Structural neighboring property for identifying protein-protein binding sites

Background: The protein-protein interaction plays a key role in the control of many biological functions, such as drug design and functional analysis. Determination of binding sites is widely applied in molecular biology research. Therefore, many efficient methods have been developed for identifying binding sites. In this paper, we calculate structural neighboring property through Voronoi diagram. Using 6,438 complexes, we study local biases of structural neighboring property on interface. Results: We propose a novel statistical method to extract interacting residues, and interacting patches can be clustered as predicted interface residues. In addition, structural neighboring property can be adopted to construct a new energy function, for evaluating docking solutions. It includes new statistical property as well as existing energy items. Comparing to existing methods, our approach improves overall F _nat value by at least 3%. On Benchmark v4.0, our method has average I _rmsd value of 3.31Å and overall F _nat value of 63%, which improves upon I _rmsd of 3.89 Å and F _nat of 49% for ZRANK, and I _rmsd of 3.99Å and F _nat of 46% for ClusPro. On the CAPRI targets, our method has average I _rmsd value of 3.46 Å and overall F _nat value of 45%, which improves upon I _rmsd of 4.18 Å and F _nat of 40% for ZRANK, and I _rmsd of 5.12 Å and F _nat of 32% for ClusPro. Conclusions: Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein-protein binding sites, with the prediction quality improved in terms of CAPRI evaluation criteria.