Sfcnn

A scoring function model based on 3D convolutional neural network for protein-ligand binding affinity prediction.

The model was trained on the PDBbind database version 2019 and tested on CASF-2016, CASF-2013, CSAR and axtex datasets.

The model can be applied for rescoring the docking results.

Contact

Yu Wang, Chongqing Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Email: wangyu@cqupt.edu.cn

Dependencies and Installation

In order to use the model necessary packages should be installed

• anaconda3 (the best choice)

• tensorflow2

conda install tensorflow

• openbabel

conda install -c openbabel openbabel

In order to run the scripts you need more:

• jupyter-nootbook
• pandas
• matplotlib
• scikit-learn
• mayavi

Also, the corresponding dataset is required.

Usage

** Make sure the h5 weights file in your working path and 'predict.py' file has been downloaded correctly.**

Protein and ligand structure files must be provided separately and you can use any appropriate file format supported by openbabel.

This model is very easy to use and the protein-ligand binding affinity could be predicted directly and quickly. According to our summary, it can be used in three ways. There are details of their usage in the 'test' folder.

1. If you have a single protein structure and a single ligand structure, use:

python predict.py -p 1a30_protein.pdb -l 1a30_ligand.mol2
python predict.py -p 1a30_protein.pdb -m pdb -l 1a30_ligand.mol2 -n mol2 -o output1

2. If you have a single protein structure and many ligand structures in a single file just like the results of many docking software:

python predict.py -p 1a30_protein.pdb -m pdb -l 1a30_decoys.mol2 -n mol2 -o output2

3. If you have more complex tasks such as analysis of the results of large-scale virtual screening and predicting the affinity of various proteins and ligands on big data set, you best use the model as follows:

import predict #step 1
from openbabel import pybel

model = predict.build_model() #step 2
protein = next(pybel.readfile('pdb','1a30_protein.pdb'))
ligand = next(pybel.readfile('mol2','1a30_ligand.mol2'))
result = predict.predict(protein, ligand, model) #step 3

Combining the model (predict.py ) and python scripts can handle a wide variety of prediction tasks. And it only takes three steps to complete this.

Here is an example of predicting protein-ligand affinities on CASF-2016 dataset:

import predict   #step 1
from openbabel import pybel

model = predict.build_model()   # step 2
core_dirs = glob(os.path.join('CASF-2016','CASF-2016','coreset','*'))
core_id = [os.path.split(i)[-1] for i in core_dirs]
f = open('output.csv','w')
f.write('#code\tscore\n')
for pdbid in core_id:
    proteinfile = os.path.join('CASF-2016','CASF-2016','coreset',pdbid, pdbid+ '_protein.pdb')
    ligandfile = os.path.join('CASF-2016','CASF-2016','coreset',pdbid, pdbid+'_ligand.mol2')
    protein = next(pybel.readfile('pdb',proteinfile))
    ligand = next(pybel.readfile('mol2',ligandfile))
    result = predict.predict(protein, ligand, model)  # step 3, can be combined with loop statements to predict multiple times
    f.write(pdbid+'\t%.4f\n' % result)
f.close()