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:loudspeaker: PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets
<div align=center> <img src="./assets/PocketGen.gif" width="60%" height="60%" alt="TOC" align=center /> </div> <div align=center> <img src="./assets/pocketgen.png" width="100%" height="100%" alt="TOC" align=center /> </div>Environment
Install conda environment via conda yaml file
conda env create -f pocketgen.yaml
conda activate pocketgen
Install via Conda and Pip
conda create -n targetdiff python=3.8
conda activate targetdiff
conda install pytorch pytorch-cuda=11.6 -c pytorch -c nvidia
conda install pyg -c pyg
conda install rdkit openbabel tensorboard pyyaml easydict python-lmdb -c conda-forge
conda install -c conda-forge openmm pdbfixer flask
conda install -c conda-forge numpy swig boost-cpp sphinx sphinx_rtd_theme
pip install meeko==0.1.dev3 wandb scipy pdb2pqr vina==1.2.2
python -m pip install git+https://github.com/Valdes-Tresanco-MS/AutoDockTools_py3
Benchmark Datasets
We use CrossDocked and Binding MOAD datasets to benchmark pocket generation.
CrossDocked
We download and process the CrossDocked dataset as described by the authors of TargetDiff
Firstly download the crossdocked_v1.1_rmsd1.0.tar.gz and split_by_name.pt and put it under the ./data directory.
Use the following commands to extract pockets, create index_seq.pkl, and split the dataset.
python data_preparation/extract_pockets.py
python data_preparation/split_pl_dataset.py
Binding MOAD
We download and process the Binding MOAD dataset following the authors of DiffSBDD Download the dataset
wget http://www.bindingmoad.org/files/biou/every_part_a.zip
wget http://www.bindingmoad.org/files/biou/every_part_b.zip
wget http://www.bindingmoad.org/files/csv/every.csv
unzip every_part_a.zip
unzip every_part_b.zip
Process the raw data using
python -W ignore process_bindingmoad.py <bindingmoad_dir>
Use the following commands to extract pockets, create index_seq.pkl, and split the dataset.
python data_preparation/extract_pockets_moad.py
python data_preparation/split_pl_dataset_moad.py
Processed datasets
We also provide the processed datasets for training from scratch at zenodo
For each dataset, it requires the preprocessed .lmdb file and split file _split.pt
Benchmark Results
Benchmarking PocketGen and other approaches for pocket generation on two datasets. Reported are average and standard deviation values across three independent runs. The best results are bolded.
| Model | AAR (↑) CrossDocked | Designability (↑) CrossDocked | Vina (↓) CrossDocked | AAR (↑) Binding MOAD | Designability (↑) Binding MOAD | Vina (↓) Binding MOAD |
|---|---|---|---|---|---|---|
| Test set | - | 0.77 | -7.016 | - | 0.79 | -8.076 |
| DEPACT | 31.52±3.26% | 0.68±0.04 | -6.632±0.18 | 35.30±2.19% | 0.67±0.06 | -7.571±0.15 |
| dyMEAN | 38.71±2.16% | 0.71±0.03 | -6.855±0.06 | 41.22±1.40% | 0.70±0.03 | 0.71±0.04 |
| FAIR | 40.16±1.17% | 0.73±0.02 | -7.015±0.12 | 43.68±0.92% | 0.72±0.05 | -7.930±0.15 |
| RFDiffusion | 46.57±2.07% | 0.74±0.01 | -6.936±0.07 | 45.31±2.73% | 0.75±0.05 | -7.942±0.14 |
| RFDiffusionAA | 50.85±1.85% | 0.75±0.03 | -7.012±0.09 | 49.09±2.49% | 0.78±0.03 | -8.020±0.11 |
| PocketGen | 63.40±1.64% | 0.77±0.02 | -7.135±0.08 | 64.43±2.35% | 0.80±0.04 | -8.112±0.14 |
Training
Train on CrossDocked:
python train_recycle.py --config ./config/train_model.yml
Train on Binding MOAD:
python train_recycle.py --config ./config/train_model_moad.yml
Model Checkpoints
Pretrained checkpoint on the CrossDocked training dataset: checkpoint.pt
Generation
python generate_new.py
We provide one example of the generated pocket for pdbid-2p16 and visualize the interactions with plip
For generation, please create a tmp dir under the running fold.
Evaluation
The code to compute self-consistency-related scores, such as scRMSD, scTM, and pLDDT can be found at eval.
The code to run protein-ligand interaction analysis is interaction.
Acknowledgement
This project draws in part from TargetDiff and ByProt, supported by MIT License and Apache-2.0 License. Thanks for their great work and code!
Contact
Zaixi Zhnag (zaixi@mail.ustc.edu.cn)
Sincerely appreciate your suggestions on our work!
License
This project is licensed under the terms of the MIT license. See LICENSE for additional details.
Reference
@article{zhang2024efficient,
title={Efficient generation of protein pockets with PocketGen},
author={Zhang, Zaixi and Shen, Wan Xiang and Liu, Qi and Zitnik, Marinka},
journal={Nature Machine Intelligence},
pages={1--14},
year={2024},
publisher={Nature Publishing Group UK London}
}