Abstract
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.
Highlights
Protein structures inherently contain an abundance of information, the extraction of which is often performed in order to correlate feature with function
We considered water molecules and a residue–residue distance weight to assess their relevance in the centrality analysis
For each Residue interaction networks (RINs), we ran the 6 centrality measures: 3 that are based on shortest paths (BCA, CCA, and RCA) and 3 that are based on local interactions of nodes in the network (ECA, DCA, and PRA)
Summary
Protein structures inherently contain an abundance of information, the extraction of which is often performed in order to correlate feature with function. At the primary—sequence—level, the approaches typically rely on comparison to annotated sequences. This can lead to reliable predictions of backbone flexibility, secondary structure with phi and psi angles, or even three-dimensional structural modeling (Kryshtafovych et al, 2019). A protein rarely works alone and often engages in macromolecular complex formation in order for it to execute its biological function. These complexes include homodimers at Centrality Analysis Highlights Binding Residues their simplest level but often reach dozens of molecules. In the absence of such information, molecular docking tools have been developed to predict the interaction between protein partners with increasing reliability (Lensink et al, 2019; Lensink et al, 2020)
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