Protein conformational switch discerned via network centrality properties

Network analysis has emerged as a powerful tool for examining structural biology systems. The spatial organization of the components of a biomolecular structure has been rendered as a graph representation and analyses have been performed to deduce the biophysical and mechanistic properties of these components. For proteins, the analysis of protein structure networks (PSNs), especially via network centrality measurements and cluster coefficients, has led to identifying amino acid residues that play key functional roles and classifying amino acid residues in general. Whether these network properties examined in various studies are sensitive to subtle (yet biologically significant) conformational changes remained to be addressed. Here, we focused on four types of network centrality properties (betweenness, closeness, degree, and eigenvector centralities) for conformational changes upon ligand binding of a sensor protein (constitutive androstane receptor) and an allosteric enzyme (ribonucleotide reductase). We found that eigenvector centrality is sensitive and can distinguish salient structural features between protein conformational states while other centrality measures, especially closeness centrality, are less sensitive and rather generic with respect to the structural specificity. We also demonstrated that an ensemble informed, modified PSN with static edges removed (which we term PSN*) has enhanced sensitivity at discerning structural changes. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

Automated summary

Network analysis is a powerful tool for studying structural biology systems, with network centrality measurements and cluster coefficients helping identify key functional residues in proteins. Eigenvector centrality is sensitive in distinguishing structural features, while closeness centrality is less sensitive and more generic with respect to structural specificity. Modifications to protein structure networks (PSNs) can enhance sensitivity to discern structural changes.