DynAlloBind is an enhanced deep learning framework for protein–ligand virtual screening and structure prediction based on apo protein structures. It extends the capabilities of DynamicBind by incorporating fine-tuning on a strategically augmented dataset, with a particular focus on G-protein-coupled receptors (GPCRs). This specialization substantially improves model performance in predicting ligand-induced conformational changes and allosteric binding site dynamics within GPCR systems.

The model can:

1. Generate ligand-bound holo conformations from apo protein inputs

2. Predict ligand binding poses and affinity scores

3. Support large-scale virtual screening

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

Download and unzip the workdir.zip containing the model checkpoint from https://drive.google.com/file/d/1-GTtaEFavlYkPpsC9S60HzmZ6TjO3kj4/view?usp=drive_link

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 ./data/8y6y.pdb ./data/8y6y.csv --savings_per_complex 40 --inference_steps 20 --header test --device $1 --python /path/to/dynamicbind/python --relax_python /path/to/relax/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_8y6y/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

• Use AlphaFold2/3 to predict the protein structure.
• Use the predicted protein structure to run DynAlloBind for prediction as described above.
• Compare the results with the ground truth to calculate the RMSD.

This project builds upon and extends the DynamicBind framework,
originally developed by Wei Lu (陆威) under the MIT License.
All rights and credits for the original framework belong to the original author.