Prediction of protein-binding areas by small-world residue networks and application to docking

Carles Pons,Fabian Glaser,Juan Fernandez-Recio

doi:10.1186/1471-2105-12-378

Carles Pons, Fabian Glaser + Show 1 more

Open Access

https://doi.org/10.1186/1471-2105-12-378

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Abstract

BackgroundProtein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces.ResultsHere we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved.ConclusionsThe network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches.

Highlights

Protein-protein interactions are involved in most cellular processes, and their detailed physicochemical and structural characterization is needed in order to understand their function at the molecular level
Protein-protein interactions are fundamental to many cellular processes [1], and a detailed atomic-level description of protein complexes would be needed in order to fully understand their association mechanism [2]
Interface prediction by network-based parameters We modeled each of the unbound protein structures of the docking benchmark 3.0 [31] as residue-based networks based on Ca atoms

Summary

Introduction

Protein-protein interactions are involved in most cellular processes, and their detailed physicochemical and structural characterization is needed in order to understand their function at the molecular level. Protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces. In small-world networks (i) the average shortest path (between any two nodes) is logarithmically related to the total number of nodes, and (ii) a large average clustering coefficient is observed [18] Using this approach, proteins can be modeled as a network of interactions, where the nodes represent residues and the edges their contacts. Topological data based on small-world network descriptions of proteins have been recently exploited to predict proteinprotein interfaces [19,20], protein-DNA interfaces [21], protein-RNA interfaces [22], ligand binding sites [23,24], modeling [25], protein dynamics [26], protein disorder [27], ribosome functional sites [28], to identify critical residues for protein function [15], or to evaluate protein docking poses [29]

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