add antibody developability from TDC

data/sabdab_chen/meta.yaml

@@ -0,0 +1,84 @@
+name: sabdab_chen
+description: |-
+  Antibody data from Chen et al, where they process from the SAbDab.
+  From an initial dataset of 3816 antibodies, they retained 2426 antibodies that
+  satisfy the following criteria:  1.have both sequence (FASTA) and Protein Data
+  Bank (PDB) structure files, 2. contain both a heavy chain and a light chain,
+  and  3.have crystal structures with resolution < 3 A. The DI label is derived
+  from BIOVIA's pipelines.
+targets:
+- id: developability
+  description: functional antibody candidate to be developed into a manufacturable(1),
+    or not(0)
+  units: ''
+  type: categorical
+  names:
+  - antibody developability
+  - monoclonal anitbody
+  - functional antibody candidate
+  - manufacturable, stable, safe, and effective antibody drug
+  uris:
+  - https://rb.gy/idkdqp
+  - https://rb.gy/b8cx8i
+benchmarks:
+- name: TDC
+  link: https://tdcommons.ai/
+  split_column: split
+identifiers:
+- id: antibody_pdb_ID
+  type: Other
+  names:
+  - pdb id
+  - Protein Data Bank id
+  description: anitbody pdb id
+- id: heavy_chain
+  type: Other
+  names:
+  - Fastq
+  - gene sequence
+  description: anitbody heavy chain amino acid sequence in FASTA
+- id: light_chain
+  type: Other
+  names:
+  - Fastq
+  - gene sequence
+  description: anitbody light chain amino acid sequence in FASTA
+license: CC BY 4.0
+links:
+- url: https://doi.org/10.1101/2020.06.18.159798
+  description: corresponding publication
+- url: https://doi.org/10.1093/nar/gkt1043
+  description: corresponding publication
+- url: https://www.3ds.com/products-services/biovia/products/data-science/pipeline-pilot/
+  description: corresponding tools used
+- url: https://tdcommons.ai/single_pred_tasks/develop/#sabdab-chen-et-al
+  description: data source
+num_points: 2409
+bibtex:
+- |-
+  @article{Chen2020,
+  doi = {10.1101/2020.06.18.159798},
+  url = {https://doi.org/10.1101/2020.06.18.159798},
+  year = {2020},
+  month = jun,
+  publisher = {Cold Spring Harbor Laboratory},
+  author = {Xingyao Chen and Thomas Dougherty and
+  Chan Hong and Rachel Schibler and Yi Cong Zhao and
+  Reza Sadeghi and Naim Matasci and Yi-Chieh Wu and Ian Kerman},
+  title = {Predicting Antibody Developability from Sequence
+  using Machine Learning}
+- |-
+  @article{Dunbar2013,
+  doi = {10.1093/nar/gkt1043},
+  url = {https://doi.org/10.1093/nar/gkt1043},
+  year = {2013},
+  month = nov,
+  publisher = {Oxford University Press ({OUP})},
+  volume = {42},
+  number = {D1},
+  pages = {D1140--D1146},
+  author = {James Dunbar and Konrad Krawczyk and Jinwoo Leem
+  and Terry Baker and Angelika Fuchs and Guy Georges and Jiye Shi and
+  Charlotte M. Deane},
+  title = {SAbDab: the structural antibody database},
+  journal = {Nucleic Acids Research}

data/sabdab_chen/transform.py

@@ -0,0 +1,170 @@
+import pandas as pd
+import yaml
+from tdc.single_pred import Develop
+
+
+def get_and_transform_data():
+    # get raw data
+    target_subfolder = "SAbDab_Chen"
+    data = Develop(name=target_subfolder)
+
+    # proceed raw data
+    df = data.get_data()
+    fields_orig = df.columns.tolist()
+    assert fields_orig == ["Antibody_ID", "Antibody", "Y"]
+
+    fn_data_original = "data_original.csv"
+
+    antibody_list = df.Antibody.tolist()
+    s2l = lambda list_string: list(
+        map(str.strip, list_string.strip("][").replace("'", "").split(","))
+    )
+    df["heavy_chain"] = [s2l(x)[0] for x in antibody_list]
+    df["light_chain"] = [s2l(x)[1] for x in antibody_list]
+    df = df[["Antibody_ID", "heavy_chain", "light_chain", "Y"]]
+    df.to_csv(fn_data_original, index=False)
+
+    #  load raw data and assert columns
+    df = pd.read_csv(fn_data_original, sep=",")
+    fields_orig = df.columns.tolist()
+    assert fields_orig == ["Antibody_ID", "heavy_chain", "light_chain", "Y"]
+    fields_clean = ["antibody_pdb_ID", "heavy_chain", "light_chain", "developability"]
+    df.columns = fields_clean
+    assert not df.duplicated().sum()
+
+    # save to csv
+    fn_data_csv = "data_clean.csv"
+    df.to_csv(fn_data_csv, index=False)
+
+    meta = {
+        "name": "sabdab_chen",  # unique identifier, we will also use this for directory names
+        "description": """Antibody data from Chen et al, where they process from the SAbDab.
+From an initial dataset of 3816 antibodies, they retained 2426 antibodies that
+satisfy the following criteria:  1.have both sequence (FASTA) and Protein Data
+Bank (PDB) structure files, 2. contain both a heavy chain and a light chain,
+and  3.have crystal structures with resolution < 3 A. The DI label is derived
+from BIOVIA's pipelines.""",
+        "targets": [
+            {
+                "id": "developability",  # name of the column in a tabular dataset
+                "description": "functional antibody candidate to be developed into a manufacturable(1), or not(0)",
+                "units": "",  # units of the values in this column (leave empty if unitless)
+                "type": "categorical",  # can be "categorical", "ordinal", "continuous"
+                "names": [  # names for the property (to sample from for building the prompts)
+                    "antibody developability",
+                    "monoclonal anitbody",
+                    "functional antibody candidate",
+                    "manufacturable, stable, safe, and effective antibody drug",
+                ],
+                "uris": [
+                    "https://rb.gy/idkdqp",
+                    "https://rb.gy/b8cx8i",
+                ],
+            },
+        ],
+        "benchmarks": [
+        {
+            "name": "TDC",  # unique benchmark name
+            "link": "https://tdcommons.ai/",  # benchmark URL
+            "split_column": "split",  # name of the column that contains the split information
+        },
+        ],
+        "identifiers": [
+            {
+                "id": "antibody_pdb_ID",  # column name
+                "type": "Other",  # can be "SMILES", "SELFIES", "IUPAC", "Other"
+                "names":[
+                "pdb id",
+                "Protein Data Bank id",
+                ],
+                "description": "anitbody pdb id",  # description (optional, except for "Other")
+            },
+            {
+                "id": "heavy_chain",  # column name
+                "type": "Other",  # can be "SMILES", "SELFIES", "IUPAC", "Other"
+                "names":[
+                "Fastq",
+                "gene sequence",
+                ],
+                "description": "anitbody heavy chain amino acid sequence in FASTA",  # description (optional, except for "Other")
+            },
+            {
+                "id": "light_chain",  # column name
+                "type": "Other",  # can be "SMILES", "SELFIES", "IUPAC", "Other"
+                "names":[
+                "Fastq",
+                "gene sequence",
+                ],
+                "description": "anitbody light chain amino acid sequence in FASTA",  # description (optional, except for "Other")
+            },
+        ],
+        "license": "CC BY 4.0",  # license under which the original dataset was published
+        "links": [  # list of relevant links (original dataset, other uses, etc.)
+            {
+                "url": "https://doi.org/10.1101/2020.06.18.159798",
+                "description": "corresponding publication",
+            },
+            {
+                "url": "https://doi.org/10.1093/nar/gkt1043",
+                "description": "corresponding publication",
+            },
+            {
+                "url": "https://www.3ds.com/products-services/biovia/products/data-science/pipeline-pilot/",
+                "description": "corresponding tools used",
+            },
+            {
+                "url": "https://tdcommons.ai/single_pred_tasks/develop/#sabdab-chen-et-al",
+                "description": "data source",
+            },
+        ],
+        "num_points": len(df),  # number of datapoints in this dataset
+        "bibtex": [
+            """@article{Chen2020,
+doi = {10.1101/2020.06.18.159798},
+url = {https://doi.org/10.1101/2020.06.18.159798},
+year = {2020},
+month = jun,
+publisher = {Cold Spring Harbor Laboratory},
+author = {Xingyao Chen and Thomas Dougherty and
+Chan Hong and Rachel Schibler and Yi Cong Zhao and
+Reza Sadeghi and Naim Matasci and Yi-Chieh Wu and Ian Kerman},
+title = {Predicting Antibody Developability from Sequence
+using Machine Learning}""",
+            """@article{Dunbar2013,
+doi = {10.1093/nar/gkt1043},
+url = {https://doi.org/10.1093/nar/gkt1043},
+year = {2013},
+month = nov,
+publisher = {Oxford University Press ({OUP})},
+volume = {42},
+number = {D1},
+pages = {D1140--D1146},
+author = {James Dunbar and Konrad Krawczyk and Jinwoo Leem
+and Terry Baker and Angelika Fuchs and Guy Georges and Jiye Shi and
+Charlotte M. Deane},
+title = {SAbDab: the structural antibody database},
+journal = {Nucleic Acids Research}""",
+        ],
+    }
+
+    def str_presenter(dumper, data):
+        """configures yaml for dumping multiline strings
+        Ref: https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data
+        """
+        if data.count("\n") > 0:  # check for multiline string
+            return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|")
+        return dumper.represent_scalar("tag:yaml.org,2002:str", data)
+
+    yaml.add_representer(str, str_presenter)
+    yaml.representer.SafeRepresenter.add_representer(
+        str, str_presenter
+    )  # to use with safe_dum
+    fn_meta = "meta.yaml"
+    with open(fn_meta, "w") as f:
+        yaml.dump(meta, f, sort_keys=False)
+
+    print(f"Finished processing {meta['name']} dataset!")
+
+
+if __name__ == "__main__":
+    get_and_transform_data()

