PRODIGY-LIG

PRODIGY-LIG predicts binding affinities of protein–small ligand complexes using atomic-level contact analysis of protein–ligand interfaces.


Key Features:

  • Predictive Methodology: Employs atomic contacts and structural properties derived from protein–ligand interfaces to estimate binding affinity.
  • Performance: Demonstrated top performance in blind testing, including the D3R Grand Challenge 2 where it predicted binding affinities for 102 protein–ligand complexes.
  • Residue-to-Atomic Adaptation: Adapts residue-based contact analysis from PRODIGY for protein–protein complexes to atomic-level evaluations tailored to small ligands.

Scientific Applications:

  • Drug discovery: Prioritizes and ranks small-molecule candidates by predicted binding affinity to protein targets.
  • Molecular interaction analysis: Characterizes interaction dynamics between proteins and small molecules using interface-derived structural metrics.
  • Computational benchmarking: Serves in benchmarking and validation of affinity predictors with blind-test datasets such as D3R Grand Challenge 2.

Methodology:

Performs atomic-contact-based analysis of protein–ligand interfaces using structural properties derived from the interface.

Topics

Collections

Details

License:
Apache-2.0
Maturity:
Mature
Cost:
Free of charge
Tool Type:
api, command-line tool, web application
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Python
Added:
8/9/2019
Last Updated:
6/16/2020

Operations

Publications

Vangone A, Schaarschmidt J, Koukos P, Geng C, Citro N, Trellet ME, Xue LC, Bonvin AMJJ. Large-scale prediction of binding affinity in protein–small ligand complexes: the PRODIGY-LIG web server. Bioinformatics. 2018;35(9):1585-1587. doi:10.1093/bioinformatics/bty816. PMID:31051038.

PMID: 31051038
Funding: - European: 675858, H2020 - BioExcel: 675728 - NWO: 718.015.001 - ASDI eScience: 027016G04 - Netherlands Organization for Scientific Research: 722.014.005 - China Scholarship Council: 201406220132 - Marie Skłodowska-Curie Individual: BAP-659025, H2020 MSCA-IF-2015

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