Abstract
BACKGROUND: Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. RESULTS: In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average I(rmsd) value of 3.28Å and overall F(nat) value of 63%, which improves upon I(rmsd) of 3.89Å and F(nat) of 49% for ZRANK, and I(rmsd) of 3.99Å and F(nat) of 46% for ClusPro. On CAPRI targets, our method achieves average I(rmsd) value of 3.45Å and overall F(nat) value of 46%, which improves upon I(rmsd) of 4.18Å and F(nat) of 40% for ZRANK, and I(rmsd) of 5.12Å and F(nat) of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. CONCLUSION: Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.