PocketGen

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PocketGen (Nature Machine Intelligence 24): Generating Full-Atom Ligand-Binding Protein Pockets

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# :loudspeaker: PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets

## Environment

### Install conda environment via conda yaml file
```bash
conda env create -f pocketgen.yaml
conda activate pocketgen
```

### Install via Conda and Pip
```bash
conda create -n pocketgen python=3.8
conda activate pocketgen
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](https://github.com/guanjq/targetdiff)  
Firstly download the [crossdocked_v1.1_rmsd1.0.tar.gz](https://drive.google.com/file/d/1U0ZgITApL7EClcQiiVK_OevAV_H20L4d/view?usp=sharing) and [split_by_name.pt](https://drive.google.com/file/d/1UVJmLvx-kcorMyDDR_LPCqR8dFPuoRtI/view?usp=sharing) 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](https://github.com/arneschneuing/DiffSBDD)
Download the dataset
```bash
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
``` bash
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](https://zenodo.org/records/10125312)

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 ./configs/train_model.yml
```
Train on Binding MOAD:
```
python train_recycle.py --config ./configs/train_model_moad.yml
```

## Model Checkpoints
Pretrained checkpoint on the CrossDocked training dataset: [checkpoint.pt](https://drive.google.com/file/d/1cuvdiu3bXyni71A2hoeZSWT1NOsNfeD_/view?usp=sharing)

## Generation
```
python generate_new.py
```
We provide one example of the generated pocket for pdbid-2p16 and visualize the interactions with [plip](https://github.com/pharmai/plip)  
For generation, please create a tmp dir under the running fold. 
<div align=center>
<img src="./assets/2p16.png" width="50%" height="50%" alt="TOC" align=center />
</div>

## Evaluation
The code to compute self-consistency-related scores, such as scRMSD, scTM, and pLDDT can be found at [eval](https://github.com/aqlaboratory/genie/tree/main).

The code to run protein-ligand interaction analysis is [interaction.](https://github.com/zaixizhang/PocketGen/blob/efdfb6effe57deb72e765b27cad4cd6ed2ef74f5/evaluation/protein_ligand_interaction.py)

## Acknowledgement
This project draws in part from [TargetDiff](https://github.com/guanjq/targetdiff) and [ByProt](https://github.com/BytedProtein/ByProt), supported by MIT License and Apache-2.0 License. Thanks for their great work and code!

## Contact

Zaixi Zhnag (zz8680@princeton.edu)

Sincerely appreciate your suggestions on our work!

## License

This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/zaixizhang/Pocket-Generation/blob/main/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}
}
```

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