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Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding

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 capabi...

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Autores principales: Brysbaert, Guillaume, Lensink, Marc F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581030/
https://www.ncbi.nlm.nih.gov/pubmed/36303777
http://dx.doi.org/10.3389/fbinf.2021.684970
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author Brysbaert, Guillaume
Lensink, Marc F.
author_facet Brysbaert, Guillaume
Lensink, Marc F.
author_sort Brysbaert, Guillaume
collection PubMed
description 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.
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spelling pubmed-95810302022-10-26 Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding Brysbaert, Guillaume Lensink, Marc F. Front Bioinform Bioinformatics 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. Frontiers Media S.A. 2021-06-18 /pmc/articles/PMC9581030/ /pubmed/36303777 http://dx.doi.org/10.3389/fbinf.2021.684970 Text en Copyright © 2021 Brysbaert and Lensink. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioinformatics
Brysbaert, Guillaume
Lensink, Marc F.
Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding
title Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding
title_full Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding
title_fullStr Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding
title_full_unstemmed Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding
title_short Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding
title_sort centrality measures in residue interaction networks to highlight amino acids in protein–protein binding
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581030/
https://www.ncbi.nlm.nih.gov/pubmed/36303777
http://dx.doi.org/10.3389/fbinf.2021.684970
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