title: Quasi-consensus-based comparison of profile hidden Markov models for protein sequences.
abstract: A simple approach for the sensitive detection of distant relationships among protein families and for sequence-structure alignment via comparison of hidden Markov models based on their quasi-consensus sequences is presented. Using a previously published benchmark dataset, the approach is demonstrated to give better homology detection and yield alignments with improved accuracy in comparison to an existing state-of-the-art dynamic programming profile-profile comparison method. This method also runs significantly faster and is therefore suitable for a server covering the rapidly increasing structure database. A server based on this method is available at http://liao.cis.udel.edu/website/servers/modmod
authors.affiliation: Delaware Biotechnology Institute, Newark, DE 19715, USA.
authors.forename: Robel Y
authors.initials: RY
authors.lastname: Kahsay
authors.forename: Guoli
authors.initials: G
authors.lastname: Wang
authors.forename: Guang
authors.initials: G
authors.lastname: Gao
authors.forename: Li
authors.initials: L
authors.lastname: Liao
authors.forename: Roland
authors.initials: R
authors.lastname: Dunbrack
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D016384:Consensus Sequence; D020869:Gene Expression Profiling / Q000379:methods*; D008390:Markov Chains; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid