Nucleic Acids Research Advance Access published online on August 26, 2006
Nucleic Acids Research, doi:10.1093/nar/gkl514
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© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
MUMMALS: multiple sequence alignment improved by using hidden Markov models with local structural information
1 Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA 2 Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA
*To whom correspondence should be addressed. Tel: +1 214 645 5951; Fax: +1 214 645 5948; Email: jpei{at}chop.swmed.edu
Received May 10, 2006. Revised June 26, 2006. Accepted July 5, 2006.
We have developed MUMMALS, a program to construct multiple protein sequence alignment using probabilistic consistency. MUMMALS improves alignment quality by using pairwise alignment hidden Markov models (HMMs) with multiple match states that describe local structural information without exploiting explicit structure predictions. Parameters for such models have been estimated from a large library of structure-based alignments. We show that (i) on remote homologs, MUMMALS achieves statistically best accuracy among several leading aligners, such as ProbCons, MAFFT and MUSCLE, albeit the average improvement is small, in the order of several percent; (ii) a large collection (>10 000) of automatically computed pairwise structure alignments of divergent protein domains is superior to smaller but carefully curated datasets for estimation of alignment parameters and performance tests; (iii) reference-independent evaluation of alignment quality using sequence alignment-dependent structure superpositions correlates well with reference-dependent evaluation that compares sequence-based alignments to structure-based reference alignments.
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