蛋白质经常与其他蛋白质发生相互作用(PPI)以执行相关的功能,是蛋白质组学中亟待解决的关键问题。本项目提出PPI位点/Hotspots的形成与氨基酸的进化环境密切相关的假设，从蛋白质的一级序列出发，研究PPI位点/Hotspots在疏水力等物理化学环境上的进化关系，以探索PPI的规律与本质。首先，研究基于疏水作用力等理化特征的氨基酸残基的进化谱，并由此对蛋白质的氨基酸残基进行相关协同进化编码；然后，构建相互作用位点的motif进化库，在此motif进化库中BLAST搜索发现目标蛋白质可能的作用位点以及Hotspots；同时开发多核空间多输入均衡数据集的集成学习方法来预测PPI位点及Hotspots；最后融合两种预测结果，以达到更好的预测效果。本项目的成果可以为了解蛋白质生理功能的实现机制和治疗相关疾病的药物靶点设计提供理论上的依据。

Protein always interacts with other proteins (Protein-Protein Interaction, PPI) to help perform functions, which is almost a vital issue to be solved urgently in proteomics. The solution of the issue can be used to understand the essence of life acitivities and further push the development of whole bioinformatics. The proposal presents a hypotheses that there might have high relationship between evolutionary context of amino acid residues and the formation of PPI sites/Hotspots. The proposal is based on amino acid sequence only, investigates the evolutionary context of PPI sites with respect to hydrophobicity and other physicochemical properties of residues, and further discovers the essence of PPI. First, study the co-evolutionary profile of amino acid residues based on hydrophobicity and other physicochemical characters, and further encode amino acids in protein chain by the use of correlation methods. Second, construct the motif library for PPI sites/Hot spots and search for homology PPI sites/Hotspots of target protein in the motif library by running BLAST program. Further, develop ensemble method of multi-kernels with balanced multi-input datasets for predicting PPI sites/Hotspots. Finally, obtain better predictions by combining the two prediction results. As a result, the proposal can used to understanding protein functions and hence providing rationales for drug target design for curing related diseases.