DLSCORE: A deep learning based scoring function for predicting protein-ligand binding affinity.

Purpose: The main purpose of DLSCORE is to accurately predict binding affinities between a protein (target) and a ligand.

DLSCORE is an ensemble of neural networks, trained on the recent release of the refined PDBBind data(v2016) using BINding ANAlyzer (BINANA ) descriptors.

Installation

Virtual environment

• Install MglTools (<a target='_blank' href='http://mgltools.scripps.edu/downloads'>Source</a>). Make sure pythonsh command works in the terminal
• Install the virtualenv: bash setup.sh. The script will also make sure the network files are available
• Activate the environment: source .venv/bin/activate

This virtual environment runs DLScore using the cpu version of tensorflow. To run the GPU version, please use the conda installation described below:

Conda (GPU version)

• Install the conda environment. conda env create -f environment_gpu.yml
• Install MglTools (<a target='_blank' href='http://mgltools.scripps.edu/downloads'>Source</a>). Make sure pythonsh command works in the terminal
• If running on Stampede2 (TACC server), please load the module intel/18.0.0
• Download the network files: ./download_networks.sh

Test the installation by runnning bash test_run.sh

Docker

• Download the docker image: docker pull sirimullalab/dlscore
• Run docker run sirimullalab/dlscore -h to see available options

Prediction

For options, run python dlscore.py -h

The output will be the following:

usage: dlscore.py [-h] --ligand LIGAND --receptor RECEPTOR
                  [--vina_executable VINA_EXECUTABLE]
                  [--num_networks NUM_NETWORKS] [--output OUTPUT_FILE]
                  [--network_type {refined,general}] [--verbose {0,1}]

Main script for running DLSCORE

optional arguments:
  -h, --help            show this help message and exit
  --ligand LIGAND       Ligand file. Supported file types: .pdb, .pdbqt, .mol2
  --receptor RECEPTOR   Receptor file. Supported file types: .pdb, .pdbqt,
                        .mol2
  --vina_executable VINA_EXECUTABLE
                        File path for Vina executable.
  --num_networks NUM_NETWORKS
                        Number of networks to use for prediction. Default:10
  --output OUTPUT_FILE  Name of the output file. Dafault: out.csv
  --network_type {refined,general}
                        DLSCORE has two types of weights trained on PDB-Bind
                        2016 refined set and general set (including refined).
                        Any of these two variants can be used. Possible
                        options are: general and refined. Dafault is set to
                        refined.
  --verbose {0,1}       Verbose mode. False if 0, True if 1. Default is set to
                        False.

DLSCORE will be producing the number of networks specified with in -num_networks, NNScore 2.0 will display 20, and vina 1 (plus 5) . The output thrown by DLSCORE and NNScore 2.0 are pKd values, while Vina gives delta G (kcal/mol)

The same applies for the rest of the proteins and ligands. To see the rest of the protein-ligand complexes for our dataset (300 from PDBbind v.2016 refined-set), please read our research article

Contributing

If you wish to contribute, submit pull requests or read more about our code of conduct, please read CONTRIBUTING.md

Authors

Mahmudulla Hassan, Daniel Castaneda Mogollon, Dr. Olac Fuentes, Dr. Suman Sirimulla.

See also the list of contributors who made this project possible.

Funding

This work is supported by Dr. Suman Sirimulla’s startup fund from UTEP School of Pharmacy.

Acknowledgments

We would like to thank Patrick Walters for sharing his metk code and strengthing our statistical results.