " 16\n", + " Oxidation@M;Carbamidomethyl@C\n", + " 1;3\n", + " 9\n", " 0\n", - " 4\n", + " 2\n", + " 560.717907\n", " \n", " \n", - " 171\n", - " MACDESTYKxKFGHIK\n", + " 8\n", + " MACDESTYK\n", " 1\n", + " 0\n", + " True\n", + " False\n", + " Carbamidomethyl@C\n", + " 3\n", + " 9\n", + " 0\n", " 2\n", + " 552.720450\n", + " \n", + " \n", + " 9\n", + " MACDESTYK\n", + " 1\n", + " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term\n", - " 3;0\n", - " 16\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", + " 9\n", " 0\n", " 2\n", + " 581.723190\n", " \n", " \n", - " 172\n", - " MACDESTYKxKFGHIK\n", + " 10\n", + " MACDESTYK\n", " 1\n", + " 0\n", + " True\n", + " False\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;3\n", + " 9\n", + " 0\n", " 2\n", + " 573.725732\n", + " \n", + " \n", + " 11\n", + " MLCDESTSK\n", + " 1\n", + " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term\n", - " 3;0\n", - " 16\n", + " Oxidation@M;Carbamidomethyl@C\n", + " 1;3\n", + " 9\n", + " 1\n", + " 2\n", + " 543.725732\n", + " \n", + " \n", + " 12\n", + " MLCDESTSK\n", + " 1\n", " 0\n", + " True\n", + " False\n", + " Carbamidomethyl@C\n", " 3\n", + " 9\n", + " 1\n", + " 2\n", + " 535.728275\n", " \n", " \n", - " 173\n", - " MACDESTYKxKFGHIK\n", + " 13\n", + " MLCDESTSK\n", + " 1\n", + " 0\n", + " True\n", + " False\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", + " 9\n", " 1\n", " 2\n", + " 564.731015\n", + " \n", + " \n", + " 14\n", + " MLCDESTSK\n", + " 1\n", + " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term\n", - " 3;0\n", - " 16\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;3\n", + " 9\n", + " 1\n", + " 2\n", + " 556.733557\n", + " \n", + " \n", + " 15\n", + " ASDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " \n", + " \n", + " 10\n", + " 1\n", + " 2\n", + " 564.282586\n", + " \n", + " \n", + " 16\n", + " ASDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Acetyl@Protein_N-term\n", + " 0\n", + " 10\n", + " 1\n", + " 2\n", + " 585.287868\n", + " \n", + " \n", + " 17\n", + " FGHIKLMNPQR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 11\n", + " 0\n", + " 2\n", + " 678.863889\n", + " \n", + " \n", + " 18\n", + " FGHIKLMNPQR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 11\n", + " 0\n", + " 3\n", + " 452.911685\n", + " \n", + " \n", + " 19\n", + " FGHIKLMNPQR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " \n", + " \n", + " 11\n", + " 0\n", + " 2\n", + " 670.866431\n", + " \n", + " \n", + " 20\n", + " FGHIKLMNPQR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " \n", + " \n", + " 11\n", + " 0\n", + " 3\n", + " 447.580046\n", + " \n", + " \n", + " 21\n", + " FGHIKLMNPQR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term;Oxidation@M\n", + " 0;7\n", + " 11\n", + " 0\n", + " 2\n", + " 699.869171\n", + " \n", + " \n", + " 22\n", + " FGHIKLMNPQR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term;Oxidation@M\n", + " 0;7\n", + " 11\n", + " 0\n", + " 3\n", + " 466.915206\n", + " \n", + " \n", + " 23\n", + " FGHIKLMNPQR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term\n", + " 0\n", + " 11\n", + " 0\n", + " 2\n", + " 691.871714\n", + " \n", + " \n", + " 24\n", + " FGHIKLMNPQR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term\n", + " 0\n", + " 11\n", + " 0\n", + " 3\n", + " 461.583568\n", + " \n", + " \n", + " 25\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Oxidation@M;Carbamidomethyl@C\n", + " 1;3\n", + " 11\n", + " 1\n", + " 2\n", + " 695.323071\n", + " \n", + " \n", + " 26\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Oxidation@M;Carbamidomethyl@C\n", + " 1;3\n", + " 11\n", + " 1\n", + " 3\n", + " 463.884473\n", + " \n", + " \n", + " 27\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Carbamidomethyl@C\n", + " 3\n", + " 11\n", + " 1\n", + " 2\n", + " 687.325613\n", + " \n", + " \n", + " 28\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Carbamidomethyl@C\n", + " 3\n", + " 11\n", + " 1\n", + " 3\n", + " 458.552834\n", + " \n", + " \n", + " 29\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", + " 11\n", + " 1\n", + " 2\n", + " 716.328353\n", + " \n", + " \n", + " 30\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", + " 11\n", + " 1\n", + " 3\n", + " 477.887994\n", + " \n", + " \n", + " 31\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;3\n", + " 11\n", + " 1\n", + " 2\n", + " 708.330896\n", + " \n", + " \n", + " 32\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", + " True\n", + " False\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;3\n", + " 11\n", + " 1\n", + " 3\n", + " 472.556356\n", + " \n", + " \n", + " 33\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 11\n", + " 1\n", + " 2\n", + " 699.887364\n", + " \n", + " \n", + " 34\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 11\n", + " 1\n", + " 3\n", + " 466.927335\n", + " \n", + " \n", + " 35\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " \n", + " \n", + " 11\n", + " 1\n", + " 2\n", + " 691.889907\n", + " \n", + " \n", + " 36\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " \n", + " \n", + " 11\n", + " 1\n", + " 3\n", + " 461.595697\n", + " \n", + " \n", + " 37\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term;Oxidation@M\n", + " 0;7\n", + " 11\n", + " 1\n", + " 2\n", + " 720.892646\n", + " \n", + " \n", + " 38\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term;Oxidation@M\n", + " 0;7\n", + " 11\n", + " 1\n", + " 3\n", + " 480.930856\n", + " \n", + " \n", + " 39\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term\n", + " 0\n", + " 11\n", + " 1\n", + " 2\n", + " 712.895189\n", + " \n", + " \n", + " 40\n", + " FLHIKLMNPNR\n", + " 0;1\n", + " 1\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term\n", " 0\n", + " 11\n", + " 1\n", + " 3\n", + " 475.599218\n", + " \n", + " \n", + " 41\n", + " FLHIKLMNPQRTT\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 13\n", + " 1\n", + " 2\n", + " 807.942867\n", + " \n", + " \n", + " 42\n", + " FLHIKLMNPQRTT\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 13\n", + " 1\n", + " 3\n", + " 538.964337\n", + " \n", + " \n", + " 43\n", + " FLHIKLMNPQRTT\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 13\n", + " 1\n", + " 4\n", + " 404.475072\n", + " \n", + " \n", + " 44\n", + " FLHIKLMNPQRTT\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " \n", + " \n", + " 13\n", + " 1\n", + " 2\n", + " 799.945410\n", + " \n", + " \n", + " 45\n", + " FLHIKLMNPQRTT\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " \n", + " \n", + " 13\n", + " 1\n", + " 3\n", + " 533.632699\n", + " \n", + " \n", + " 46\n", + " FLHIKLMNPQRTT\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " \n", + " \n", + " 13\n", + " 1\n", " 4\n", + " 400.476343\n", + " \n", + " \n", + " 47\n", + " FGHIKLMNPQRST\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 13\n", + " 0\n", + " 2\n", + " 772.903742\n", + " \n", + " \n", + " 48\n", + " FGHIKLMNPQRST\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 13\n", + " 0\n", + " 3\n", + " 515.604920\n", + " \n", + " \n", + " 49\n", + " FGHIKLMNPQRST\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " \n", + " \n", + " 13\n", + " 0\n", + " 2\n", + " 764.906285\n", + " \n", + " \n", + " 50\n", + " FGHIKLMNPQRST\n", + " 1\n", + " 2\n", + " False\n", + " True\n", + " \n", + " \n", + " 13\n", + " 0\n", + " 3\n", + " 510.273282\n", " \n", " \n", "\n", - "

    174 rows × 10 columns

