jchodera / plipify

PLIPify: Protein-Ligand Interaction Frequencies across Multiple Structures

Powered by: Volkamer lab

👷 Note: This repo is still work-in-progress.

Background

Protein-ligand interactions are an essential part of research in structural bioinformatics and drug discovery. Tools such as the Protein-Ligand Interaction Profiler (PLIP) enable us to get detailed interaction profiles for single structures. However, combining this data for multiple structures of a protein to identify possible interaction hotspots across them, e.g. when bound to different ligands, remains difficult. The aim of plipify is to create and visualize a fingerprint that represents the protein-ligand interaction frequencies over multiple structures of the same protein.

Note that full credits for protein-ligand profile computation go to PLIP [1]. plipify provides a wrapper around PLIP, which allows to digest multiple structures at once, performs the mapping of the individual profiles to fingerprints and reports protein-ligand interaction frequencies.

[1] Salentin, S. et al. PLIP: fully automated protein-ligand interaction profiler. Nucl. Acids Res., 2015, 43 (W1): W443-W447.

Project 01

Exploring SARS-CoV-2 Main Protease Interaction Hotspots Using plipify

This is part of a community effort to rapidly find new hits to target the virus main protease.

The COVID-19 (coronavirus disease 2019) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health emergency and there is still an urgent need for effective anti-COVID drugs (see COVID-19 Science Report: Therapeutics). A promising target is the main protease Mpro of SARS-CoV-2 (first crystal structure, 01-2020, 6LU7). UK’s Diamond Light Source has performed a large crystal-based fragment screen on Mpro, yielding by now over 400 complex structures. A huge crowed sourcing campaign - the COVID moonshot project - was invoked by PostEra and partners which encourage researchers from around the world to use the fragment hits as a starting point and contribute, amongst others, by suggesting potential inhibitors (effective and easy-to-make).

Besides other structures-based attempts (see our repo), we applied plipify to the set of available structures, to generate more insides about the common bindig modes.

For more details, please see the fragalysis.ipynb.

Project 02/03

Coming soon ...

Installation using conda

Prerequisite

Anaconda and Git should be pre-installed. See Anaconda's website and Git's website for download.

How to

1. Clone the github repository:

git clone https://github.com/volkamerlab/plipify.git

2. Change directory:

cd plipify

3. Create the conda environment:

conda env create -f devtools/conda-envs/test_env.yaml

4. Activate the environment:

conda activate plipify

5. Install plipify package:

pip install -e .

Contributors

• Methodology: Jaime Rodríguez-Guerra, Franziska Fritz, Andrea Volkamer
  • plipify nb: Franziska Fritz, Jaime Rodríguez-Guerra
• Projects:
  • 01: One protein against many ligands: Jaime Rodríguez-Guerra, William Glass, Andrea Volkamer
  • WIP: 02: One ligand against several targets: Jaime Rodríguez-Guerra, David Schaller, Andrea Volkamer
  • WIP: 03: Automated interaction statistics for any protein in the PDB: David Schaller, Jaime Rodríguez-Guerra, Andrea Volkamer

Repository structure and important files

|-- LICENSE
|-- README.md
|-- devtools    <- environment file
|-- plipify     <- plipify code
|-- projects
|   |-- 01      <- One protein against many ligands
|   |-- 02      <- One ligand against several targets

Copyright

Copyright (c) 2021, Volkamer Lab

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.2.