Nucleic Acids Research Advance Access originally published online on March 16, 2007
Nucleic Acids Research 2007 35(7):2238-2246; doi:10.1093/nar/gkm107
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Nucleic Acids Research, 2007, Vol. 35, No. 7 2238-2246
© 2007 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 |
Consensus sequences improve PSI-BLAST through mimicking profile–profile alignments
1Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, USA, 2Columbia University Center for Computational Biology and Bioinformatics (C2B2), 1130 St. Nicholas Ave. Rm. 801, New York, NY 10032, USA and 3NorthEast Structural Genomics Consortium (NESG), Columbia University, 1130 St. Nicholas Ave. Rm. 802, New York, NY 10032, USA
*To whom correspondence should be addressed. Tel: +1 212 851 4669; Fax: +1 212 851 5176; Email: dsp23{at}columbia.edu
Received December 20, 2006. Revised February 5, 2007. Accepted February 6, 2007.
Sequence alignments may be the most fundamental computational resource for molecular biology. The best methods that identify sequence relatedness through profile–profile comparisons are much slower and more complex than sequence–sequence and sequence–profile comparisons such as, respectively, BLAST and PSI-BLAST. Families of related genes and gene products (proteins) can be represented by consensus sequences that list the nucleic/amino acid most frequent at each sequence position in that family. Here, we propose a novel approach for consensus-sequence-based comparisons. This approach improved searches and alignments as a standard add-on to PSI-BLAST without any changes of code. Improvements were particularly significant for more difficult tasks such as the identification of distant structural relations between proteins and their corresponding alignments. Despite the fact that the improvements were higher for more divergent relations, they were consistent even at high accuracy/low error rates for non-trivially related proteins. The improvements were very easy to achieve; no parameter used by PSI-BLAST was altered and no single line of code changed. Furthermore, the consensus sequence add-on required relatively little additional CPU time. We discuss how advanced users of PSI-BLAST can immediately benefit from using consensus sequences on their local computers. We have also made the method available through the Internet (http://www.rostlab.org/services/consensus/).