Centrality Measures in Residue Interaction ...
Type de document :
Article dans une revue scientifique: Article de synthèse/Review paper
URL permanente :
Titre :
Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding
Auteur(s) :
Brysbaert, Guillaume [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Lensink, Marc [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Lensink, Marc [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Titre de la revue :
Frontiers in Bioinformatics
Nom court de la revue :
Front. Bioinform.
Numéro :
1
Éditeur :
Frontiers Media SA
Date de publication :
2021-06-18
ISSN :
2673-7647
Mot(s)-clé(s) en anglais :
residue interaction networks
centrality
protein structure networks
structural bioinformatics
protein binding
protein–protein interaction
protein complexes
protein assemblies
centrality
protein structure networks
structural bioinformatics
protein binding
protein–protein interaction
protein complexes
protein assemblies
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
Residue interaction networks (RINs) describe a protein structure as a network of interacting residues. Central nodes in these networks, identified by centrality analyses, highlight those residues that play a role in the ...
Lire la suite >Residue interaction networks (RINs) describe a protein structure as a network of interacting residues. Central nodes in these networks, identified by centrality analyses, highlight those residues that play a role in the structure and function of the protein. However, little is known about the capability of such analyses to identify residues involved in the formation of macromolecular complexes. Here, we performed six different centrality measures on the RINs generated from the complexes of the SKEMPI 2 database of changes in protein–protein binding upon mutation in order to evaluate the capability of each of these measures to identify major binding residues. The analyses were performed with and without the crystallographic water molecules, in addition to the protein residues. We also investigated the use of a weight factor based on the inter-residue distances to improve the detection of these residues. We show that for the identification of major binding residues, closeness, degree, and PageRank result in good precision, whereas betweenness, eigenvector, and residue centrality analyses give a higher sensitivity. Including water in the analysis improves the sensitivity of all measures without losing precision. Applying weights only slightly raises the sensitivity of eigenvector centrality analysis. We finally show that a combination of multiple centrality analyses is the optimal approach to identify residues that play a role in protein–protein interaction.Lire moins >
Lire la suite >Residue interaction networks (RINs) describe a protein structure as a network of interacting residues. Central nodes in these networks, identified by centrality analyses, highlight those residues that play a role in the structure and function of the protein. However, little is known about the capability of such analyses to identify residues involved in the formation of macromolecular complexes. Here, we performed six different centrality measures on the RINs generated from the complexes of the SKEMPI 2 database of changes in protein–protein binding upon mutation in order to evaluate the capability of each of these measures to identify major binding residues. The analyses were performed with and without the crystallographic water molecules, in addition to the protein residues. We also investigated the use of a weight factor based on the inter-residue distances to improve the detection of these residues. We show that for the identification of major binding residues, closeness, degree, and PageRank result in good precision, whereas betweenness, eigenvector, and residue centrality analyses give a higher sensitivity. Including water in the analysis improves the sensitivity of all measures without losing precision. Applying weights only slightly raises the sensitivity of eigenvector centrality analysis. We finally show that a combination of multiple centrality analyses is the optimal approach to identify residues that play a role in protein–protein interaction.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
Université de Lille
CNRS
CNRS
Équipe(s) de recherche :
Computational Molecular Systems Biology
Date de dépôt :
2021-10-08T09:31:33Z
2021-10-11T12:26:40Z
2021-10-11T12:26:40Z
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