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License: MIT bioRxiv

ApoDock

ApoDock is a modular docking paradigm that combines machine learning-driven conditional side-chain packing based on protein backbone and ligand information with traditional sampling methods to ensure physically realistic poses.

To run ApoDock, clone this GitHub repository and install Python.

Requirements

  • Operating System: Linux (Recommended)

Install Dependencies

conda env create -f environment.yaml
conda activate apodock

Then install gvp-gnn:

cd ApoDock_public
git clone https://github.com/drorlab/gvp-pytorch.git
cd gvp-pytorch
pip install -e .

Quick start

First, you need to download the binary sampling software Gnina or Smina to docking_program dir, then give execution permission:

chmod +x Gnina
chmod +x Smina.static

Then set the enviroment path:

export PATH="$PATH:/your/path/to/ApoDock_public/docking_program"

Inference

A demo:

python docking.py --protein ./demo/1a0q/1a0q_protein.pdb --ligand ./demo/1a0q/1a0q_ligand.sdf --ref_lig ./demo/1a0q/1a0q_ligand.sdf --packing

Use .CSV file for docking:

python docking.py --csv docking_list.csv --packing

Output example will in defalut dir docking_results, you can use --out_dir option to determine the output position.


Acknowledgements

This work draws upon code from ProteinMPNN, OpenFold, RTMscore, and PIPPack, and we would like to thank them for their excellent contributions. these studies are important and interesting.

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