data/tap/meta.yaml

@@ -0,0 +1,118 @@
+name: tap
+description: |-
+  Immunogenicity, instability, self-association,
+  high viscosity, polyspecificity, or poor expression can all preclude
+  an antibody from becoming a therapeutic. Early identification of these
+  negative characteristics is essential. Akin to the Lipinski guidelines,
+  which measure druglikeness in small molecules,
+  Therapeutic Antibody Profiler (TAP) highlights antibodies
+  that possess characteristics that are rare/unseen in
+  clinical-stage mAb therapeutics.
+targets:
+- id: CDR_Length
+  description: CDR Complementarity-determining regions length
+  units: ''
+  type: continuous
+  names:
+  - Antibody Complementarity-determining regions length
+  - Therapeutic Antibody Profiler
+  - antibody developability
+  - monoclonal anitbody
+  uris:
+  - https://rb.gy/s9gv88
+  - https://rb.gy/km77hq
+  - https://rb.gy/b8cx8i
+- id: PSH
+  description: patches of surface hydrophobicity
+  units: ''
+  type: continuous
+  names:
+  - antibody patches of surface hydrophobicity
+  - Therapeutic Antibody Profiler
+  - antibody developability
+  - monoclonal anitbody
+  uris:
+  - https://rb.gy/bchhaa
+  - https://rb.gy/2irr4l
+  - https://rb.gy/b8cx8i
+- id: PPC
+  description: patches of positive charge
+  units: ''
+  type: continuous
+  names:
+  - patches of positive charge
+  - Therapeutic Antibody Profiler
+  - antibody developability
+  - monoclonal anitbody
+  uris:
+  - https://rb.gy/b8cx8i
+- id: PNC
+  description: patches of negative charge
+  units: ''
+  type: continuous
+  names:
+  - anitbody patches of negative charge
+  - Therapeutic Antibody Profiler
+  - antibody developability
+  - monoclonal anitbody
+  uris:
+  - https://rb.gy/b8cx8i
+- id: SFvCSP
+  description: structural Fv charge symmetry parameter
+  units: ''
+  type: continuous
+  names:
+  - antibody structural Fv charge symmetry parameter
+  - Therapeutic Antibody Profiler
+  - antibody developability
+  - monoclonal anitbody
+  uris:
+  - https://rb.gy/uxyhc3
+  - https://rb.gy/b8cx8i
+benchmarks:
+- name: TDC
+  link: https://tdcommons.ai/
+  split_column: split
+identifiers:
+- id: antibody_name
+  type: Other
+  names:
+  - Name of the antibody
+  - Name of the antibody drug
+  - Name of drug
+  description: anitbody name
+- id: heavy_chain
+  type: Other
+  names:
+  - Fastq
+  - gene sequence
+  description: anitbody heavy chain amino acid sequence
+- id: light_chain
+  type: Other
+  names:
+  - Fastq
+  - gene sequence
+  description: anitbody light chain amino acid sequence
+license: CC BY 4.0
+links:
+- url: https://doi.org/10.1073/pnas.1810576116
+  description: corresponding publication
+- url: https://tdcommons.ai/single_pred_tasks/develop/#tap
+  description: data source
+num_points: 241
+bibtex:
+- |-
+  @article{Raybould2019,
+  doi = {10.1073/pnas.1810576116},
+  url = {https://doi.org/10.1073/pnas.1810576116},
+  year = {2019},
+  month = feb,
+  publisher = {Proceedings of the National Academy of Sciences},
+  volume = {116},
+  number = {10},
+  pages = {4025--4030},
+  author = {Matthew I. J. Raybould and Claire Marks and Konrad Krawczyk
+  and Bruck Taddese and Jaroslaw Nowak and Alan P. Lewis and Alexander Bujotzek
+  and Jiye Shi and Charlotte M. Deane},
+  title = {Five computational developability guidelines for therapeutic antibody profiling},
+  journal = {Proceedings of the National Academy of Sciences}