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PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

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# PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

This repo contains a PyTorch implementation for the paper  PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

If you have any questions, please open an issue or contact [Runze Zhang](runze.zhang@icm.uu.se)✉️.

# PackDock Overview
![](https://github.com/Zhang-Runze/PackDock/blob/main/figs/Method%20Overview.jpg)

# Setup Environment

We recommend setting up the environment using [Anaconda](https://docs.anaconda.com/free/anaconda/install/index.html).

Clone the current repo

git clone https://github.com/Zhang-Runze/PackDock.git
    
This is an example for how to set up a working conda environment to run the code (but make sure to use the correct pytorch, pytorch-geometric, cuda versions or cpu only versions):

conda create --name packdock python=3.8
    conda activate packdock
    conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
    pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cu117.html
    python -m pip install openbabel PyYAML scipy "networkx[default]" biopython rdkit-pypi e3nn spyrmsd pandas biopandas
    conda install -c conda-forge openmm pdbfixer
    
Then you need to install a ligand conformation sampling algorithm (such as [AutoDock-Vina](https://github.com/ccsb-scripps/AutoDock-Vina), [gnina](https://github.com/gnina/gnina), [Vina-GPU](https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0), etc.).
It's worth noting that PackDock offers a highly general flexible docking strategy, capable of integrating any ligand conformation sampling algorithm develop yourself or you might choose to use. This implies that employing more  advanced ligand conformation sampling algorithms could potentially lead to unexpectedly impressive docking results.

# Data preprocess
First run:

python get_pocket/get_pocket.py
    
Then proceed with further processing using [ADFR-Suite](https://ccsb.scripps.edu/adfr/downloads/) and OpenBabel.

# Model weights

The model weights are available on [zenodo](https://zenodo.org/records/10851699).

# Running PackPocket on your protein

Protein pocket packing:

python -m evaluate_protein --run_name Protein pocket packing --inference_steps 20 --samples_per_complex 10 --batch_size 10 --actual_steps 20 --cache_path data/cacheTest_CASP14 --no_final_step_noise --data_dir data/CASP/CASP14/ --split_path data/splits/CASP14_list --save_visualisation

Ligand based protein pocket packing:

python -m evaluate_ligand_based_protein --run_name Ligand based protein pocket packing --inference_steps 20 --samples_per_complex 10 --batch_size 10 --actual_steps 20 --cache_path data/cacheTest_PDBBind --no_final_step_noise --data_dir data/PDBBind_processed --split_path data/splits/timesplit_test_no_rec_overlap --save_visualisation
    
# Running PackDock on your complex

(1) Prepare `input_csv`

Required columns: `protein_path, ligand_path, complex_id`

`protein_path`: Path to the protein structure (typically .pdb, or a pre-extracted pocket PDB).

`ligand_path`: Path to the ligand structure (.sdf/.pdb/.mol2/.mol supported).

`complex_id`: Unique sample identifier(writes outputs to `--out_dir/<complex_id>/`).

(2)

python packdock_pipeline.py 
          --input_csv /path/to/input.csv \
          --out_dir results/packdock_run \
          --protein_model_dir workdir/protein_model \
          --model_dir workdir/complex_model \
          --inference_steps 20 \
          --batch_size 40 \
          --stage1_packing_num 1 \
          --stage3_packing_num 5 \
          --docking_max_workers 1 \
          --extract_pocket \
          --pocket_radius 5.0 \

(3) Key arguments

|  Argument   | Default  |  Description  |
|  ----  | ----  | ----  |
| `--input_csv`  | (required) | Sample list with `protein_path`, `ligand_path`, `complex_id`. |
| `--out_dir`  | `results/test` | Root folder for outputs, manifests, and scores. |
| `--protein_model_dir`  | `workdir/protein_model` | Folder containing the ligand-free packing model. |
| `--model_dir`  | `workdir/complex_model` | Folder containing the ligand-conditioned packing model. |
| `--inference_steps`  | `20` | Diffusion sampling steps for packing. |
| `--batch_size`  | `40` | Packing batch size. |
| `--stage1_packing_num`  | `1` | ligand-free packing samples per input graph (Stage 1). |
| `--stage3_packing_num`  | `5` | ligand-conditioned packing samples per graph (Stage 3). |
| `--docking_max_workers`  | `1` | Parallel docking workers |
| `--extract_pocket`  | `True` | Automatically extract the pocket around the ligand. |
| `--pocket_radius`  | `5.0` | Pocket radius (Å) used for extraction. |
| `--relax-stage1`  | `enabled` | Run OpenMM relaxation after Stage 1 |
| `--relax-stage3`  | `enabled` | Run OpenMM relaxation after Stage 3 |

# Retraining PackPocket
Download the data(BC40 or PDBbind and place it as described in the "Dataset" section above.

### Training a model yourself and using those weights
Train the PackPocket:

python -m train_protein --run_name PackPocket_protein --test_sigma_intervals  --log_dir workdir --lr 1e-3 --batch_size 8 --ns 48 --nv 10 --num_conv_layers 6 --dynamic_max_cross --scheduler plateau --scale_by_sigma --dropout 0.1 --remove_hs --c_alpha_max_neighbors 24 --receptor_radius 30.0 --atom_radius 5.0 --cross_distance_embed_dim 64 --distance_embed_dim 64 --sigma_embed_dim 64 --cross_max_distance 20 --num_dataloader_workers 36 --cudnn_benchmark --val_inference_freq 5 --num_inference_complexes 100 --use_ema --scheduler_patience 30 --n_epochs 300 --all_atoms --num_worker 36 --no_torsion --data_dir data/bc40_pockets_processed/ --split_train data/splits/bc40_train_set --split_val data/splits/bc40_validation_set --split_test data/splits/bc40_test_set

Train the ligand-based PackPocket:

python -m train_ligand_based_protein --run_name PackPocket_ligand_based_protein --test_sigma_intervals  --log_dir workdir --lr 1e-3 --batch_size 8 --ns 48 --nv 10 --num_conv_layers 6 --dynamic_max_cross --scheduler plateau --scale_by_sigma --dropout 0.1 --remove_hs --c_alpha_max_neighbors 24 --receptor_radius 30.0 --atom_radius 5.0 --cross_distance_embed_dim 64 --distance_embed_dim 64 --sigma_embed_dim 64 --cross_max_distance 20 --num_dataloader_workers 1 --cudnn_benchmark --val_inference_freq 5 --num_inference_complexes 100 --use_ema --scheduler_patience 30 --n_epochs 300 --all_atoms --num_worker 36 --no_torsion --data_dir data/PDBBind_processed/ --split_train data/splits/timesplit_no_lig_overlap_train --split_val data/splits/timesplit_no_lig_overlap_val --split_test data/splits/timesplit_test_no_rec_overlap

The model weights are saved in the `workdir` directory.

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