aspirincode / onionnet

A multiple-layer inter-molecular contact features based deep neural network for protein-ligand binding affinity prediction

OnionNet

A multiple-layer inter-molecular contact based deep neural network for protein-ligand binding affinity prediction. The testing set is CASF-2013 benchmark and PDBbind v2016 coreset. The protein-ligand binding affinity is directly predicted.

The model could be applied for re-scoring the docking results.

Contact

Yuguang Mu, Nanyang Technological University, ygmu_AT_ntu.edu.sg

Liangzhen Zheng, Nanyang Technological University, lzheng002_AT_e.ntu.edu.sg

Citation

Zheng L, Fan J, Mu Y. OnionNet: a multiple-layer inter-molecular contact based convolutional neural network for protein-ligand binding affinity prediction[J]. arXiv preprint arXiv:1906.02418, 2019.

Installation

Necessary packages should be installed to run the OnionNet model.

Dependecies:

python >= 3.6
numpy  
scipy  
pandas 
scikit-learn
mdtraj 
tensorflow

To install necessary environment, create a new env with conda commands

# download the package and then enter the folder
git clone https://github.com/zhenglz/onionnet.git
cd onionnet

# create a new pearsonal conda environment
conda create -n onionnet python=3.6
conda activate onionnet

# install necessary packages
conda install -c anaconda scipy numpy pandas
conda install tensorflow
conda install -c omnia mdtraj

# do some tests now
python generate_features.py -h
python predict_pKa.py -h

Usage

1. Prepare the protein-ligand complexes (3D structures) in pdb format

a. The protein-ligand complexes from experimental crystal or NMR structures, or from molecular
   docking, are accepted.
b. Make sure that the residue name of the ligands is the same, preferable "LIG" or "UNK".
c. Generate an file containing the complexes, one complex per line. Each line contains the 
   path of the protein-ligand complex file.

2. Generate multiple-layer inter-molecular contact features

Using the "generate_features.py" script to generate the features for OnionNet predictions.

python generate_features.py -h
python generate_features.py -inp input_complexes.dat -out output_features.csv

The input file contains the absolute or the path of the protein-ligand complexes pdb files. The content of the "input_complexes.dat" file could be:

./10gs/10gs_complex.pdb
./1a28/1a28_complex.pdb

Or:

/home/liangzhen/PDBBind_v2018/10gs/10gs_dockingpose.pdb
/home/liangzhen/PDBBind_v2018/1a28/1a28_dockingpose.pdb

Note: make sure you only cat one receptor with one docking pose into a complex file.

3. Predict the pKa of the complexes

Given a dataset containing the multiple-layer inter-molecular contact features, we could predict the binding affinities (in pKa scale).

python predict_pKa.py -h
python predict_pKa.py -model OnionNet_HFree.model -scaler StandardScaler.model -fn features.csv -out output_predicted_pKa.csv