Premium
Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape
Author(s) -
Zoete Vincent,
Schuepbach Thierry,
Bovigny Christophe,
Chaskar Prasad,
Daina Antoine,
Röhrig Ute F.,
Michielin Olivier
Publication year - 2016
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.24249
Subject(s) - docking (animal) , energy landscape , solvation , force field (fiction) , computer science , conformational isomerism , protein ligand , chemistry , molecular dynamics , protein–ligand docking , computational chemistry , computational science , artificial intelligence , virtual screening , molecule , medicine , biochemistry , nursing , organic chemistry
Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure‐based computer‐aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand–protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state‐of‐the‐art docking programs. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.