Predicting Protein-Protein Interaction Based on Fisher Scores Extracted from Domain Profiles

In this work, we propose a machine learning method to identify protein-protein interacting partners based on domain level knowledge that can take into account information about the interaction sites. The general approach is to use the profile hidden Markov models of protein domains and the known interactions between domains to train a support vector machine. Proteins are characterized by the vectors of fisher scores that are obtained from comparing the protein sequences to the hidden Markov model for a given domain. Protein pairs, represented by concatenation of their respective fisher score vectors, are classified as interacting partners and non interacting partners by a trained SVM. By selecting the fisher scores based on a profile hidden Markov model that differentiates the interaction sites from other residues within the domain, we demonstrated that the prediction accuracy was significantly improved, as measured in a series of cross validation experiments.