Recognition of beta-structural motifs using hidden Markov models trained with simulated evolution.

Abstract

One of the most successful methods to date for recognizing protein sequences that are evolutionarily related, has been profile hidden Markov models. However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta-sheets. We thus explore methods for incorporating pairwise dependencies into these models. We consider the remote homology detection problem for beta-structural motifs. In particular, we ask if a statistical model trained on members of only one family in a SCOP beta-structural superfamily, can recognize members of other families in that superfamily. We show that HMMs trained with our pairwise model of simulated evolution achieve nearly a median 5% improvement in AUC for beta-structural motif recognition as compared to ordinary HMMs. All datasets and HMMs are available at: http://bcb.cs.tufts.edu/pairwise/.