    \n", "" ], "text/plain": [ - " sequence protein_idxes miss_cleavage is_prot_nterm \\\n", - "0 xKFGHIK 1 1 False \n", - "1 xKFGHIK 1 1 False \n", - "2 xKFGHIK 1 1 False \n", - "3 IHGFKxK 3 1 False \n", - "4 IHGFKxK 3 1 False \n", - ".. ... ... ... ... \n", - "169 MACDESTYKxKFGHIK 1 2 True \n", - "170 MACDESTYKxKFGHIK 1 2 True \n", - "171 MACDESTYKxKFGHIK 1 2 True \n", - "172 MACDESTYKxKFGHIK 1 2 True \n", - "173 MACDESTYKxKFGHIK 1 2 True \n", - "\n", - " is_prot_cterm mods \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - ".. ... ... \n", - "169 False Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat... \n", - "170 False Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat... \n", - "171 False Carbamidomethyl@C;Acetyl@Protein_N-term \n", - "172 False Carbamidomethyl@C;Acetyl@Protein_N-term \n", - "173 False Carbamidomethyl@C;Acetyl@Protein_N-term \n", + " sequence protein_idxes miss_cleavage is_prot_nterm is_prot_cterm \\\n", + "0 LMNPQRST 1 1 False True \n", + "1 LMNPQRST 1 1 False True \n", + "2 ACDESTYK 1 0 True False \n", + "3 ACDESTYK 1 0 True False \n", + "4 LLNPQRTT 1 1 False True \n", + "5 ASDESTSK 1 0 True False \n", + "6 ASDESTSK 1 0 True False \n", + "7 MACDESTYK 1 0 True False \n", + "8 MACDESTYK 1 0 True False \n", + "9 MACDESTYK 1 0 True False \n", + "10 MACDESTYK 1 0 True False \n", + "11 MLCDESTSK 1 0 True False \n", + "12 MLCDESTSK 1 0 True False \n", + "13 MLCDESTSK 1 0 True False \n", + "14 MLCDESTSK 1 0 True False \n", + "15 ASDESTYKVK 1 1 True False \n", + "16 ASDESTYKVK 1 1 True False \n", + "17 FGHIKLMNPQR 0;1 1 True True \n", + "18 FGHIKLMNPQR 0;1 1 True True \n", + "19 FGHIKLMNPQR 0;1 1 True True \n", + "20 FGHIKLMNPQR 0;1 1 True True \n", + "21 FGHIKLMNPQR 0;1 1 True True \n", + "22 FGHIKLMNPQR 0;1 1 True True \n", + "23 FGHIKLMNPQR 0;1 1 True True \n", + "24 FGHIKLMNPQR 0;1 1 True True \n", + "25 MLCDESTYKVK 1 1 True False \n", + "26 MLCDESTYKVK 1 1 True False \n", + "27 MLCDESTYKVK 1 1 True False \n", + "28 MLCDESTYKVK 1 1 True False \n", + "29 MLCDESTYKVK 1 1 True False \n", + "30 MLCDESTYKVK 1 1 True False \n", + "31 MLCDESTYKVK 1 1 True False \n", + "32 MLCDESTYKVK 1 1 True False \n", + "33 FLHIKLMNPNR 0;1 1 True True \n", + "34 FLHIKLMNPNR 0;1 1 True True \n", + "35 FLHIKLMNPNR 0;1 1 True True \n", + "36 FLHIKLMNPNR 0;1 1 True True \n", + "37 FLHIKLMNPNR 0;1 1 True True \n", + "38 FLHIKLMNPNR 0;1 1 True True \n", + "39 FLHIKLMNPNR 0;1 1 True True \n", + "40 FLHIKLMNPNR 0;1 1 True True \n", + "41 FLHIKLMNPQRTT 1 2 False True \n", + "42 FLHIKLMNPQRTT 1 2 False True \n", + "43 FLHIKLMNPQRTT 1 2 False True \n", + "44 FLHIKLMNPQRTT 1 2 False True \n", + "45 FLHIKLMNPQRTT 1 2 False True \n", + "46 FLHIKLMNPQRTT 1 2 False True \n", + "47 FGHIKLMNPQRST 1 2 False True \n", + "48 FGHIKLMNPQRST 1 2 False True \n", + "49 FGHIKLMNPQRST 1 2 False True \n", + "50 FGHIKLMNPQRST 1 2 False True \n", "\n", - " mod_sites nAA decoy charge \n", - "0 7 0 2 \n", - "1 7 0 3 \n", - "2 7 0 4 \n", - "3 7 1 2 \n", - "4 7 1 3 \n", - ".. ... ... ... ... \n", - "169 3;0;1 16 0 3 \n", - "170 3;0;1 16 0 4 \n", - "171 3;0 16 0 2 \n", - "172 3;0 16 0 3 \n", - "173 3;0 16 0 4 \n", + " mods mod_sites nAA decoy \\\n", + "0 Oxidation@M 2 8 0 \n", + "1 8 0 \n", + "2 Carbamidomethyl@C 2 8 0 \n", + "3 Acetyl@Protein_N-term;Carbamidomethyl@C 0;2 8 0 \n", + "4 8 1 \n", + "5 8 1 \n", + "6 Acetyl@Protein_N-term 0 8 1 \n", + "7 Oxidation@M;Carbamidomethyl@C 1;3 9 0 \n", + "8 Carbamidomethyl@C 3 9 0 \n", + "9 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 9 0 \n", + "10 Acetyl@Protein_N-term;Carbamidomethyl@C 0;3 9 0 \n", + "11 Oxidation@M;Carbamidomethyl@C 1;3 9 1 \n", + "12 Carbamidomethyl@C 3 9 1 \n", + "13 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 9 1 \n", + "14 Acetyl@Protein_N-term;Carbamidomethyl@C 0;3 9 1 \n", + "15 10 1 \n", + "16 Acetyl@Protein_N-term 0 10 1 \n", + "17 Oxidation@M 7 11 0 \n", + "18 Oxidation@M 7 11 0 \n", + "19 11 0 \n", + "20 11 0 \n", + "21 Acetyl@Protein_N-term;Oxidation@M 0;7 11 0 \n", + "22 Acetyl@Protein_N-term;Oxidation@M 0;7 11 0 \n", + "23 Acetyl@Protein_N-term 0 11 0 \n", + "24 Acetyl@Protein_N-term 0 11 0 \n", + "25 Oxidation@M;Carbamidomethyl@C 1;3 11 1 \n", + "26 Oxidation@M;Carbamidomethyl@C 1;3 11 1 \n", + "27 Carbamidomethyl@C 3 11 1 \n", + "28 Carbamidomethyl@C 3 11 1 \n", + "29 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 11 1 \n", + "30 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 11 1 \n", + "31 Acetyl@Protein_N-term;Carbamidomethyl@C 0;3 11 1 \n", + "32 Acetyl@Protein_N-term;Carbamidomethyl@C 0;3 11 1 \n", + "33 Oxidation@M 7 11 1 \n", + "34 Oxidation@M 7 11 1 \n", + "35 11 1 \n", + "36 11 1 \n", + "37 Acetyl@Protein_N-term;Oxidation@M 0;7 11 1 \n", + "38 Acetyl@Protein_N-term;Oxidation@M 0;7 11 1 \n", + "39 Acetyl@Protein_N-term 0 11 1 \n", + "40 Acetyl@Protein_N-term 0 11 1 \n", + "41 Oxidation@M 7 13 1 \n", + "42 Oxidation@M 7 13 1 \n", + "43 Oxidation@M 7 13 1 \n", + "44 13 1 \n", + "45 13 1 \n", + "46 13 1 \n", + "47 Oxidation@M 7 13 0 \n", + "48 Oxidation@M 7 13 0 \n", + "49 13 0 \n", + "50 13 0 \n", "\n", - "[174 rows x 10 columns]" + " charge precursor_mz \n", + "0 2 481.739834 \n", + "1 2 473.742377 \n", + "2 2 487.200207 \n", + "3 2 508.205490 \n", + "4 2 471.771991 \n", + "5 2 412.685247 \n", + "6 2 433.690529 \n", + "7 2 560.717907 \n", + "8 2 552.720450 \n", + "9 2 581.723190 \n", + "10 2 573.725732 \n", + "11 2 543.725732 \n", + "12 2 535.728275 \n", + "13 2 564.731015 \n", + "14 2 556.733557 \n", + "15 2 564.282586 \n", + "16 2 585.287868 \n", + "17 2 678.863889 \n", + "18 3 452.911685 \n", + "19 2 670.866431 \n", + "20 3 447.580046 \n", + "21 2 699.869171 \n", + "22 3 466.915206 \n", + "23 2 691.871714 \n", + "24 3 461.583568 \n", + "25 2 695.323071 \n", + "26 3 463.884473 \n", + "27 2 687.325613 \n", + "28 3 458.552834 \n", + "29 2 716.328353 \n", + "30 3 477.887994 \n", + "31 2 708.330896 \n", + "32 3 472.556356 \n", + "33 2 699.887364 \n", + "34 3 466.927335 \n", + "35 2 691.889907 \n", + "36 3 461.595697 \n", + "37 2 720.892646 \n", + "38 3 480.930856 \n", + "39 2 712.895189 \n", + "40 3 475.599218 \n", + "41 2 807.942867 \n", + "42 3 538.964337 \n", + "43 4 404.475072 \n", + "44 2 799.945410 \n", + "45 3 533.632699 \n", + "46 4 400.476343 \n", + "47 2 772.903742 \n", + "48 3 515.604920 \n", + "49 2 764.906285 \n", + "50 3 510.273282 " ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1730,7 +2291,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1762,6 +2323,7 @@ " mod_sites\n", " labeling_channel\n", " charge\n", + " precursor_mz\n", " \n", " \n", " \n", @@ -1775,119 +2337,10 @@ " 0\n", " 0\n", " 2\n", + " 427.248152\n", " \n", " \n", " 1\n", - " OPQRST\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl@Any_N-term\n", - " 0\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 2\n", - " OPQRST\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl@Any_N-term\n", - " 0\n", - " 0\n", - " 4\n", - " \n", - " \n", - " 3\n", - " UVWXYZ\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl@Any_N-term\n", - " 0\n", - " 0\n", - " 2\n", - " \n", - " \n", - " 4\n", - " UVWXYZ\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl@Any_N-term\n", - " 0\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 5\n", - " UVWXYZ\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl@Any_N-term\n", - " 0\n", - " 0\n", - " 4\n", - " \n", - " \n", - " 6\n", - " ABCDEFG\n", - " 7\n", - " False\n", - " False\n", - " Carbamidomethyl@C;Dimethyl@Any_N-term\n", - " 3;0\n", - " 0\n", - " 2\n", - " \n", - " \n", - " 7\n", - " ABCDEFG\n", - " 7\n", - " False\n", - " False\n", - " Carbamidomethyl@C;Dimethyl@Any_N-term\n", - " 3;0\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 8\n", - " ABCDEFG\n", - " 7\n", - " False\n", - " False\n", - " Carbamidomethyl@C;Dimethyl@Any_N-term\n", - " 3;0\n", - " 0\n", - " 4\n", - " \n", - " \n", - " 9\n", - " HIJKLMN\n", - " 7\n", - " False\n", - " False\n", - " Oxidation@M;Dimethyl@Any_N-term;Dimethyl@K\n", - " 6;0;4\n", - " 0\n", - " 2\n", - " \n", - " \n", - " 10\n", - " HIJKLMN\n", - " 7\n", - " False\n", - " False\n", - " Oxidation@M;Dimethyl@Any_N-term;Dimethyl@K\n", - " 6;0;4\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 11\n", " HIJKLMN\n", " 7\n", " False\n", @@ -1895,32 +2348,11 @@ " Oxidation@M;Dimethyl@Any_N-term;Dimethyl@K\n", " 6;0;4\n", " 0\n", - " 4\n", - " \n", - " \n", - " 12\n", - " HIJKLMN\n", - " 7\n", - " False\n", - " False\n", - " Dimethyl@Any_N-term;Dimethyl@K\n", - " 0;4\n", - " 0\n", " 2\n", + " 470.786056\n", " \n", " \n", - " 13\n", - " HIJKLMN\n", - " 7\n", - " False\n", - " False\n", - " Dimethyl@Any_N-term;Dimethyl@K\n", - " 0;4\n", - " 0\n", - " 3\n", - " \n", - " \n", - " 14\n", + " 2\n", " HIJKLMN\n", " 7\n", " False\n", @@ -1928,32 +2360,11 @@ " Dimethyl@Any_N-term;Dimethyl@K\n", " 0;4\n", " 0\n", - " 4\n", - " \n", - " \n", - " 15\n", - " OPQRST\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl:2H(4)@Any_N-term\n", - " 