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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.

Identifying protein-protein interface via a novel multi-scale local sequence and structural representation