Overview

- Please note that the  repository is for reference purposes only. We do not guarantee its active functionality. A user-friendly version is currently under development.

Folding-Docking-Affinity (FDA) is a framework which folds proteins, determines protein-ligand binding conformations, and predicts binding affinities from computed three-dimensional protein-ligand binding structures.

Dependencies

The Folding part was tested with Python 3.10.13 and CUDA 12.3 on Ubuntu 20.04, with access to Nvidia Tesla V100 (32GB RAM), Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz, and 1.5TB RAM. Please follow localcolabfold to install the working environment.

The Docking and Affinity parts were tested with Python 3.9.18 and CUDA 11.5 on CentOS Linux 7 (Core), with access to Nvidia A100 (80GB RAM), AMD EPYC 7352 24-Core Processor, and 1TB RAM. Run the following to create a conda environment, FDA.

conda create --name FDA python=3.9
conda activate FDA
conda install conda-forge::pymol-open-source
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
pip install scipy
pip install --no-index pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.11.0+cu113.html
pip install torch_geometric
python -m pip install PyYAML scipy "networkx[default]" biopython rdkit-pypi e3nn spyrmsd pandas biopandas

Datasets

Create a directory /data, and download the processed data for replicating benchmark and ablation study results from zenodo and decompress the files

git clone git@github.com:ZhiGroup/FDA.git
cd FDA
mkdir data
cd data
wget https://zenodo.org/records/10968593/files/benchmark.tar.gz?download=1
wget https://zenodo.org/records/10968593/files/ablation_study.tar.gz?download=1
tar -xvzf benchmark.tar.gz?download=1
tar -xvzf ablation_study.tar.gz?download=1
cd ../

File structure

|--data
   |--ablation_study
      |--crystal_crystal
      |--crystal_diffdock
      |--colabfold_diffdock
      |--train.csv
      |--valid.csv
      |--test.csv
   |--benchmark
      |--complex
      |--davis_colabfold_protein
      |--davis_ligand
      |--davis_data.tsv

Replicate results

Affinity prediction benchmark

Folding

Use ColabFold to generate three-dimensional protein structures. Please follow localcolabfold to install the working environment. Or directly download the processed data from zenodo and place them in /data directory and jump to the last step.

python folding/create_davis_protein_input.py
colabfold_batch --templates --amber folding/input/davis_protein.csv folding/output/davis_colabfold_protein --use-gpu-relax --num-relax 1 --gpu 0

Docking

Create protein-ligand complex directories

python docking/create_dir.py

Implement DiffDock to generate ligand binding poses. Download ESM2 embedding from zenodo and place the file in docking/DiffDock/data/. The process of generating ESM2 embedding could refer DiffDock

cd docking/DiffDock
python -m affinity.dataset_davis_colabfold --run_name davis_colabfold --inference_steps 20 --samples_per_complex 10 --batch_size 10 --ns 12 --nv 6 --num_conv_layers 3 --dynamic_max_cross --scale_by_sigma --dropout 0.2 --remove_hs --c_alpha_max_neighbors 24 --receptor_radius 15 --gpu_num 6

Add DiffDock-generated ligand poses into original complex directories.

cd ../../
python docking/update_dir.py

Affinity

Pre-process protein-ligand complexes and generate inputs for GIGN.

python affinity/GIGN/preprocessing.py
cd affinity/GIGN
python dataset_GIGN_benchmark.py

Train GIGN to predict binding affinity under different split_methods (drug, protein, both, and seqid).

cd affinity/GIGN
python train_GIGN_benchmark.py --split_method drug --gpu 0

Ablation study

Download the processed data from zenodo and place them in /data. Train GIGN to predict binding affinity under three different scenarios (crystal_crystal, crystal_diffdock, and colabfold_diffdock).

cd affinity/GIGN
python train_GIGN_ablation.py --scenario crystal_crystal --gpu 0

Citation

Wu, MH., Xie, Z., & Zhi, D. Protein-ligand binding affinity prediction: Is 3D binding pose needed?. bioRxiv (2024). https://doi.org/10.1101/2024.04.16.589805