0\n", - " 4\n", " 2\n", + " 462.788599\n", " \n", " \n", - " 16\n", - " OPQRST\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl:2H(4)@Any_N-term\n", - " 0\n", - " 4\n", - " 3\n", - " \n", - " \n", - " 17\n", + " 3\n", " OPQRST\n", " 6\n", " False\n", @@ -1961,76 +2372,11 @@ " Dimethyl:2H(4)@Any_N-term\n", " 0\n", " 4\n", - " 4\n", - " \n", - " \n", - " 18\n", - " UVWXYZ\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl:2H(4)@Any_N-term\n", - " 0\n", - " 4\n", " 2\n", + " 429.260705\n", " \n", " \n", - " 19\n", - " UVWXYZ\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl:2H(4)@Any_N-term\n", - " 0\n", - " 4\n", - " 3\n", - " \n", - " \n", - " 20\n", - " UVWXYZ\n", - " 6\n", - " False\n", - " False\n", - " Dimethyl:2H(4)@Any_N-term\n", - " 0\n", - " 4\n", - " 4\n", - " \n", - " \n", - " 21\n", - " ABCDEFG\n", - " 7\n", - " False\n", - " False\n", - " Carbamidomethyl@C;Dimethyl:2H(4)@Any_N-term\n", - " 3;0\n", - " 4\n", - " 2\n", - " \n", - " \n", - " 22\n", - " ABCDEFG\n", - " 7\n", - " False\n", - " False\n", - " Carbamidomethyl@C;Dimethyl:2H(4)@Any_N-term\n", - " 3;0\n", - " 4\n", - " 3\n", - " \n", - " \n", - " 23\n", - " ABCDEFG\n", - " 7\n", - " False\n", - " False\n", - " Carbamidomethyl@C;Dimethyl:2H(4)@Any_N-term\n", - " 3;0\n", - " 4\n", - " 4\n", - " \n", - " \n", - " 24\n", + " 4\n", " HIJKLMN\n", " 7\n", " False\n", @@ -2039,31 +2385,10 @@ " 6;0;4\n", " 4\n", " 2\n", + " 474.811163\n", " \n", " \n", - " 25\n", - " HIJKLMN\n", - " 7\n", - " False\n", - " False\n", - " Oxidation@M;Dimethyl:2H(4)@Any_N-term;Dimethyl...\n", - " 6;0;4\n", - " 4\n", - " 3\n", - " \n", - " \n", - " 26\n", - " HIJKLMN\n", - " 7\n", - " False\n", - " False\n", - " Oxidation@M;Dimethyl:2H(4)@Any_N-term;Dimethyl...\n", - " 6;0;4\n", - " 4\n", - " 4\n", - " \n", - " \n", - " 27\n", + " 5\n", " HIJKLMN\n", " 7\n", " False\n", @@ -2072,132 +2397,39 @@ " 0;4\n", " 4\n", " 2\n", - " \n", - " \n", - " 28\n", - " HIJKLMN\n", - " 7\n", - " False\n", - " False\n", - " Dimethyl:2H(4)@Any_N-term;Dimethyl:2H(4)@K\n", - " 0;4\n", - " 4\n", - " 3\n", - " \n", - " \n", - " 29\n", - " HIJKLMN\n", - " 7\n", - " False\n", - " False\n", - " Dimethyl:2H(4)@Any_N-term;Dimethyl:2H(4)@K\n", - " 0;4\n", - " 4\n", - " 4\n", + " 466.813706\n", " \n", " \n", "\n", "" ], "text/plain": [ - " sequence nAA is_prot_nterm is_prot_cterm \\\n", - "0 OPQRST 6 False False \n", - "1 OPQRST 6 False False \n", - "2 OPQRST 6 False False \n", - "3 UVWXYZ 6 False False \n", - "4 UVWXYZ 6 False False \n", - "5 UVWXYZ 6 False False \n", - "6 ABCDEFG 7 False False \n", - "7 ABCDEFG 7 False False \n", - "8 ABCDEFG 7 False False \n", - "9 HIJKLMN 7 False False \n", - "10 HIJKLMN 7 False False \n", - "11 HIJKLMN 7 False False \n", - "12 HIJKLMN 7 False False \n", - "13 HIJKLMN 7 False False \n", - "14 HIJKLMN 7 False False \n", - "15 OPQRST 6 False False \n", - "16 OPQRST 6 False False \n", - "17 OPQRST 6 False False \n", - "18 UVWXYZ 6 False False \n", - "19 UVWXYZ 6 False False \n", - "20 UVWXYZ 6 False False \n", - "21 ABCDEFG 7 False False \n", - "22 ABCDEFG 7 False False \n", - "23 ABCDEFG 7 False False \n", - "24 HIJKLMN 7 False False \n", - "25 HIJKLMN 7 False False \n", - "26 HIJKLMN 7 False False \n", - "27 HIJKLMN 7 False False \n", - "28 HIJKLMN 7 False False \n", - "29 HIJKLMN 7 False False \n", + " sequence nAA is_prot_nterm is_prot_cterm \\\n", + "0 OPQRST 6 False False \n", + "1 HIJKLMN 7 False False \n", + "2 HIJKLMN 7 False False \n", + "3 OPQRST 6 False False \n", + "4 HIJKLMN 7 False False \n", + "5 HIJKLMN 7 False False \n", "\n", - " mods mod_sites \\\n", - "0 Dimethyl@Any_N-term 0 \n", - "1 Dimethyl@Any_N-term 0 \n", - "2 Dimethyl@Any_N-term 0 \n", - "3 Dimethyl@Any_N-term 0 \n", - "4 Dimethyl@Any_N-term 0 \n", - "5 Dimethyl@Any_N-term 0 \n", - "6 Carbamidomethyl@C;Dimethyl@Any_N-term 3;0 \n", - "7 Carbamidomethyl@C;Dimethyl@Any_N-term 3;0 \n", - "8 Carbamidomethyl@C;Dimethyl@Any_N-term 3;0 \n", - "9 Oxidation@M;Dimethyl@Any_N-term;Dimethyl@K 6;0;4 \n", - "10 Oxidation@M;Dimethyl@Any_N-term;Dimethyl@K 6;0;4 \n", - "11 Oxidation@M;Dimethyl@Any_N-term;Dimethyl@K 6;0;4 \n", - "12 Dimethyl@Any_N-term;Dimethyl@K 0;4 \n", - "13 Dimethyl@Any_N-term;Dimethyl@K 0;4 \n", - "14 Dimethyl@Any_N-term;Dimethyl@K 0;4 \n", - "15 Dimethyl:2H(4)@Any_N-term 0 \n", - "16 Dimethyl:2H(4)@Any_N-term 0 \n", - "17 Dimethyl:2H(4)@Any_N-term 0 \n", - "18 Dimethyl:2H(4)@Any_N-term 0 \n", - "19 Dimethyl:2H(4)@Any_N-term 0 \n", - "20 Dimethyl:2H(4)@Any_N-term 0 \n", - "21 Carbamidomethyl@C;Dimethyl:2H(4)@Any_N-term 3;0 \n", - "22 Carbamidomethyl@C;Dimethyl:2H(4)@Any_N-term 3;0 \n", - "23 Carbamidomethyl@C;Dimethyl:2H(4)@Any_N-term 3;0 \n", - "24 Oxidation@M;Dimethyl:2H(4)@Any_N-term;Dimethyl... 6;0;4 \n", - "25 Oxidation@M;Dimethyl:2H(4)@Any_N-term;Dimethyl... 6;0;4 \n", - "26 Oxidation@M;Dimethyl:2H(4)@Any_N-term;Dimethyl... 6;0;4 \n", - "27 Dimethyl:2H(4)@Any_N-term;Dimethyl:2H(4)@K 0;4 \n", - "28 Dimethyl:2H(4)@Any_N-term;Dimethyl:2H(4)@K 0;4 \n", - "29 Dimethyl:2H(4)@Any_N-term;Dimethyl:2H(4)@K 0;4 \n", + " mods mod_sites \\\n", + "0 Dimethyl@Any_N-term 0 \n", + "1 Oxidation@M;Dimethyl@Any_N-term;Dimethyl@K 6;0;4 \n", + "2 Dimethyl@Any_N-term;Dimethyl@K 0;4 \n", + "3 Dimethyl:2H(4)@Any_N-term 0 \n", + "4 Oxidation@M;Dimethyl:2H(4)@Any_N-term;Dimethyl... 6;0;4 \n", + "5 Dimethyl:2H(4)@Any_N-term;Dimethyl:2H(4)@K 0;4 \n", "\n", - " labeling_channel charge \n", - "0 0 2 \n", - "1 0 3 \n", - "2 0 4 \n", - "3 0 2 \n", - "4 0 3 \n", - "5 0 4 \n", - "6 0 2 \n", - "7 0 3 \n", - "8 0 4 \n", - "9 0 2 \n", - "10 0 3 \n", - "11 0 4 \n", - "12 0 2 \n", - "13 0 3 \n", - "14 0 4 \n", - "15 4 2 \n", - "16 4 3 \n", - "17 4 4 \n", - "18 4 2 \n", - "19 4 3 \n", - "20 4 4 \n", - "21 4 2 \n", - "22 4 3 \n", - "23 4 4 \n", - "24 4 2 \n", - "25 4 3 \n", - "26 4 4 \n", - "27 4 2 \n", - "28 4 3 \n", - "29 4 4 " + " labeling_channel charge precursor_mz \n", + "0 0 2 427.248152 \n", + "1 0 2 470.786056 \n", + "2 0 2 462.788599 \n", + "3 4 2 429.260705 \n", + "4 4 2 474.811163 \n", + "5 4 2 466.813706 " ] }, - "execution_count": 15, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -2238,7 +2470,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -2278,143 +2510,143 @@ " \n", " \n", " 0\n", - " RQPNMLK\n", - " 2\n", + " LMNPQRST\n", + " 1\n", " 1\n", - " True\n", " False\n", + " True\n", " Oxidation@M\n", - " 5\n", - " 7\n", - " 1\n", " 2\n", - " 451.747462\n", + " 8\n", + " 0\n", + " 2\n", + " 481.739834\n", " \n", " \n", " 1\n", - " RQPNMLK\n", - " 2\n", + " LMNPQRST\n", + " 1\n", " 1\n", - " True\n", " False\n", + " True\n", " \n", " \n", - " 7\n", - " 1\n", + " 8\n", + " 0\n", " 2\n", - " 443.750005\n", + " 473.742377\n", " \n", " \n", " 2\n", - " RQPNMLK\n", - " 2\n", + " ACDESTYK\n", " 1\n", + " 0\n", " True\n", " False\n", - " Acetyl@Protein_N-term;Oxidation@M\n", - " 0;5\n", - " 7\n", - " 1\n", + " Carbamidomethyl@C\n", + " 2\n", + " 8\n", + " 0\n", " 2\n", - " 472.752744\n", + " 487.200207\n", " \n", " \n", " 3\n", - " RQPNMLK\n", - " 2\n", + " ACDESTYK\n", " 1\n", + " 0\n", " True\n", " False\n", - " Acetyl@Protein_N-term\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;2\n", + " 8\n", " 0\n", - " 7\n", - " 1\n", " 2\n", - " 464.755287\n", + " 508.205490\n", " \n", " \n", " 4\n", - " LMNPQRST\n", + " LLNPQRTT\n", " 1\n", " 1\n", " False\n", " True\n", - " Oxidation@M\n", - " 2\n", + " \n", + " \n", " 8\n", - " 0\n", + " 1\n", " 2\n", - " 481.739834\n", + " 471.771991\n", " \n", " \n", " 5\n", - " LMNPQRST\n", + " ASDESTSK\n", " 1\n", - " 1\n", - " False\n", + " 0\n", " True\n", + " False\n", " \n", " \n", " 8\n", - " 0\n", + " 1\n", " 2\n", - " 473.742377\n", + " 412.685247\n", " \n", " \n", " 6\n", - " ACDESTYK\n", + " ASDESTSK\n", " 1\n", " 0\n", " True\n", " False\n", - " Carbamidomethyl@C\n", - " 2\n", - " 8\n", + " Acetyl@Protein_N-term\n", " 0\n", + " 8\n", + " 1\n", " 2\n", - " 487.200207\n", + " 433.690529\n", " \n", " \n", " 7\n", - " ACDESTYK\n", + " MACDESTYK\n", " 1\n", " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term\n", - " 2;0\n", - " 8\n", + " Oxidation@M;Carbamidomethyl@C\n", + " 1;3\n", + " 9\n", " 0\n", " 2\n", - " 508.205490\n", + " 