CLUB-MARTINI: Selecting Favourable ...
Type de document :
Article dans une revue scientifique
PMID :
URL permanente :
Titre :
CLUB-MARTINI: Selecting Favourable Interactions amongst Available Candidates, a Coarse-Grained Simulation Approach to Scoring Docking Decoys
Auteur(s) :
Hou, Qingzhen [Auteur]
Lensink, Marc [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Heringa, Jaap [Auteur]
Feenstra, K. Anton [Auteur]
Lensink, Marc [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Heringa, Jaap [Auteur]
Feenstra, K. Anton [Auteur]
Titre de la revue :
PLoS One
Nom court de la revue :
PLoS ONE
Pagination :
e0155251
Date de publication :
2016-05-11
ISSN :
1932-6203
Mot(s)-clé(s) en anglais :
Protein Conformation, alpha-Helical
Molecular Dynamics Simulation
Protein Interaction Mapping
Proteins
Thermodynamics
Algorithms
Protein Conformation, beta-Strand
Databases, Protein
Protein Binding
Molecular Docking Simulation
Protein Interaction Domains and Motifs
Kinetics
Binding Sites
Molecular Dynamics Simulation
Protein Interaction Mapping
Proteins
Thermodynamics
Algorithms
Protein Conformation, beta-Strand
Databases, Protein
Protein Binding
Molecular Docking Simulation
Protein Interaction Domains and Motifs
Kinetics
Binding Sites
Discipline(s) HAL :
Chimie/Chimie théorique et/ou physique
Résumé en anglais : [en]
Large-scale identification of native binding orientations is crucial for understanding the role of protein-protein interactions in their biological context. Measuring binding free energy is the method of choice to estimate ...
Lire la suite >Large-scale identification of native binding orientations is crucial for understanding the role of protein-protein interactions in their biological context. Measuring binding free energy is the method of choice to estimate binding strength and reveal the relevance of particular conformations in which proteins interact. In a recent study, we successfully applied coarse-grained molecular dynamics simulations to measure binding free energy for two protein complexes with similar accuracy to full-atomistic simulation, but 500-fold less time consuming. Here, we investigate the efficacy of this approach as a scoring method to identify stable binding conformations from thousands of docking decoys produced by protein docking programs. To test our method, we first applied it to calculate binding free energies of all protein conformations in a CAPRI (Critical Assessment of PRedicted Interactions) benchmark dataset, which included over 19000 protein docking solutions for 15 benchmark targets. Based on the binding free energies, we ranked all docking solutions to select the near-native binding modes under the assumption that the native-solutions have lowest binding free energies. In our top 100 ranked structures, for the 'easy' targets that have many near-native conformations, we obtain a strong enrichment of acceptable or better quality structures; for the 'hard' targets without near-native decoys, our method is still able to retain structures which have native binding contacts. Moreover, in our top 10 selections, CLUB-MARTINI shows a comparable performance when compared with other state-of-the-art docking scoring functions. As a proof of concept, CLUB-MARTINI performs remarkably well for many targets and is able to pinpoint near-native binding modes in the top selections. To the best of our knowledge, this is the first time interaction free energy calculated from MD simulations have been used to rank docking solutions at a large scale.Lire moins >
Lire la suite >Large-scale identification of native binding orientations is crucial for understanding the role of protein-protein interactions in their biological context. Measuring binding free energy is the method of choice to estimate binding strength and reveal the relevance of particular conformations in which proteins interact. In a recent study, we successfully applied coarse-grained molecular dynamics simulations to measure binding free energy for two protein complexes with similar accuracy to full-atomistic simulation, but 500-fold less time consuming. Here, we investigate the efficacy of this approach as a scoring method to identify stable binding conformations from thousands of docking decoys produced by protein docking programs. To test our method, we first applied it to calculate binding free energies of all protein conformations in a CAPRI (Critical Assessment of PRedicted Interactions) benchmark dataset, which included over 19000 protein docking solutions for 15 benchmark targets. Based on the binding free energies, we ranked all docking solutions to select the near-native binding modes under the assumption that the native-solutions have lowest binding free energies. In our top 100 ranked structures, for the 'easy' targets that have many near-native conformations, we obtain a strong enrichment of acceptable or better quality structures; for the 'hard' targets without near-native decoys, our method is still able to retain structures which have native binding contacts. Moreover, in our top 10 selections, CLUB-MARTINI shows a comparable performance when compared with other state-of-the-art docking scoring functions. As a proof of concept, CLUB-MARTINI performs remarkably well for many targets and is able to pinpoint near-native binding modes in the top selections. To the best of our knowledge, this is the first time interaction free energy calculated from MD simulations have been used to rank docking solutions at a large scale.Lire moins >
Langue :
Anglais
Audience :
Non spécifiée
Établissement(s) :
CNRS
Université de Lille
Université de Lille
Équipe(s) de recherche :
Computational Molecular Systems Biology
Date de dépôt :
2020-02-12T15:11:19Z
2021-03-17T10:22:36Z
2021-03-17T10:22:36Z
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