Improving model construction of profile HMMs for remote homology detection through structural alignment

Background: Remote homology detection is a challenging problem in Bioinformatics. Arguably, profile Hidden Markov Models (pHMMs) are one of the most successful approaches in addressing this important problem. pHMM packages present a relatively small computational cost, and perform particularly well at recognizing remote homologies. This raises the question of whether structural alignments could impact the performance of pHMMs trained from proteins in the Twilight Zone, as structural alignments are often more accurate than sequence alignments at identifying motifs and functional residues. Next, we assess the impact of using structural alignments in pHMM performance. Results: We used the SCOP database to perform our experiments. Structural alignments were obtained using the 3DCOFFEE and MAMMOTH-mult tools; sequence alignments were obtained using CLUSTALW, TCOFFEE, MAFFT and PROBCONS. We performed leave-one-family-out cross-validation over super-families. Performance was evaluated throug...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Juliana S. Bernardes, Alberto M. R. Dávila, Vítor Santos Costa, Gerson Zaverucha
• Keywords: BMCBI 2007, Markov Models, Sequence Alignment, Structural Alignment