Prediction of hot regions in protein-protein interaction based on the Gi statistics and cascade classifier

There is an important relationship between the stability of protein complex and hot region. Research has shown that in protein-protein interaction (PPI), residues are denser around the hot region. Therefore, this paper proposed an algorithm based on Gi statistics, regional division rule and regional amplification principle to form residue dense region (RDR); Then, according to the results of cascade classifier composed of Naive Bayes and Back-Propagation (BP) neural network classifier, non-hotspot residues in RDRs were removed; At length, we used binding free energy change value calculated from Robetta Server to modify predicted hot regions. The experimental results showd that the proposed method can effectively improve the prediction accuracy on hot regions.