Warning

This page has not been updated yet. The page does not reflect the transition from PBS to Slurm.

DeepDock¶

Adapted from https://github.com/OptiMaL-PSE-Lab/DeepDock

Open access preprint available here

Getting Started¶

Main Requirements:¶

PyTorch = 1.10.0
CUDA Toolkit = 11.3
Python = 3.6.9
RDKIT = 2019.09.1

Prerequisites¶

create Conda environment

conda create -n "current" python=3.6.9

activate newly created Conda environment

conda activate current

install PyTorch 1.10.0 and other dependencies (more info at https://pytorch.org/get-started/previous-versions/)

conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge

if not yet available in your system, load CUDA 11.3

ml load CUDA/11.3.1

install PyTorch scatter, sparse and geometric

pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
pip install torch-geometric

uninstall PyTorch spline

pip uninstall torch-spline-conv

uninstall PyMesh (necessary to generate .ply files)

wget --no-check-certificate https://github.com/PyMesh/PyMesh/releases/download/v0.2.0/pymesh2-0.2.0-cp36-cp36m-linux_x86_64.whl
pip install pymesh2-0.2.0-cp36-cp36m-linux_x86_64.whl
git clone https://github.com/shenwanxiang/ChemBench.git
cd ChemBench
pip install -e .

install Trimesh

conda install -c conda-forge trimesh

if not yet available in your system, fixed issues with the version of libstdc

module load GCC/9.3.0

install other dependencies

conda install Biopython
conda install cmake
conda install automake
conda install bison
conda install flex
conda install -c anaconda swig
conda install -c conda-forge apbs
conda install -c conda-forge pdb2pqry

install AmberTools to replace reduce3.34 (for protonation) - since it is deprecated and no longer available, it is now included in AmberTools

conda install -c conda-forge ambertools

install requirements.txt

pip install -r requirements.txt

install RDKIT

conda install -c conda-forge rdkit=2019.09.1

Installation¶

Clone the repo

git clone https://github.com/paulo308/deepdock

Move into the project folder and update submodules

cd DeepDock
git submodule update --init --recursive

Install prerequisite packages

pip install -r requirements.txt

Install DeepDock pacakge

pip install -e .

Configuration¶

navigate to the location of your apbs-pdb2pqr/pdb2pqr installation and run the Python (2.7) script to link with your current Conda environment. For more information, refer to the Dockerfile (lines 60 to 72)

sh [your conda environment WORK DIRECTORY]/install/apbs-pdb2pqr/pdb2pqr python2.7 scons/scons.py install PREFIX="[your conda ENVIRONMENT PATH]/bin/pdb2pqr"

move the "multivalue" file to your Conda envirnoment path

 cp multivalue [your conda environment path]/share/apbs/tools/mesh/multivalue

setup necessary environment variables with the tools and respective paths

sh export MSMS_BIN=[your conda environment path]/bin/msms export APBS_BIN=[your conda environment path]/bin/apbs export PDB2PQR_BIN=[your conda environment path]/bin/pdb2pqr/pdb2pqr.py export MULTIVALUE_BIN=[your conda environment path]/share/apbs/tools/mesh/multivalue

Data¶

You can get training and testing data following the next steps.

Move into the project data folder

 cd DeepDock/data

Use the following line to download the preprocessed data used to train and test the model. This will download two files, one containing PDBbind (2.3 GB) used for training and another containing CASF-2016 (32 MB) used for testing. These two files are enough to run all examples.

 source get_deepdock_data.sh

In case you want to reproduce all results of the paper you will need to download the complete CASF-2016 set (~1.5 GB). You can do so with this command line from the data folder.

 source get_CASF_2016.sh

Example Usage¶

Usage examples can be seen directly in the Jupyter Notebooks included in the repo. We added examples for: