puresnet 0.2.2

PUResNetV2.0 Prediction of Protein Ligand Binding Sites

PUResNetV2.0

A powerful tool for predicting ligand binding sites in protein structures. Webserver Available at https://nsclbio.jbnu.ac.kr/tools/jmol/

Table of Contents

• Overview
• Usage
  • Setup Conda Environment
    • Creating environment named sparseconv
    • Installing pytorch and cuda drivers
    • Installing MinkowskiEngine
    • Installing other requirements
    • Installing PUResNetV2.0 package
  • Setup Docker
• Getting Started
• Example Usage
• Citation
• License

Overview

PUResNetV2.0 is a state-of-the-art deep learning model designed to predict ligand binding sites in protein structures. Utilizing advanced sparse convolution techniques and the powerful MinkowskiEngine, PUResNetV2.0 offers fast and accurate predictions to aid in computational drug discovery.

Usage

Setup Conda Environment

Creating environment named sparseconv

conda create -n sparseconv python=3.10 -c conda-forge
conda activate sparseconv

Installing pytorch and cuda drivers

conda install openblas-devel -c anaconda
conda install pytorch=1.13.0 torchvision=0.14 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install -c "nvidia/label/cuda-11.7.0" cuda-toolkit

Installing MinkowskiEngine

export CUDA_HOME=$CONDA_PREFIX
pip install -U git+https://github.com/NVIDIA/MinkowskiEngine --no-deps

Installing other requirements

conda install -c conda-forge openbabel
conda install -c anaconda scikit-learn

Installing PUResNetV2.0 package

pip install puresnet==0.1

Setup Docker

JupyterLab with CUDA, PyTorch, and Python 3.10

This Docker image provides a ready-to-use JupyterLab environment with CUDA, PyTorch, and Python 3.10.

Prerequisites

• Docker installed on your system (https://docs.docker.com/get-docker/)
• NVIDIA GPU with compatible CUDA drivers (https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)

How to Run the Docker Image

1. Pull the Docker image from Docker Hub:

docker pull jivankandel/puresnet:latest

2. Run the Docker container, exposing the JupyterLab port (8888) and enabling GPU access:

docker run --gpus all -it --user root -p 8888:8888 -v "$(pwd)":/work --workdir /work jivankandel/puresnet:latest

GOTO folder on your local machine where you want to store your notebooks and data and run above command.

To run Examples

docker run --gpus all -it --user root -p 8888:8888 --workdir /Example jivankandel/puresnet:latest

3. Open your web browser and navigate to http://localhost:8888. JupyterLab should be running without requiring any authentication.

Stopping the Docker Container

To stop the running Docker container, find the container ID using the following command:

docker ps

Take note of the CONTAINER ID corresponding to your running image. Then, stop the container using the following command:

docker stop <container_id>

Replace <container_id> with the appropriate CONTAINER ID from the previous step.

Getting Started

After installing PUResNetV2.0, you can start predicting ligand binding sites for your protein structures. Follow the instructions in the Example Usage section to learn how to use the tool effectively.

Example Usage

Inside Example explore following notebook files:

1. Creating sparse tensor.ipynb
2. Predicting.ipynb
3. Training.ipynb

Citation

1. Kandel, J., Tayara, H. & Chong, K.T. PUResNet: prediction of protein-ligand binding sites using deep residual neural network. J Cheminform 13, 65 (2021). https://doi.org/10.1186/s13321-021-00547-7
2. Jeevan, K., Palistha, S., Tayara, H. et al. PUResNetV2.0: a deep learning model leveraging sparse representation for improved ligand binding site prediction. J Cheminform 16, 66 (2024). https://doi.org/10.1186/s13321-024-00865-6

License

MIT License

Copyright (c) 2023 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 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.