560.717907\n", " \n", " \n", " 8\n", - " YTSEDCAM\n", - " 3\n", + " MACDESTYK\n", + " 1\n", " 0\n", - " False\n", " True\n", - " Carbamidomethyl@C;Oxidation@M\n", - " 6;8\n", - " 8\n", - " 1\n", + " False\n", + " Carbamidomethyl@C\n", + " 3\n", + " 9\n", + " 0\n", " 2\n", - " 496.670426\n", + " 552.720450\n", " \n", " \n", " 9\n", - " YTSEDCAM\n", - " 3\n", + " MACDESTYK\n", + " 1\n", " 0\n", - " False\n", " True\n", - " Carbamidomethyl@C\n", - " 6\n", - " 8\n", - " 1\n", + " False\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", + " 9\n", + " 0\n", " 2\n", - " 488.672968\n", + " 581.723190\n", " \n", " \n", " 10\n", @@ -2423,324 +2655,324 @@ " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Oxidation@M\n", - " 3;1\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;3\n", " 9\n", " 0\n", " 2\n", - " 560.717907\n", + " 573.725732\n", " \n", " \n", " 11\n", - " MACDESTYK\n", + " MLCDESTSK\n", " 1\n", " 0\n", " True\n", " False\n", - " Carbamidomethyl@C\n", - " 3\n", + " Oxidation@M;Carbamidomethyl@C\n", + " 1;3\n", " 9\n", - " 0\n", + " 1\n", " 2\n", - " 552.720450\n", + " 543.725732\n", " \n", " \n", " 12\n", - " MACDESTYK\n", + " MLCDESTSK\n", " 1\n", " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat...\n", - " 3;0;1\n", + " Carbamidomethyl@C\n", + " 3\n", " 9\n", - " 0\n", + " 1\n", " 2\n", - " 581.723190\n", + " 535.728275\n", " \n", " \n", " 13\n", - " MACDESTYK\n", + " MLCDESTSK\n", " 1\n", " 0\n", " True\n", " False\n", - " Carbamidomethyl@C;Acetyl@Protein_N-term\n", - " 3;0\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", " 9\n", - " 0\n", + " 1\n", " 2\n", - " 573.725732\n", + " 564.731015\n", " \n", " \n", " 14\n", - " TSRQPNMLK\n", - " 3\n", + " MLCDESTSK\n", " 1\n", + " 0\n", " True\n", " False\n", - " Oxidation@M\n", - " 7\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;3\n", " 9\n", " 1\n", " 2\n", - " 545.787316\n", + " 556.733557\n", " \n", " \n", " 15\n", - " TSRQPNMLK\n", - " 3\n", + " ASDESTYKVK\n", + " 1\n", " 1\n", " True\n", " False\n", " \n", " \n", - " 9\n", + " 10\n", " 1\n", " 2\n", - " 537.789858\n", + " 564.282586\n", " \n", " \n", " 16\n", - " TSRQPNMLK\n", - " 3\n", + " ASDESTYKVK\n", + " 1\n", " 1\n", " True\n", " False\n", - " Acetyl@Protein_N-term;Oxidation@M\n", - " 0;7\n", - " 9\n", + " Acetyl@Protein_N-term\n", + " 0\n", + " 10\n", " 1\n", " 2\n", - " 566.792598\n", + " 585.287868\n", " \n", " \n", " 17\n", - " TSRQPNMLK\n", - " 3\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", " True\n", - " False\n", - " Acetyl@Protein_N-term\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 11\n", " 0\n", - " 9\n", - " 1\n", " 2\n", - " 558.795141\n", + " 678.863889\n", " \n", " \n", " 18\n", - " QPNMLKIHGF\n", - " 2\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", - " False\n", + " True\n", " True\n", " Oxidation@M\n", - " 4\n", - " 10\n", - " 1\n", - " 2\n", - " 600.813333\n", + " 7\n", + " 11\n", + " 0\n", + " 3\n", + " 452.911685\n", " \n", " \n", " 19\n", - " QPNMLKIHGF\n", - " 2\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", - " False\n", " True\n", - " Oxidation@M\n", - " 4\n", - " 10\n", - " 1\n", - " 3\n", - " 400.877981\n", + " True\n", + " \n", + " \n", + " 11\n", + " 0\n", + " 2\n", + " 670.866431\n", " \n", " \n", " 20\n", - " QPNMLKIHGF\n", - " 2\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", - " False\n", + " True\n", " True\n", " \n", " \n", - " 10\n", - " 1\n", - " 2\n", - " 592.815876\n", + " 11\n", + " 0\n", + " 3\n", + " 447.580046\n", " \n", " \n", " 21\n", - " QPNMLKIHGFK\n", - " 3\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", - " False\n", - " False\n", - " Oxidation@M\n", - " 4\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term;Oxidation@M\n", + " 0;7\n", " 11\n", - " 1\n", + " 0\n", " 2\n", - " 664.860815\n", + " 699.869171\n", " \n", " \n", " 22\n", - " QPNMLKIHGFK\n", - " 3\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", - " False\n", - " False\n", - " Oxidation@M\n", - " 4\n", + " True\n", + " True\n", + " Acetyl@Protein_N-term;Oxidation@M\n", + " 0;7\n", " 11\n", - " 1\n", + " 0\n", " 3\n", - " 443.576302\n", + " 466.915206\n", " \n", " \n", " 23\n", - " QPNMLKIHGFK\n", - " 3\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", - " False\n", - " False\n", - " \n", - " \n", + " True\n", + " True\n", + " Acetyl@Protein_N-term\n", + " 0\n", " 11\n", - " 1\n", + " 0\n", " 2\n", - " 656.863357\n", + " 691.871714\n", " \n", " \n", " 24\n", - " QPNMLKIHGFK\n", - " 3\n", + " FGHIKLMNPQR\n", + " 0;1\n", " 1\n", - " False\n", - " False\n", - " \n", - " \n", + " True\n", + " True\n", + " Acetyl@Protein_N-term\n", + " 0\n", " 11\n", - " 1\n", + " 0\n", " 3\n", - " 438.244664\n", + " 461.583568\n", " \n", " \n", " 25\n", - " RQPNMLKIHGF\n", - " 2\n", - " 2\n", - " True\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", " True\n", - " Oxidation@M\n", - " 5\n", + " False\n", + " Oxidation@M;Carbamidomethyl@C\n", + " 1;3\n", " 11\n", " 1\n", " 2\n", - " 678.863889\n", + " 695.323071\n", " \n", " \n", " 26\n", - " RQPNMLKIHGF\n", - " 2\n", - " 2\n", - " True\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", " True\n", - " Oxidation@M\n", - " 5\n", + " False\n", + " Oxidation@M;Carbamidomethyl@C\n", + " 1;3\n", " 11\n", " 1\n", " 3\n", - " 452.911685\n", + " 463.884473\n", " \n", " \n", " 27\n", - " RQPNMLKIHGF\n", - " 2\n", - " 2\n", - " True\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", " True\n", - " \n", - " \n", + " False\n", + " Carbamidomethyl@C\n", + " 3\n", " 11\n", " 1\n", " 2\n", - " 670.866431\n", + " 687.325613\n", " \n", " \n", " 28\n", - " RQPNMLKIHGF\n", - " 2\n", - " 2\n", - " True\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", " True\n", - " \n", - " \n", + " False\n", + " Carbamidomethyl@C\n", + " 3\n", " 11\n", " 1\n", " 3\n", - " 447.580046\n", + " 458.552834\n", " \n", " \n", " 29\n", - " RQPNMLKIHGF\n", - " 2\n", - " 2\n", - " True\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", " True\n", - " Acetyl@Protein_N-term;Oxidation@M\n", - " 0;5\n", + " False\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", " 11\n", " 1\n", " 2\n", - " 699.869171\n", + " 716.328353\n", " \n", " \n", " 30\n", - " RQPNMLKIHGF\n", - " 2\n", - " 2\n", - " True\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", " True\n", - " Acetyl@Protein_N-term;Oxidation@M\n", - " 0;5\n", + " False\n", + " Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...\n", + " 0;1;3\n", " 11\n", " 1\n", " 3\n", - " 466.915206\n", + " 477.887994\n", " \n", " \n", " 31\n", - " RQPNMLKIHGF\n", - " 2\n", - " 2\n", - " True\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", " True\n", - " Acetyl@Protein_N-term\n", - " 0\n", + " False\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;3\n", " 11\n", " 1\n", " 2\n", - " 691.871714\n", + " 708.330896\n", " \n", " \n", " 32\n", - " RQPNMLKIHGF\n", - " 2\n", - " 2\n", - " True\n", + " MLCDESTYKVK\n", + " 1\n", + " 1\n", " True\n", - " Acetyl@Protein_N-term\n", - " 0\n", + " False\n", + " Acetyl@Protein_N-term;Carbamidomethyl@C\n", + " 0;3\n", " 11\n", " 1\n", " 3\n", - " 461.583568\n", + " 472.556356\n", " \n", " \n", " 33\n", - " FGHIKLMNPQR\n", + " FLHIKLMNPNR\n", " 0;1\n", " 1\n", " True\n", @@ -2748,13 +2980,13 @@ " Oxidation@M\n", " 7\n", " 11\n", - " 0\n", + " 1\n", " 2\n", - " 678.863889\n", + " 699.887364\n", " \n", " \n", " 34\n", - " FGHIKLMNPQR\n", + " FLHIKLMNPNR\n", " 0;1\n", " 1\n", " True\n", @@ -2762,13 +2994,13 @@ " Oxidation@M\n", " 7\n", " 11\n", - " 0\n", + " 1\n", " 3\n", - " 452.911685\n", + " 466.927335\n", " \n", " \n", " 35\n", - " FGHIKLMNPQR\n", + " FLHIKLMNPNR\n", " 0;1\n", " 1\n", " True\n", @@ -2776,13 +3008,13 @@ " \n", " \n", " 11\n", - " 0\n", + " 1\n", " 2\n", - " 670.866431\n", + " 691.889907\n", " \n", " \n", " 36\n", - " FGHIKLMNPQR\n", + " FLHIKLMNPNR\n", " 0;1\n", " 1\n", " True\n", @@ -2790,13 +3022,13 @@ " \n", " \n", " 11\n", - " 0\n", + " 1\n", " 3\n", - " 447.580046\n", + " 461.595697\n", " \n", " \n", " 37\n", - " FGHIKLMNPQR\n", + " FLHIKLMNPNR\n", " 0;1\n", " 1\n", " True\n", @@ -2804,13 +3036,13 @@ " Acetyl@Protein_N-term;Oxidation@M\n", " 0;7\n", " 11\n", - " 0\n", + " 1\n", " 2\n", - " 699.869171\n", + " 720.892646\n", " \n", " \n", " 38\n", - " FGHIKLMNPQR\n", + " FLHIKLMNPNR\n", " 0;1\n", " 1\n", " True\n", @@ -2818,13 +3050,13 @@ " Acetyl@Protein_N-term;Oxidation@M\n", " 0;7\n", " 11\n", - " 0\n", + " 1\n", " 3\n", - " 466.915206\n", + " 480.930856\n", " \n", " \n", " 39\n", - " FGHIKLMNPQR\n", + " FLHIKLMNPNR\n", " 0;1\n", " 1\n", " True\n", @@ -2832,13 +3064,13 @@ " Acetyl@Protein_N-term\n", " 0\n", " 11\n", - " 0\n", + " 1\n", " 2\n", - " 691.871714\n", + " 712.895189\n", " \n", " \n", " 40\n", - " FGHIKLMNPQR\n", + " FLHIKLMNPNR\n", " 0;1\n", " 1\n", " True\n", @@ -2846,13 +3078,13 @@ " Acetyl@Protein_N-term\n", " 0\n", " 11\n", - " 0\n", + " 1\n", " 3\n", - " 461.583568\n", + " 475.599218\n", " \n", " \n", " 41\n", - " FGHIKLMNPQRST\n", + " FLHIKLMNPQRTT\n", " 1\n", " 2\n", " False\n", @@ -2860,13 +3092,13 @@ " Oxidation@M\n", " 7\n", " 13\n", - " 0\n", + " 1\n", " 2\n", - " 772.903742\n", + " 807.942867\n", " \n", " \n", " 42\n", - " FGHIKLMNPQRST\n", + " FLHIKLMNPQRTT\n", " 1\n", " 2\n", " False\n", @@ -2874,27 +3106,27 @@ " Oxidation@M\n", " 7\n", " 13\n", - " 0\n", + " 1\n", " 3\n", - " 515.604920\n", + " 538.964337\n", " \n", " \n", " 43\n", - " FGHIKLMNPQRST\n", + " FLHIKLMNPQRTT\n", " 1\n", " 2\n", " False\n", " True\n", - " \n", - " \n", + " Oxidation@M\n", + " 7\n", " 13\n", - " 0\n", - " 2\n", - " 764.906285\n", + " 1\n", + " 4\n", + " 404.475072\n", " \n", " \n", " 44\n", - " FGHIKLMNPQRST\n", + " FLHIKLMNPQRTT\n", " 1\n", " 2\n", " False\n", @@ -2902,362 +3134,260 @@ " \n", " \n", " 13\n", - " 0\n", - " 3\n", - " 510.273282\n", - " \n", - " \n", - " 45\n", - " TSRQPNMLKIHGFK\n", - " 3\n", - " 2\n", - " True\n", - " False\n", - " Oxidation@M\n", - " 7\n", - " 14\n", " 1\n", " 2\n", - " 836.951224\n", - " \n", - " \n", - " 46\n", - " TSRQPNMLKIHGFK\n", - " 3\n", - " 2\n", - " True\n", - " False\n", - " Oxidation@M\n", - " 7\n", - " 14\n", - " 1\n", - " 3\n", - " 558.303241\n", + " 799.945410\n", " \n", " \n", - " 47\n", - " TSRQPNMLKIHGFK\n", - " 3\n", - " 2\n", - " True\n", - " False\n", - " Oxidation@M\n", - " 7\n", - " 14\n", + " 45\n", + " FLHIKLMNPQRTT\n", " 1\n", - " 4\n", - " 418.979250\n", - " \n", - " \n", - " 48\n", - " TSRQPNMLKIHGFK\n", - " 3\n", " 2\n", - " True\n", " False\n", - " \n", - " \n", - " 14\n", - " 1\n", - " 2\n", - " 828.953766\n", - " \n", - " \n", - " 49\n", - " TSRQPNMLKIHGFK\n", - " 3\n", - " 2\n", " True\n", - " False\n", " \n", " \n", - " 14\n", + " 13\n", " 1\n", " 3\n", - " 552.971603\n", + " 533.632699\n", " \n", " \n", - " 50\n", - " TSRQPNMLKIHGFK\n", - " 3\n", + " 46\n", + " FLHIKLMNPQRTT\n", + " 1\n", " 2\n", - " True\n", " False\n", + " True\n", " \n", " \n", - " 14\n", + " 13\n", " 1\n", " 4\n", - " 414.980521\n", + " 400.476343\n", " \n", " \n", - " 51\n", - " TSRQPNMLKIHGFK\n", - " 3\n", + " 47\n", + " FGHIKLMNPQRST\n", + " 1\n", " 2\n", - " True\n", " False\n", - " Acetyl@Protein_N-term;Oxidation@M\n", - " 0;7\n", - " 14\n", - " 1\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 13\n", + " 0\n", " 2\n", - " 857.956506\n", + " 772.903742\n", " \n", " \n", - " 52\n", - " TSRQPNMLKIHGFK\n", - " 3\n", + " 48\n", + " FGHIKLMNPQRST\n", + " 1\n", " 2\n", - " True\n", " False\n", - " Acetyl@Protein_N-term;Oxidation@M\n", - " 0;7\n", - " 14\n", - " 1\n", + " True\n", + " Oxidation@M\n", + " 7\n", + " 13\n", + " 0\n", " 3\n", - " 572.306763\n", + " 515.604920\n", " \n", " \n", - " 53\n", - " TSRQPNMLKIHGFK\n", - " 3\n", - " 2\n", - " True\n", - " False\n", - " Acetyl@Protein_N-term;Oxidation@M\n", - " 0;7\n", - " 14\n", + " 49\n", + " FGHIKLMNPQRST\n", " 1\n", - " 4\n", - " 429.481891\n", - " \n", - " \n", - " 54\n", - " TSRQPNMLKIHGFK\n", - " 3\n", " 2\n", - " True\n", " False\n", - " Acetyl@Protein_N-term\n", + " True\n", + " \n", + " \n", + " 13\n", " 0\n", - " 14\n", - " 1\n", " 2\n", - " 849.959049\n", + " 764.906285\n", " \n", " \n", - " 55\n", - " TSRQPNMLKIHGFK\n", - " 3\n", - " 2\n", - " True\n", - " False\n", - " Acetyl@Protein_N-term\n", - " 0\n", - " 14\n", + " 50\n", + " FGHIKLMNPQRST\n", " 1\n", - " 3\n", - " 566.975125\n", - " \n", - " \n", - " 56\n", - " TSRQPNMLKIHGFK\n", - " 3\n", " 2\n", - " True\n", " False\n", - " Acetyl@Protein_N-term\n", + " True\n", + " \n", + " \n", + " 13\n", " 0\n", - " 14\n", - " 1\n", - " 4\n", - " 425.483163\n", + " 3\n", + " 510.273282\n", " \n", " \n", "\n", "" ], "text/plain": [ - " sequence protein_idxes miss_cleavage is_prot_nterm is_prot_cterm \\\n", - "0 RQPNMLK 2 1 True False \n", - "1 RQPNMLK 2 1 True False \n", - "2 RQPNMLK 2 1 True False \n", - "3 RQPNMLK 2 1 True False \n", - "4 LMNPQRST 1 1 False True \n", - "5 LMNPQRST 1 1 False True \n", - "6 ACDESTYK 1 0 True False \n", - "7 ACDESTYK 1 0 True False \n", - "8 YTSEDCAM 3 0 False True \n", - "9 YTSEDCAM 3 0 False True \n", - "10 MACDESTYK 1 0 True False \n", - "11 MACDESTYK 1 0 True False \n", - "12 MACDESTYK 1 0 True False \n", - "13 MACDESTYK 1 0 True False \n", - "14 TSRQPNMLK 3 1 True False \n", - "15 TSRQPNMLK 3 1 True False \n", - "16 TSRQPNMLK 3 1 True False \n", - "17 TSRQPNMLK 3 1 True False \n", - "18 QPNMLKIHGF 2 1 False True \n", - "19 QPNMLKIHGF 2 1 False True \n", - "20 QPNMLKIHGF 2 1 False True \n", - "21 QPNMLKIHGFK 3 1 False False \n", - "22 QPNMLKIHGFK 3 1 False False \n", - "23 QPNMLKIHGFK 3 1 False False \n", - "24 QPNMLKIHGFK 3 1 False False \n", - "25 RQPNMLKIHGF 2 2 True True \n", - "26 RQPNMLKIHGF 2 2 True True \n", - "27 RQPNMLKIHGF 2 2 True True \n", - "28 RQPNMLKIHGF 2 2 True True \n", - "29 RQPNMLKIHGF 2 2 True True \n", - "30 RQPNMLKIHGF 2 2 True True \n", - "31 RQPNMLKIHGF 2 2 True True \n", - "32 RQPNMLKIHGF 2 2 True True \n", - "33 FGHIKLMNPQR 0;1 1 True True \n", - "34 FGHIKLMNPQR 0;1 1 True True \n", - "35 FGHIKLMNPQR 0;1 1 True True \n", - "36 FGHIKLMNPQR 0;1 1 True True \n", - "37 FGHIKLMNPQR 0;1 1 True True \n", - "38 FGHIKLMNPQR 0;1 1 True True \n", - "39 FGHIKLMNPQR 0;1 1 True True \n", - "40 FGHIKLMNPQR 0;1 1 True True \n", - "41 FGHIKLMNPQRST 1 2 False True \n", - "42 FGHIKLMNPQRST 1 2 False True \n", - "43 FGHIKLMNPQRST 1 2 False True \n", - "44 FGHIKLMNPQRST 1 2 False True \n", - "45 TSRQPNMLKIHGFK 3 2 True False \n", - "46 TSRQPNMLKIHGFK 3 2 True False \n", - "47 TSRQPNMLKIHGFK 3 2 True False \n", - "48 TSRQPNMLKIHGFK 3 2 True False \n", - "49 TSRQPNMLKIHGFK 3 2 True False \n", - "50 TSRQPNMLKIHGFK 3 2 True False \n", - "51 TSRQPNMLKIHGFK 3 2 True False \n", - "52 TSRQPNMLKIHGFK 3 2 True False \n", - "53 TSRQPNMLKIHGFK 3 2 True False \n", - "54 TSRQPNMLKIHGFK 3 2 True False \n", - "55 TSRQPNMLKIHGFK 3 2 True False \n", - "56 TSRQPNMLKIHGFK 3 2 True False \n", + " sequence protein_idxes miss_cleavage is_prot_nterm is_prot_cterm \\\n", + "0 LMNPQRST 1 1 False True \n", + "1 LMNPQRST 1 1 False True \n", + "2 ACDESTYK 1 0 True False \n", + "3 ACDESTYK 1 0 True False \n", + "4 LLNPQRTT 1 1 False True \n", + "5 ASDESTSK 1 0 True False \n", + "6 ASDESTSK 1 0 True False \n", + "7 MACDESTYK 1 0 True False \n", + "8 MACDESTYK 1 0 True False \n", + "9 MACDESTYK 1 0 True False \n", + "10 MACDESTYK 1 0 True False \n", + "11 MLCDESTSK 1 0 True False \n", + "12 MLCDESTSK 1 0 True False \n", + "13 MLCDESTSK 1 0 True False \n", + "14 MLCDESTSK 1 0 True False \n", + "15 ASDESTYKVK 1 1 True False \n", + "16 ASDESTYKVK 1 1 True False \n", + "17 FGHIKLMNPQR 0;1 1 True True \n", + "18 FGHIKLMNPQR 0;1 1 True True \n", + "19 FGHIKLMNPQR 0;1 1 True True \n", + "20 FGHIKLMNPQR 0;1 1 True True \n", + "21 FGHIKLMNPQR 0;1 1 True True \n", + "22 FGHIKLMNPQR 0;1 1 True True \n", + "23 FGHIKLMNPQR 0;1 1 True True \n", + "24 FGHIKLMNPQR 0;1 1 True True \n", + "25 MLCDESTYKVK 1 1 True False \n", + "26 MLCDESTYKVK 1 1 True False \n", + "27 MLCDESTYKVK 1 1 True False \n", + "28 MLCDESTYKVK 1 1 True False \n", + "29 MLCDESTYKVK 1 1 True False \n", + "30 MLCDESTYKVK 1 1 True False \n", + "31 MLCDESTYKVK 1 1 True False \n", + "32 MLCDESTYKVK 1 1 True False \n", + "33 FLHIKLMNPNR 0;1 1 True True \n", + "34 FLHIKLMNPNR 0;1 1 True True \n", + "35 FLHIKLMNPNR 0;1 1 True True \n", + "36 FLHIKLMNPNR 0;1 1 True True \n", + "37 FLHIKLMNPNR 0;1 1 True True \n", + "38 FLHIKLMNPNR 0;1 1 True True \n", + "39 FLHIKLMNPNR 0;1 1 True True \n", + "40 FLHIKLMNPNR 0;1 1 True True \n", + "41 FLHIKLMNPQRTT 1 2 False True \n", + "42 FLHIKLMNPQRTT 1 2 False True \n", + "43 FLHIKLMNPQRTT 1 2 False True \n", + "44 FLHIKLMNPQRTT 1 2 False True \n", + "45 FLHIKLMNPQRTT 1 2 False True \n", + "46 FLHIKLMNPQRTT 1 2 False True \n", + "47 FGHIKLMNPQRST 1 2 False True \n", + "48 FGHIKLMNPQRST 1 2 False True \n", + "49 FGHIKLMNPQRST 1 2 False True \n", + "50 FGHIKLMNPQRST 1 2 False True \n", "\n", " mods mod_sites nAA decoy \\\n", - "0 Oxidation@M 5 7 1 \n", - "1 7 1 \n", - "2 Acetyl@Protein_N-term;Oxidation@M 0;5 7 1 \n", - "3 Acetyl@Protein_N-term 0 7 1 \n", - "4 Oxidation@M 2 8 0 \n", - "5 8 0 \n", - "6 Carbamidomethyl@C 2 8 0 \n", - "7 Carbamidomethyl@C;Acetyl@Protein_N-term 2;0 8 0 \n", - "8 Carbamidomethyl@C;Oxidation@M 6;8 8 1 \n", - "9 Carbamidomethyl@C 6 8 1 \n", - "10 Carbamidomethyl@C;Oxidation@M 3;1 9 0 \n", - "11 Carbamidomethyl@C 3 9 0 \n", - "12 Carbamidomethyl@C;Acetyl@Protein_N-term;Oxidat... 