Identification of protein hot regions by combing structure-based classification, energy-based clustering and sequence-based conservation in evolution

Abstract

Revealing the protein hot regions is the key point for understanding the protein-protein interaction, while due to the long period and labour-consuming of experimental methods, it is very helpful to use computational method to improve the efficiency to predict hot regions. In previous methods, some methods are based on a single side, such as structure, energy, and sequence, every side has its limitations. In this paper, we proposed a new method that combines structure-based classification, energy-based clustering and sequence-based conservation. This method makes full use of three sides of protein features and minimise the limitations of using one single side. Experimental results show that the proposed method increases the prediction accuracy of protein hot regions.