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Improving accuracy and efficiency of blind protein‐ligand docking by focusing on predicted binding sites
Author(s) -
Ghersi Dario,
Sanchez Roberto
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22154
Subject(s) - docking (animal) , protein–ligand docking , binding site , virtual screening , searching the conformational space for docking , ligand (biochemistry) , computational biology , chemistry , plasma protein binding , computer science , molecular dynamics , biology , computational chemistry , biochemistry , receptor , medicine , nursing
The use of predicted binding sites (binding sites calculated from the protein structure alone) is evaluated here as a tool to focus the docking of small molecule ligands into protein structures, simulating cases where the real binding sites are unknown. The resulting approach consists of a few independent docking runs carried out on small boxes, centered on the predicted binding sites, as opposed to one larger blind docking run that covers the complete protein structure. The focused and blind approaches were compared using a set of 77 known protein‐ligand complexes and 19 ligand‐free structures. The focused approach is shown to: (1) identify the correct binding site more frequently than blind docking; (2) produce more accurate docking poses for the ligand; (3) require less computational time. Additionally, the results show that very few real binding sites are missed in spite of focusing on only three predicted binding sites per target protein. Overall the results indicate that, by improving the sampling in regions that are likely to correspond to binding sites, the focused docking approach increases accuracy and efficiency of protein ligand docking for those cases where the ligand‐binding site is unknown. This is especially relevant in applications such as reverse virtual screening and structure‐based functional annotation of proteins. Proteins 2009. © 2008 Wiley‐Liss, Inc.