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# Uni-Fold: an open-source platform for developing protein folding models beyond AlphaFold. | ||
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Uni-Fold is a thoroughly open-source platform for developing protein folding models beyond [AlphaFold](https://github.com/deepmind/alphafold/), with following advantages: | ||
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- Reimplemented AlphaFold and AlphaFold-Multimer models in PyTorch framework. The first project for AlphaFold-Multimer training. | ||
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- Model correctness proved by successful from-scratch training with equivalent accuracy. | ||
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- Highest efficiency among existing AlphaFold implementations. | ||
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- Rich features from [Uni-Core](https://github.com/dptech-corp/Uni-Core/), such as efficient fp16/bf16 training, per sample gradient clipping, and fused kernels. | ||
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The name Uni-Fold is inherited from Uni-Fold-JAX. First released on Dec 8 2021, [Uni-Fold-JAX](https://github.com/dptech-corp/Uni-Fold-jax) was the first open-source project (with training scripts) that successfully reproduced the from-scratch training of AlphaFold. Until now, Uni-Fold-JAX is still the only project that supports training of the original AlphaFold implementation in JAX framework. Due to efficiency and collaboration considerations, we moved from Jax to PyTorch on Jan 2022, based on which we further developed the multimer models. | ||
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## Installation | ||
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## Preparing the datasets | ||
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Training and inference with Uni-Fold require homology searches on sequence and structure databases. Use the following command to download these databases: | ||
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```bash | ||
bash scripts/download_all_data.sh /path/to/database/directory | ||
``` | ||
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Make sure there is at least 3TB storage space for downloading (~500GB) and uncompressing the databases. | ||
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## Downloading the pre-trained model parameters | ||
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Parameters are coming soon :) | ||
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<!-- Inferenece and finetuning with Uni-Fold requires pretrained model parameters. Use the following command to download the parameters: --> | ||
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## Converting the AlphaFold and OpenFold parameters to Uni-Fold | ||
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[converting scripts] | ||
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## Running Uni-Fold | ||
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After properly configurating the environment and databases, run the following command to predict the structure of the input fasta: | ||
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```bash | ||
bash run_unifold.sh \ | ||
path/to/the/input.fasta \ # fasta_path | ||
path/to/the/output/directory/ \ # output_dir_base | ||
path/to/the/databases \ # database_dir | ||
2020-05-01 \ # max_template_date | ||
model_2_af2 \ # model_name | ||
path/to/model_parameters.pt # param_path | ||
``` | ||
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[More descriptions for model names and ckp names] | ||
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## Uni-Fold outputs | ||
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[Explanations on Uni-Fold Outputs] | ||
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## Training Uni-Fold | ||
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### Monomer Model | ||
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### Multimer Model | ||
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## Citing this work | ||
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Citation is coming soon :) | ||
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<!-- If you use the code or data in this package, please cite: | ||
```bibtex | ||
``` --> | ||
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## Acknowledgements | ||
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Our training framework is based on [Uni-Core](https://github.com/dptech-corp/Uni-Core/), and fused operators are from [fused_ops](https://github.com/guolinke/fused_ops/). Some of the PyTorch implementations refer to an early version of [OpenFold](https://github.com/aqlaboratory/openfold), while mostly follow the original codes of [AlphaFold](https://github.com/deepmind/alphafold/). For the data processing part, we follow [AlphaFold](https://github.com/deepmind/alphafold/), and use [Biopython](https://biopython.org/), [HH-suite3](https://github.com/soedinglab/hh-suite/), [HMMER](http://eddylab.org/software/hmmer/), [Kalign](https://msa.sbc.su.se/cgi-bin/msa.cgi), [pandas](https://pandas.pydata.org/), [NumPy](https://numpy.org/), and [SciPy](https://scipy.org/). | ||
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## License and Disclaimer | ||
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Copyright 2022 DP Technology. | ||
### Uni-Fold Code License | ||
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Follow AlphaFold, Uni-Fold is licensed under permissive Apache Licence, Version 2.0. | ||
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### Model Parameters License | ||
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The Uni-Fold parameters are made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can find details at: https://creativecommons.org/licenses/by/4.0/legalcode |
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fasta_path=$1 | ||
output_dir_base=$2 | ||
database_dir=$3 | ||
max_template_date=$4 | ||
model_name=$5 | ||
param_path=$6 | ||
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echo "Starting homogeneous searching..." | ||
python unifold/homo_search.py \ | ||
--fasta_path=$fasta_path \ | ||
--max_template_date=$max_template_date \ | ||
--output_dir=$output_dir_base \ | ||
--uniref90_database_path=$database_dir/uniref90/uniref90.fasta \ | ||
--mgnify_database_path=$database_dir/mgnify/mgy_clusters_2018_12.fa \ | ||
--bfd_database_path=$database_dir/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \ | ||
--uniclust30_database_path=$database_dir/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \ | ||
--uniprot_database_path=$database_dir/uniprot_db/uniprot_220501/uniprot_trembl.fasta \ | ||
--pdb_seqres_database_path=$database_dir/pdb_seqres/pdb_seqres.txt \ | ||
--template_mmcif_dir=$database_dir/pdb_mmcif/mmcif_files \ | ||
--obsolete_pdbs_path=$database_dir/pdb_mmcif/obsolete.dat \ | ||
--use_precomputed_msas=True | ||
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echo "Starting prediction..." | ||
fasta_dir=$(dirname $fasta_path) | ||
target_name=${fasta_dir##*/} | ||
python unifold/inference.py \ | ||
--model_name=$model_name \ | ||
--param_path=$param_path \ | ||
--data_dir=$output_dir_base \ | ||
--target_name=$target_name \ | ||
--output_dir=$output_dir_base |
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# Copyright 2022 DP Technology | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
"""Install script for setuptools.""" | ||
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from setuptools import find_packages | ||
from setuptools import setup | ||
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setup( | ||
name="unifold", | ||
version="2.0.0", | ||
description="An open-source platform for developing protein folding models beyond AlphaFold.", | ||
author="DP Technology", | ||
author_email="unifold@dp.tech", | ||
license="Apache License, Version 2.0", | ||
url="https://github.com/dptech-corp/Uni-Fold", | ||
packages=find_packages( | ||
exclude=["scripts", "tests", "example_data", "docker", "benchmark"] | ||
), | ||
install_requires=[ | ||
"absl-py", | ||
"biopython", | ||
"ml-collections", | ||
"numpy", | ||
"pandas", | ||
"scipy", | ||
], | ||
classifiers=[ | ||
"Development Status :: 5 - Production/Stable", | ||
"Intended Audience :: Science/Research", | ||
"License :: OSI Approved :: Apache Software License", | ||
"Operating System :: POSIX :: Linux", | ||
"Programming Language :: Python :: 3.6", | ||
"Programming Language :: Python :: 3.7", | ||
"Programming Language :: Python :: 3.8", | ||
"Programming Language :: Python :: 3.9", | ||
"Programming Language :: Python :: 3.10", | ||
"Topic :: Scientific/Engineering :: Artificial Intelligence", | ||
], | ||
) |