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.

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

conda env create -f environment_dynamicbind.yml
conda env create -f environment_relax.yml

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.

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

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.

• --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.

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

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.

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").

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.

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

https://zenodo.org/records/10429051

@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}
}