Improving interacting residue prediction using long-distance information in hidden Markov models

Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with early trace back mechanism built into interaction profile hidden Markov models (ipHMMs) that can incorporate the long-distance correlations between interacting residues to improve prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method´s effectiveness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, and it was demonstrated that the prediction consistently improves as the correlations increase.