Skip to content

Latest commit

 

History

History
66 lines (58 loc) · 1.5 KB

README.md

File metadata and controls

66 lines (58 loc) · 1.5 KB
Raw

DeepSurf

A surface-based deep learning approach for the prediction of ligand binding sites on proteins (https://doi.org/10.1093/bioinformatics/btab009)

Setup

Experiments were conducted on an Ubuntu 18.04 machine with Python 3.6.9 and CUDA 10.0

  1. Install dependencies
sudo apt-get update && apt-get install python3-venv, p7zip, swig, libopenbabel-dev, g++
  1. Clone this repository
git clone https://github.com/stemylonas/DeepSurf
cd DeepSurf
  1. Create environment and install python dependencies
python3 -m venv venv --prompt DeepSurf
source venv/bin/activate
pip install -r requirements.txt
  1. Compile custom LDS module
cd lds
chmod a+x compile.sh
./compile.sh
cd ..
  1. Download pretrained models
pip install gdown
gdown 1nIBoD3_5nuMqgRGx4G1OHZwLsiUjb7JG
p7zip -d models.7z
  1. Collect and install DMS
wget www.cgl.ucsf.edu/Overview/ftp/dms.zip
unzip dms.zip
rm dms.zip
cd dms
sudo make install
cd ..

Or easily setup by using install.sh:

chmod +x install.sh
./install.sh

Usage example

python predict.py -p protein.pdb -mp model_path -o output_path

Ex:

python predict.py -p protein.pdb -mp models/ -o output

For more input options, check 'predict.py'. All other molecules (waters, ions, ligands) should be removed from the structure. If the input protein has not been protonated, add --protonate to the execution command.
The provided models have been trained on a subset of scPDB (training_subset_of_scpdb.proteins)