3;0;1 9 0 \n", - "13 Carbamidomethyl@C;Acetyl@Protein_N-term 3;0 9 0 \n", - "14 Oxidation@M 7 9 1 \n", - "15 9 1 \n", - "16 Acetyl@Protein_N-term;Oxidation@M 0;7 9 1 \n", - "17 Acetyl@Protein_N-term 0 9 1 \n", - "18 Oxidation@M 4 10 1 \n", - "19 Oxidation@M 4 10 1 \n", - "20 10 1 \n", - "21 Oxidation@M 4 11 1 \n", - "22 Oxidation@M 4 11 1 \n", - "23 11 1 \n", - "24 11 1 \n", - "25 Oxidation@M 5 11 1 \n", - "26 Oxidation@M 5 11 1 \n", - "27 11 1 \n", - "28 11 1 \n", - "29 Acetyl@Protein_N-term;Oxidation@M 0;5 11 1 \n", - "30 Acetyl@Protein_N-term;Oxidation@M 0;5 11 1 \n", - "31 Acetyl@Protein_N-term 0 11 1 \n", - "32 Acetyl@Protein_N-term 0 11 1 \n", - "33 Oxidation@M 7 11 0 \n", - "34 Oxidation@M 7 11 0 \n", - "35 11 0 \n", - "36 11 0 \n", - "37 Acetyl@Protein_N-term;Oxidation@M 0;7 11 0 \n", - "38 Acetyl@Protein_N-term;Oxidation@M 0;7 11 0 \n", - "39 Acetyl@Protein_N-term 0 11 0 \n", - "40 Acetyl@Protein_N-term 0 11 0 \n", - "41 Oxidation@M 7 13 0 \n", - "42 Oxidation@M 7 13 0 \n", - "43 13 0 \n", - "44 13 0 \n", - "45 Oxidation@M 7 14 1 \n", - "46 Oxidation@M 7 14 1 \n", - "47 Oxidation@M 7 14 1 \n", - "48 14 1 \n", - "49 14 1 \n", - "50 14 1 \n", - "51 Acetyl@Protein_N-term;Oxidation@M 0;7 14 1 \n", - "52 Acetyl@Protein_N-term;Oxidation@M 0;7 14 1 \n", - "53 Acetyl@Protein_N-term;Oxidation@M 0;7 14 1 \n", - "54 Acetyl@Protein_N-term 0 14 1 \n", - "55 Acetyl@Protein_N-term 0 14 1 \n", - "56 Acetyl@Protein_N-term 0 14 1 \n", + "0 Oxidation@M 2 8 0 \n", + "1 8 0 \n", + "2 Carbamidomethyl@C 2 8 0 \n", + "3 Acetyl@Protein_N-term;Carbamidomethyl@C 0;2 8 0 \n", + "4 8 1 \n", + "5 8 1 \n", + "6 Acetyl@Protein_N-term 0 8 1 \n", + "7 Oxidation@M;Carbamidomethyl@C 1;3 9 0 \n", + "8 Carbamidomethyl@C 3 9 0 \n", + "9 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 9 0 \n", + "10 Acetyl@Protein_N-term;Carbamidomethyl@C 0;3 9 0 \n", + "11 Oxidation@M;Carbamidomethyl@C 1;3 9 1 \n", + "12 Carbamidomethyl@C 3 9 1 \n", + "13 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 9 1 \n", + "14 Acetyl@Protein_N-term;Carbamidomethyl@C 0;3 9 1 \n", + "15 10 1 \n", + "16 Acetyl@Protein_N-term 0 10 1 \n", + "17 Oxidation@M 7 11 0 \n", + "18 Oxidation@M 7 11 0 \n", + "19 11 0 \n", + "20 11 0 \n", + "21 Acetyl@Protein_N-term;Oxidation@M 0;7 11 0 \n", + "22 Acetyl@Protein_N-term;Oxidation@M 0;7 11 0 \n", + "23 Acetyl@Protein_N-term 0 11 0 \n", + "24 Acetyl@Protein_N-term 0 11 0 \n", + "25 Oxidation@M;Carbamidomethyl@C 1;3 11 1 \n", + "26 Oxidation@M;Carbamidomethyl@C 1;3 11 1 \n", + "27 Carbamidomethyl@C 3 11 1 \n", + "28 Carbamidomethyl@C 3 11 1 \n", + "29 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 11 1 \n", + "30 Acetyl@Protein_N-term;Oxidation@M;Carbamidomet... 0;1;3 11 1 \n", + "31 Acetyl@Protein_N-term;Carbamidomethyl@C 0;3 11 1 \n", + "32 Acetyl@Protein_N-term;Carbamidomethyl@C 0;3 11 1 \n", + "33 Oxidation@M 7 11 1 \n", + "34 Oxidation@M 7 11 1 \n", + "35 11 1 \n", + "36 11 1 \n", + "37 Acetyl@Protein_N-term;Oxidation@M 0;7 11 1 \n", + "38 Acetyl@Protein_N-term;Oxidation@M 0;7 11 1 \n", + "39 Acetyl@Protein_N-term 0 11 1 \n", + "40 Acetyl@Protein_N-term 0 11 1 \n", + "41 Oxidation@M 7 13 1 \n", + "42 Oxidation@M 7 13 1 \n", + "43 Oxidation@M 7 13 1 \n", + "44 13 1 \n", + "45 13 1 \n", + "46 13 1 \n", + "47 Oxidation@M 7 13 0 \n", + "48 Oxidation@M 7 13 0 \n", + "49 13 0 \n", + "50 13 0 \n", "\n", " charge precursor_mz \n", - "0 2 451.747462 \n", - "1 2 443.750005 \n", - "2 2 472.752744 \n", - "3 2 464.755287 \n", - "4 2 481.739834 \n", - "5 2 473.742377 \n", - "6 2 487.200207 \n", - "7 2 508.205490 \n", - "8 2 496.670426 \n", - "9 2 488.672968 \n", - "10 2 560.717907 \n", - "11 2 552.720450 \n", - "12 2 581.723190 \n", - "13 2 573.725732 \n", - "14 2 545.787316 \n", - "15 2 537.789858 \n", - "16 2 566.792598 \n", - "17 2 558.795141 \n", - "18 2 600.813333 \n", - "19 3 400.877981 \n", - "20 2 592.815876 \n", - "21 2 664.860815 \n", - "22 3 443.576302 \n", - "23 2 656.863357 \n", - "24 3 438.244664 \n", - "25 2 678.863889 \n", - "26 3 452.911685 \n", - "27 2 670.866431 \n", - "28 3 447.580046 \n", - "29 2 699.869171 \n", - "30 3 466.915206 \n", - "31 2 691.871714 \n", - "32 3 461.583568 \n", - "33 2 678.863889 \n", - "34 3 452.911685 \n", - "35 2 670.866431 \n", - "36 3 447.580046 \n", - "37 2 699.869171 \n", - "38 3 466.915206 \n", - "39 2 691.871714 \n", - "40 3 461.583568 \n", - "41 2 772.903742 \n", - "42 3 515.604920 \n", - "43 2 764.906285 \n", - "44 3 510.273282 \n", - "45 2 836.951224 \n", - "46 3 558.303241 \n", - "47 4 418.979250 \n", - "48 2 828.953766 \n", - "49 3 552.971603 \n", - "50 4 414.980521 \n", - "51 2 857.956506 \n", - "52 3 572.306763 \n", - "53 4 429.481891 \n", - "54 2 849.959049 \n", - "55 3 566.975125 \n", - "56 4 425.483163 " + "0 2 481.739834 \n", + "1 2 473.742377 \n", + "2 2 487.200207 \n", + "3 2 508.205490 \n", + "4 2 471.771991 \n", + "5 2 412.685247 \n", + "6 2 433.690529 \n", + "7 2 560.717907 \n", + "8 2 552.720450 \n", + "9 2 581.723190 \n", + "10 2 573.725732 \n", + "11 2 543.725732 \n", + "12 2 535.728275 \n", + "13 2 564.731015 \n", + "14 2 556.733557 \n", + "15 2 564.282586 \n", + "16 2 585.287868 \n", + "17 2 678.863889 \n", + "18 3 452.911685 \n", + "19 2 670.866431 \n", + "20 3 447.580046 \n", + "21 2 699.869171 \n", + "22 3 466.915206 \n", + "23 2 691.871714 \n", + "24 3 461.583568 \n", + "25 2 695.323071 \n", + "26 3 463.884473 \n", + "27 2 687.325613 \n", + "28 3 458.552834 \n", + "29 2 716.328353 \n", + "30 3 477.887994 \n", + "31 2 708.330896 \n", + "32 3 472.556356 \n", + "33 2 699.887364 \n", + "34 3 466.927335 \n", + "35 2 691.889907 \n", + "36 3 461.595697 \n", + "37 2 720.892646 \n", + "38 3 480.930856 \n", + "39 2 712.895189 \n", + "40 3 475.599218 \n", + "41 2 807.942867 \n", + "42 3 538.964337 \n", + "43 4 404.475072 \n", + "44 2 799.945410 \n", + "45 3 533.632699 \n", + "46 4 400.476343 \n", + "47 2 772.903742 \n", + "48 3 515.604920 \n", + "49 2 764.906285 \n", + "50 3 510.273282 " ] }, - "execution_count": 16, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3277,7 +3407,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -3286,7 +3416,7 @@ "(400.0, 2000.0, \"mass of 'x' is 100000000.0\")" ] }, - "execution_count": 17, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -3308,7 +3438,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -3339,28 +3469,28 @@ " \n", " \n", " 0\n", - " 157.108387\n", - " 746.386537\n", + " 114.091339\n", + " 849.388306\n", " \n", " \n", " 1\n", - " 285.166965\n", - " 618.327959\n", + " 261.126740\n", + " 702.352905\n", " \n", " \n", " 2\n", - " 382.219729\n", - " 521.275195\n", + " 375.169678\n", + " 588.309998\n", " \n", " \n", " 3\n", - " 496.262656\n", - " 407.232268\n", + " 472.222443\n", + " 491.257233\n", " \n", " \n", " 4\n", - " 643.298056\n", - " 260.196868\n", + " 600.281006\n", + " 363.198669\n", " \n", " \n", " ...\n", @@ -3368,53 +3498,53 @@ " ...\n", " \n", " \n", - " 556\n", - " 1098.572440\n", - " 601.345658\n", + " 486\n", + " 941.502563\n", + " 588.309998\n", " \n", " \n", - " 557\n", - " 1211.656504\n", - " 488.261594\n", + " 487\n", + " 1038.555298\n", + " 491.257233\n", " \n", " \n", - " 558\n", - " 1348.715416\n", - " 351.202682\n", + " 488\n", + " 1166.613892\n", + " 363.198669\n", " \n", " \n", - " 559\n", - " 1405.736879\n", - " 294.181218\n", + " 489\n", + " 1322.714966\n", + " 207.097549\n", " \n", " \n", - " 560\n", - " 1552.805293\n", - " 147.112804\n", + " 490\n", + " 1409.747070\n", + " 120.065521\n", " \n", " \n", "\n", - "

