EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization.

Détails

ID Serval
serval:BIB_B988D4CFEBC1
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization.
Périodique
Proteins
Auteur⸱e⸱s
Grosdidier A., Zoete V., Michielin O.
ISSN
1097-0134[electronic]
Statut éditorial
Publié
Date de publication
2007
Volume
67
Numéro
4
Pages
1010-1025
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
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 A 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 A 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 A, 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 alphaVbeta3 integrin, starting far away from the binding pocket.
Mots-clé
Algorithms, Binding Sites, Computer Simulation, Crystallography, X-Ray, Evolution, Molecular, Ligands, Models, Molecular, Probability, Protein Binding, Protein Structure, Tertiary, Proteins/chemistry, Proteins/genetics, Static Electricity
Pubmed
Web of science
Création de la notice
28/01/2008 11:22
Dernière modification de la notice
20/08/2019 15:27
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