Grosdidier and V. Zoete contributed equally to this work.
EADock: Docking of small molecules into protein active sites with a multiobjective evolutionary optimization
Article first published online: 22 MAR 2007
Copyright © 2007 Wiley-Liss, Inc.
Proteins: Structure, Function, and Bioinformatics
Volume 67, Issue 4, pages 1010–1025, June 2007
How to Cite
Grosdidier, A., Zoete, V. and Michielin, O. (2007), EADock: Docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins, 67: 1010–1025. doi: 10.1002/prot.21367
- Issue published online: 1 MAY 2007
- Article first published online: 22 MAR 2007
- Manuscript Accepted: 12 DEC 2006
- Manuscript Revised: 22 NOV 2006
- Manuscript Received: 22 SEP 2006
- Swiss National Science Foundation. Grant Numbers: 3232B0-103172, 3200B0-103173
- Oncosuisse. Grant Number: OCS 01381-08-2003
- National Center of Competence in Research (NCCR)
- Swiss Institute of Bioinformatics
- Cluster versus Cancer Project and its Foundation
- small ligand docking;
- evolutionary algorithms;
- rational drug design;
In recent years, protein–ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein–ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 Å around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 Å root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 Å, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the αVβ3 integrin, starting far away from the binding pocket. Proteins 2007. © 2007 Wiley-Liss, Inc.