    561 rows × 2 columns

    \n", + "

    491 rows × 2 columns

    \n", "" ], "text/plain": [ " b_z1 y_z1\n", - "0 157.108387 746.386537\n", - "1 285.166965 618.327959\n", - "2 382.219729 521.275195\n", - "3 496.262656 407.232268\n", - "4 643.298056 260.196868\n", + "0 114.091339 849.388306\n", + "1 261.126740 702.352905\n", + "2 375.169678 588.309998\n", + "3 472.222443 491.257233\n", + "4 600.281006 363.198669\n", ".. ... ...\n", - "556 1098.572440 601.345658\n", - "557 1211.656504 488.261594\n", - "558 1348.715416 351.202682\n", - "559 1405.736879 294.181218\n", - "560 1552.805293 147.112804\n", + "486 941.502563 588.309998\n", + "487 1038.555298 491.257233\n", + "488 1166.613892 363.198669\n", + "489 1322.714966 207.097549\n", + "490 1409.747070 120.065521\n", "\n", - "[561 rows x 2 columns]" + "[491 rows x 2 columns]" ] }, - "execution_count": 18, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -3433,7 +3563,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -3463,39 +3593,39 @@ " \n", " \n", " \n", - " 31\n", - " 114.091340\n", - " 833.393413\n", + " 35\n", + " 72.044388\n", + " 753.326111\n", " \n", " \n", - " 32\n", - " 245.131826\n", - " 702.352928\n", + " 36\n", + " 159.076416\n", + " 666.294067\n", " \n", " \n", - " 33\n", - " 359.174753\n", - " 588.310000\n", + " 37\n", + " 274.103363\n", + " 551.267151\n", " \n", " \n", - " 34\n", - " 456.227517\n", - " 491.257237\n", + " 38\n", + " 403.145966\n", + " 422.224548\n", " \n", " \n", - " 35\n", - " 584.286094\n", - " 363.198659\n", + " 39\n", + " 490.177979\n", + " 335.192505\n", " \n", " \n", - " 36\n", - " 740.387205\n", - " 207.097548\n", + " 40\n", + " 591.225647\n", + " 234.144836\n", " \n", " \n", - " 37\n", - " 827.419234\n", - " 120.065520\n", + " 41\n", + " 678.257690\n", + " 147.112808\n", " \n", " \n", "\n", @@ -3503,16 +3633,16 @@ ], "text/plain": [ " b_z1 y_z1\n", - "31 114.091340 833.393413\n", - "32 245.131826 702.352928\n", - "33 359.174753 588.310000\n", - "34 456.227517 491.257237\n", - "35 584.286094 363.198659\n", - "36 740.387205 207.097548\n", - "37 827.419234 120.065520" + "35 72.044388 753.326111\n", + "36 159.076416 666.294067\n", + "37 274.103363 551.267151\n", + "38 403.145966 422.224548\n", + "39 490.177979 335.192505\n", + "40 591.225647 234.144836\n", + "41 678.257690 147.112808" ] }, - "execution_count": 19, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -3547,7 +3677,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.11.9" }, "orig_nbformat": 4, "vscode": { diff --git a/tests/run_tests.sh b/tests/run_tests.sh index 36c9eec2..f39c67c6 100755 --- a/tests/run_tests.sh +++ b/tests/run_tests.sh @@ -1,11 +1,15 @@ # TODO make tutorial_dev_spectral_libraries.ipynb work -INCLUDED_NBS=$(find ../docs/nbs -name "*.ipynb" | grep -v tutorial_dev_spectral_libraries.ipynb) -python -m pytest --nbmake $(echo $INCLUDED_NBS) +DOCS_NBS=$(find ../docs/nbs -name "*.ipynb" | grep -v tutorial_dev_spectral_libraries.ipynb) # TODO make test_isotope_mp.ipynb work # Note: multiprocessing in ipynb sometimes suspended on some versions of Windows, ignore the # corresponding notebook(s) if this occurs again # INCLUDED_NBS=$(find ../nbs_tests -name "*.ipynb" | grep -v test_isotope_mp.ipynb) -INCLUDED_NBS=$(find ../nbs_tests -name "*.ipynb") -python -m pytest --nbmake $(echo $INCLUDED_NBS) +TEST_NBS=$(find ../nbs_tests -name "*.ipynb") + +TUTORIAL_NBS=$(find ../docs/tutorials -name "*.ipynb") + +ALL_NBS=$(echo $DOCS_NBS$'\n'$TEST_NBS$'\n'$TUTORIAL_NBS) + +python -m pytest --nbmake $(echo $ALL_NBS) From c5e1adf58b4b9aa6a3de00b42131cb6dc361b03b Mon Sep 17 00:00:00 2001 From: jalew188 Date: Thu, 25 Jul 2024 10:05:27 +0200 Subject: [PATCH 51/53] #208 Explain why protein decoy is inconsistant with DecoyGenerator --- alphabase/protein/protein_level_decoy.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/alphabase/protein/protein_level_decoy.py b/alphabase/protein/protein_level_decoy.py index 0ea12d76..9710e554 100644 --- a/alphabase/protein/protein_level_decoy.py +++ b/alphabase/protein/protein_level_decoy.py @@ -71,6 +71,8 @@ def _append_protein_df_to_target_lib(self): ) -# remove "protein_reverse" decoy due to conflicting with DecoyGenerator +# remove "protein_reverse" decoy due to conflicting with DecoyGenerator, +# as DecoyGenerator only works for peptide-level, but ProteinReverseDecoy +# is protein-level. # def register_decoy(): # decoy_lib_provider.register("protein_reverse", ProteinReverseDecoy) From 6c160884d6fb1f80d47b03135ff9a27582e29fe4 Mon Sep 17 00:00:00 2001 From: jalew188 Date: Mon, 5 Aug 2024 14:05:00 +0800 Subject: [PATCH 52/53] =?UTF-8?q?Bump=20version:=201.2.5=20=E2=86=92=201.2?= =?UTF-8?q?.6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 4 ++-- alphabase/__init__.py | 2 +- docs/conf.py | 2 +- release/one_click_linux_gui/control | 2 +- release/one_click_linux_gui/create_installer_linux.sh | 2 +- release/one_click_macos_gui/Info.plist | 4 ++-- release/one_click_macos_gui/create_installer_macos.sh | 4 ++-- release/one_click_macos_gui/distribution.xml | 2 +- release/one_click_windows_gui/alphabase_innoinstaller.iss | 2 +- release/one_click_windows_gui/create_installer_windows.sh | 2 +- 10 files changed, 13 insertions(+), 13 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index e4500ed2..9afe2236 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,9 +1,9 @@ [bumpversion] -current_version = 1.2.5 +current_version = 1.2.6 commit = True tag = False parse = (?P\d+)\.(?P\d+)\.(?P\d+)(\-(?P[a-z]+)(?P\d+))? -serialize = +serialize = {major}.{minor}.{patch} {major}.{minor}.{patch} diff --git a/alphabase/__init__.py b/alphabase/__init__.py index 8a48bb30..6c9878f6 100644 --- a/alphabase/__init__.py +++ b/alphabase/__init__.py @@ -2,7 +2,7 @@ __project__ = "alphabase" -__version__ = "1.2.5" +__version__ = "1.2.6" __license__ = "Apache" __description__ = "An infrastructure Python package of the AlphaX ecosystem" __author__ = "Mann Labs" diff --git a/docs/conf.py b/docs/conf.py index 976d8239..d44d5136 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -24,7 +24,7 @@ copyright = "2022, Mann Labs, MPIB" author = "Mann Labs, MPIB" -release = "1.2.5" +release = "1.2.6" # -- General configuration --------------------------------------------------- diff --git a/release/one_click_linux_gui/control b/release/one_click_linux_gui/control index e447a1c6..7aee4103 100644 --- a/release/one_click_linux_gui/control +++ b/release/one_click_linux_gui/control @@ -1,5 +1,5 @@ Package: AlphaBase -Version: 1.2.5 +Version: 1.2.6 Architecture: all Maintainer: Mann Labs Description: AlphaBase diff --git a/release/one_click_linux_gui/create_installer_linux.sh b/release/one_click_linux_gui/create_installer_linux.sh index 69a18e50..21d7015e 100644 --- a/release/one_click_linux_gui/create_installer_linux.sh +++ b/release/one_click_linux_gui/create_installer_linux.sh @@ -17,7 +17,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_linux_gui # Make sure you include the required extra packages and always use the stable or very-stable options! -pip install "../../dist/alphabase-1.2.5-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.2.6-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller diff --git a/release/one_click_macos_gui/Info.plist b/release/one_click_macos_gui/Info.plist index c76b5ef3..095489b2 100644 --- a/release/one_click_macos_gui/Info.plist +++ b/release/one_click_macos_gui/Info.plist @@ -9,9 +9,9 @@ CFBundleIconFile alpha_logo.icns CFBundleIdentifier - alphabase.1.2.5 + alphabase.1.2.6 CFBundleShortVersionString - 1.2.5 + 1.2.6 CFBundleInfoDictionaryVersion 6.0 CFBundleName diff --git a/release/one_click_macos_gui/create_installer_macos.sh b/release/one_click_macos_gui/create_installer_macos.sh index 0c3627fd..b4230dd1 100644 --- a/release/one_click_macos_gui/create_installer_macos.sh +++ b/release/one_click_macos_gui/create_installer_macos.sh @@ -20,7 +20,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_macos_gui -pip install "../../dist/alphabase-1.2.5-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.2.6-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller @@ -40,5 +40,5 @@ cp ../../LICENSE.txt Resources/LICENSE.txt cp ../logos/alpha_logo.png Resources/alpha_logo.png chmod 777 scripts/* -pkgbuild --root dist/alphabase --identifier de.mpg.biochem.alphabase.app --version 1.2.5 --install-location /Applications/AlphaBase.app --scripts scripts AlphaBase.pkg +pkgbuild --root dist/alphabase --identifier de.mpg.biochem.alphabase.app --version 1.2.6 --install-location /Applications/AlphaBase.app --scripts scripts AlphaBase.pkg productbuild --distribution distribution.xml --resources Resources --package-path AlphaBase.pkg dist/alphabase_gui_installer_macos.pkg diff --git a/release/one_click_macos_gui/distribution.xml b/release/one_click_macos_gui/distribution.xml index abf03388..8082a752 100644 --- a/release/one_click_macos_gui/distribution.xml +++ b/release/one_click_macos_gui/distribution.xml @@ -1,6 +1,6 @@ - AlphaBase 1.2.5 + AlphaBase 1.2.6 diff --git a/release/one_click_windows_gui/alphabase_innoinstaller.iss b/release/one_click_windows_gui/alphabase_innoinstaller.iss index 12bed322..6bd6a0b3 100644 --- a/release/one_click_windows_gui/alphabase_innoinstaller.iss +++ b/release/one_click_windows_gui/alphabase_innoinstaller.iss @@ -2,7 +2,7 @@ ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES! #define MyAppName "AlphaBase" -#define MyAppVersion "1.2.5" +#define MyAppVersion "1.2.6" #define MyAppPublisher "Max Planck Institute of Biochemistry and the University of Copenhagen, Mann Labs" #define MyAppURL "https://github.com/MannLabs/alphabase" #define MyAppExeName "alphabase_gui.exe" diff --git a/release/one_click_windows_gui/create_installer_windows.sh b/release/one_click_windows_gui/create_installer_windows.sh index 81229509..e64cf0eb 100644 --- a/release/one_click_windows_gui/create_installer_windows.sh +++ b/release/one_click_windows_gui/create_installer_windows.sh @@ -17,7 +17,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_windows_gui # Make sure you include the required extra packages and always use the stable or very-stable options! -pip install "../../dist/alphabase-1.2.5-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.2.6-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller From 95b3f2fb200fc9cc5665837bafcb346075a6b9f2 Mon Sep 17 00:00:00 2001 From: jalew188 Date: Mon, 5 Aug 2024 14:05:36 +0800 Subject: [PATCH 53/53] =?UTF-8?q?Bump=20version:=201.2.6=20=E2=86=92=201.3?= =?UTF-8?q?.0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- alphabase/__init__.py | 2 +- docs/conf.py | 2 +- release/one_click_linux_gui/control | 2 +- release/one_click_linux_gui/create_installer_linux.sh | 2 +- release/one_click_macos_gui/Info.plist | 4 ++-- release/one_click_macos_gui/create_installer_macos.sh | 4 ++-- release/one_click_macos_gui/distribution.xml | 2 +- release/one_click_windows_gui/alphabase_innoinstaller.iss | 2 +- release/one_click_windows_gui/create_installer_windows.sh | 2 +- 10 files changed, 12 insertions(+), 12 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 9afe2236..c096fdd7 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 1.2.6 +current_version = 1.3.0 commit = True tag = False parse = (?P\d+)\.(?P\d+)\.(?P\d+)(\-(?P[a-z]+)(?P\d+))? diff --git a/alphabase/__init__.py b/alphabase/__init__.py index 6c9878f6..4564e973 100644 --- a/alphabase/__init__.py +++ b/alphabase/__init__.py @@ -2,7 +2,7 @@ __project__ = "alphabase" -__version__ = "1.2.6" +__version__ = "1.3.0" __license__ = "Apache" __description__ = "An infrastructure Python package of the AlphaX ecosystem" __author__ = "Mann Labs" diff --git a/docs/conf.py b/docs/conf.py index d44d5136..a62d5de0 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -24,7 +24,7 @@ copyright = "2022, Mann Labs, MPIB" author = "Mann Labs, MPIB" -release = "1.2.6" +release = "1.3.0" # -- General configuration --------------------------------------------------- diff --git a/release/one_click_linux_gui/control b/release/one_click_linux_gui/control index 7aee4103..1b6bfced 100644 --- a/release/one_click_linux_gui/control +++ b/release/one_click_linux_gui/control @@ -1,5 +1,5 @@ Package: AlphaBase -Version: 1.2.6 +Version: 1.3.0 Architecture: all Maintainer: Mann Labs Description: AlphaBase diff --git a/release/one_click_linux_gui/create_installer_linux.sh b/release/one_click_linux_gui/create_installer_linux.sh index 21d7015e..4cca8b70 100644 --- a/release/one_click_linux_gui/create_installer_linux.sh +++ b/release/one_click_linux_gui/create_installer_linux.sh @@ -17,7 +17,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_linux_gui # Make sure you include the required extra packages and always use the stable or very-stable options! -pip install "../../dist/alphabase-1.2.6-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.3.0-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller diff --git a/release/one_click_macos_gui/Info.plist b/release/one_click_macos_gui/Info.plist index 095489b2..3e85f54e 100644 --- a/release/one_click_macos_gui/Info.plist +++ b/release/one_click_macos_gui/Info.plist @@ -9,9 +9,9 @@ CFBundleIconFile alpha_logo.icns CFBundleIdentifier - alphabase.1.2.6 + alphabase.1.3.0 CFBundleShortVersionString - 1.2.6 + 1.3.0 CFBundleInfoDictionaryVersion 6.0 CFBundleName diff --git a/release/one_click_macos_gui/create_installer_macos.sh b/release/one_click_macos_gui/create_installer_macos.sh index b4230dd1..0df5bcde 100644 --- a/release/one_click_macos_gui/create_installer_macos.sh +++ b/release/one_click_macos_gui/create_installer_macos.sh @@ -20,7 +20,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_macos_gui -pip install "../../dist/alphabase-1.2.6-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.3.0-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller @@ -40,5 +40,5 @@ cp ../../LICENSE.txt Resources/LICENSE.txt cp ../logos/alpha_logo.png Resources/alpha_logo.png chmod 777 scripts/* -pkgbuild --root dist/alphabase --identifier de.mpg.biochem.alphabase.app --version 1.2.6 --install-location /Applications/AlphaBase.app --scripts scripts AlphaBase.pkg +pkgbuild --root dist/alphabase --identifier de.mpg.biochem.alphabase.app --version 1.3.0 --install-location /Applications/AlphaBase.app --scripts scripts AlphaBase.pkg productbuild --distribution distribution.xml --resources Resources --package-path AlphaBase.pkg dist/alphabase_gui_installer_macos.pkg diff --git a/release/one_click_macos_gui/distribution.xml b/release/one_click_macos_gui/distribution.xml index 8082a752..6f5812d5 100644 --- a/release/one_click_macos_gui/distribution.xml +++ b/release/one_click_macos_gui/distribution.xml @@ -1,6 +1,6 @@ - AlphaBase 1.2.6 + AlphaBase 1.3.0 diff --git a/release/one_click_windows_gui/alphabase_innoinstaller.iss b/release/one_click_windows_gui/alphabase_innoinstaller.iss index 6bd6a0b3..d6329ebe 100644 --- a/release/one_click_windows_gui/alphabase_innoinstaller.iss +++ b/release/one_click_windows_gui/alphabase_innoinstaller.iss @@ -2,7 +2,7 @@ ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES! #define MyAppName "AlphaBase" -#define MyAppVersion "1.2.6" +#define MyAppVersion "1.3.0" #define MyAppPublisher "Max Planck Institute of Biochemistry and the University of Copenhagen, Mann Labs" #define MyAppURL "https://github.com/MannLabs/alphabase" #define MyAppExeName "alphabase_gui.exe" diff --git a/release/one_click_windows_gui/create_installer_windows.sh b/release/one_click_windows_gui/create_installer_windows.sh index e64cf0eb..5ed3cf84 100644 --- a/release/one_click_windows_gui/create_installer_windows.sh +++ b/release/one_click_windows_gui/create_installer_windows.sh @@ -17,7 +17,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_windows_gui # Make sure you include the required extra packages and always use the stable or very-stable options! -pip install "../../dist/alphabase-1.2.6-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.3.0-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller

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