PUResNetV2.0 !!! New Release !!! (PUResNetV2.0: a deep learning model leveraging sparse representation for improved ligand binding site prediction)
Manuscript Link (https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00865-6)
Github Link (https://github.com/jivankandel/PUResNetV2.0)
Webserver Link (https://nsclbio.jbnu.ac.kr/tools/jmol)
Prediction of protein-ligand binding site is fundamental step in understanding functional characteristics of the protein which plays vital role in carrying out different biological functions and is a crucial stage in drug discovery. A protein shows its true nature after interacting with any capable molecule knows as ligand which binds only in favorable binding site of protein structure. PUResNetV2.0 is now available at (http://nsclbio.jbnu.ac.kr/tools/jmol) although not published yet, its recommended to try this.
scpdb_subset.zip (https://github.com/jivankandel/PUResNet/blob/main/scpdb_subset.zip)
coach.zip (https://github.com/jivankandel/PUResNet/blob/main/coach.zip)
BU48.zip (https://github.com/jivankandel/PUResNet/blob/main/BU48.zip)
- Tensorflow 1.11 (https://www.tensorflow.org/)
- Keras (https://keras.io/)
- Scipy (https://www.scipy.org/)
- Scikit-Image (https://scikit-image.org/)
- Open Babel (http://openbabel.org/wiki/Main_Page)
- Pybel (http://openbabel.org/docs/current/UseTheLibrary/Python_Pybel.html)
- TFBIO (https://gitlab.com/cheminfIBB/tfbio)
- Numpy (https://numpy.org/)
- Python 3.6 (https://www.python.org/)
Note that: It is better to setup new environment using conda or pyenv. You may need to compile open babel and tfbio if installing with PIP doesn't work.
1. Clone this repository
git clone https://github.com/jivankandel/PUResNet.git cd PUResNet2. Setup Environment
#create conda environment conda create -n env_name python=3.6 conda activate env_name conda install -c conda-forge openbabel conda install scikit-image conda install numpy conda install -c anaconda scipy conda install -c conda-forge keras=2.1 conda install -c conda-forge tensorflow=1.11
Or use environment.yml by:
conda env create -f environment.yml
- Download model file Go to this link and click download, and move it into your working directory. Since this was uploaded with git lfs, git clone won't download the full file.
Please compare sha256 checksum to make sure the file is corrected.
sha256sum whole_trained_model1.hdf
Output: 63f3f3321ab6fe58d824518185b821380ee2674369209d025c52489f29c18466
- Prediction
python predict.py -h usage: predict.py [-h] --file_format FILE_FORMAT --mode MODE --input_path INPUT_PATH --model_path MODEL_PATH [--output_format OUTPUT_FORMAT] [--output_path OUTPUT_PATH] [--gpu GPU] optional arguments: -h, --help show this help message and exit --file_format FILE_FORMAT, -ftype FILE_FORMAT File Format of Protein Structure like: mol2,pdb..etc. All file format supported by Open Babel is supported (default: None) --mode MODE, -m MODE Mode 0 is for single protein structure. Mode 1 is for multiple protein structure (default: None) --input_path INPUT_PATH, -i INPUT_PATH For mode 0 provide absolute or relative path for protein structure. For mode 1 provide absolute or relative path for folder containing protein structure (default: None) --model_path MODEL_PATH, -mpath MODEL_PATH Provide models absolute or relative path of model (default: None) --output_format OUTPUT_FORMAT, -otype OUTPUT_FORMAT Provide the output format for predicted binding side. All formats supported by Open Babel (default: mol2) --output_path OUTPUT_PATH, -o OUTPUT_PATH path to model output (default: output) --gpu GPU, -gpu GPU Provide GPU device if you want to use GPU like: 0 or 1 or 2 etc. (default: None)
Example:
python predict.py -ftype pdb -m 0 -i test.pdb -mpath whole_trained_model1.hdf -otype mol2 -o output -gpu 1
MIT License
Copyright (c) 2022 Kandel Jeevan
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER