BAPA is a convolutional neural network model for predicting binding affinity of protein-ligand complexes. The network was trained with PDBbind databased and tested with CASF and CSAR "scoring power" benchmark datasets. The related paper is submitted for BMC Bioinformatics.
python 3.6.8
Open Babel 2.4.1
tensorflow 1.12.0
numpy 1.16.4
scikit-learn 0.22
UCSF Chimera
Water of protein structure should be removed and the format of the protein and ligand structure should be converted to PDBQT using open babel. The format of protein structure is PDB and the format of ligand structure is mol2.
Please input the file containing the path of the structure file of the protein and ligand to be converted.
python convert_to_PDBQT.py -i input_list_PDBQT.txt -o ./data/complexes
For more details,
python convert_to_PDBQT.py -h
The protein structure of PDB format should be converted to mol2 format using UCSF Chimera. If you already have the mol2 file of protein, you can skip this step, but we recommend using the file converted with UCSF Chimera.
To convert a PDB file to mol2 file, use convert_to_mol2.ipynb notbook.
If you wnat to change the Dir, change the path in convert_to_mol2.ipynb notebook as follows:
path = "./data/complexes"
Five intermolecular Vina terms and one flexible Vina term should be calculated using the protein and ligand structure in the PDBQT format. The Vina terms used in the proposed method were calculated using a git repository that implements rf-score v3. The network predicts the binding affinity using six Vina terms and the number of occurrences of each descriptor.
You can download the following git repository to your BAPA Dir.
~/BAPA$ git clone https://github.com/HongjianLi/RF-Score.git
Please input the file containing the path of the structure file for protein and ligand, as below.
python get_Vina_terms.py -i input_list_Vina_terms.txt -o ./data/dataset/Vina_terms.pkl
For more details,
python get_Vina_terms.py -h
Please calculate the number of occurrences of each descriptor within the given complex using the mol2 structure of the protein and ligand. Distance threshold and the number of descriptors is fixed at 12Å, 2,500, respectively. The Result of get_descriptors_occurrence_count.py script is saved in binary file format.
Please input the file containing the path of the structure file of the protein and ligand.
python get_descriptors_occurrence_count.py -i input_list_count.txt -o ./data/dataset/dataset.pkl
For more details,
python get_descriptors_occurrence_count.py -h
When Vina terms and the number of occurence of descriptors are ready, you can predcit the binding affinity using the BAPA.
python predict.py -d ./data/dataset/dataset.pkl -v ./data/dataset/Vina_terms.pkl -o ./result/result.csv
The result of predict.py consists of the following forms:
name,prediction
protein1/ligand1,binding affinity
protein2/ligand2,binding affinity
protein3/ligand3,binding affinity
For more details,
python predict.py -h
The label information is needed for training the model. You can generate dataset.pkl file as follows.
python get_descriptors_occurrence_count.py -i input_list_count_train.txt -o ./data/dataset/dataset.pkl
The input_list_count_train.txt file should contain the label information in the last tab.
When all the files are prepared, you can train the BAPA model as follows:
python training.py -d ./data/dataset/dataset.pkl -v ./data/dataset/Vina_terms.pkl -s ./module/trained_network
For more details,
python training.py -h