Identifying protein-protein interaction sites using adapted Bayesian classifier

Identifying protein-protein interaction sites have important connotations ranging from rational drug design to analysis metabolic and signal transduction networks. In this paper, we presented an adapted Bayesian classifier based on tree augmented naive Bayesian classifier to predict interface residues of protein-protein interaction sites. This classifier used fixed structure which could denote the correlation of sequence neighborhood and the character of protein's features. The conditional probability tables of the classifier were learned from the training dataset. By testing our approach on 81 hetero-complex chains, experimental results demonstrate the performance of our approach is indeed superior to current existing methods. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes.