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DynamicBind

Source code for the Nature Communications paper DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.

DynamicBind recovers ligand-specific conformations from unbound protein structures (e.g. AF2-predicted structures), promoting efficient transitions between different equilibrium states.

Setup Environment

Create a new environment for inference. While in the project directory run

conda env create -f environment.yml

Or you setup step by step:

conda create -n dynamicbind python=3.10

Activate the environment

conda activate dynamicbind

Install

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
conda install -c conda-forge rdkit
conda install pyg  pyyaml  biopython -c pyg
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
pip install e3nn  fair-esm spyrmsd

Create a new environment for structural Relaxation.

conda create --name relax python=3.8

Activate the environment

conda activate relax

Install

conda install -c conda-forge openmm pdbfixer libstdcxx-ng openmmforcefields openff-toolkit ambertools=22 compilers biopython

Checkpoints Download

Download and unzip the workdir.zip containing the model checkpoint form https://zenodo.org/records/10137507, v2 is contained here https://zenodo.org/records/10183369.

Inference

Dynamic Docking

By default: 40 poses will be predicted, poses will be ranked (rank1 is the best-scoring pose, rank40 the lowest), relax processes are included.

Inputs:

  1. Protein (PDB File): protein.pdb
    • Automatically cleaned to remove non-standard amino acids, water molecules, or small molecules.
  2. Ligand (CSV File): ligand.csv
    • Must contain a column named 'ligand' listing smiles.
  3. Number of Animations:
    • outputs intermediate pkl data, not the final animation PDB. (After --savings_per_complex, default is 40)
  4. Frames in Animation/inference_steps:
    • default is 20.

Additional Options:

  • --header: Name of the result folder.
  • --device: GPU device ID.
  • --python: Python environment for inference.
  • --relax_python: Python environment for relaxation.
  • --num_workers: Number of processes for final output relaxation.

Example Command:

python run_single_protein_inference.py protein.pdb ligand.csv --savings_per_complex 40 --inference_steps 20 --header test --device $1 --python /gxr/luwei/anaconda3/envs/dynamicbind/bin/python --relax_python /gxr/luwei/anaconda3/envs/relax/bin/python

Docking Outputs

The results of the docking step, typically found in the results/test folder, include:

  1. Affinity Score for Each Complex: affinity_prediction.csv
  2. Pose Score and Conformation of Each Animation: Example files like rank1_ligand_lddt0.63_affinity5.67_relaxed.sdf (where 0.63 is the pose score) and corresponding protein .pdb files.
  3. Data for Animation Generation: Such as rank1_reverseprocess_data_list.pkl and rank2_reverseprocess_data_list.pkl.

Movie Generation

Inputs:

  1. Data from Docking Output: Indicated by paths like results/test/index0_idx_0/. The notation "1+2" implies that movies for rank1 and rank2 poses are needed.
  2. Number of Animations: Specified by the user (default is "1").

Example command for generating movies:

python movie_generation.py results/test/index0_idx_0/ 1+2 --device $1 --python /path/to/dynamicbind/python --relax_python /path/to/relax/python

Outputs:

  • Final Animation PDB Files: Located in results/test_1qg8/index0_idx_0/, with files like rank1_receptor_reverseprocess_relaxed.pdb and rank1_ligand_reverseprocess_relaxed.pdb.

High-Throughput Screening (HTS)

Example command for HTS:

python run_single_protein_inference.py protein.pdb ligand.csv --hts --savings_per_complex 3 --inference_steps 20 --header test --device $1 --python /path/to/dynamicbind/python --relax_python /path/to/relax/python

HTS Output files:

  • complete_affinity_prediction.csv
  • affinity_prediction.csv

Training and testing Dataset

https://zenodo.org/records/10429051

Reference

@article{lu2024dynamicbind,
  title={DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model},
  author={Lu, Wei and Zhang, Jixian and Huang, Weifeng and Zhang, Ziqiao and Jia, Xiangyu and Wang, Zhenyu and Shi, Leilei and Li, Chengtao and Wolynes, Peter G and Zheng, Shuangjia},
  journal={Nature Communications},
  volume={15},
  number={1},
  pages={1071},
  year={2024},
  publisher={Nature Publishing Group UK London}
}