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Bioinformatics Advance Access originally published online on February 1, 2008
Bioinformatics 2008 24(6):807-814; doi:10.1093/bioinformatics/btn039
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© 2008 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.

De novo identification of highly diverged protein repeats by probabilistic consistency

A. Biegert 1,2 and J. Söding 1,2,*

1Department for Protein Evolution, Max Planck Institute for Developmental Biology, Spemannstr. 35, 72076 Tübingen and 2Gene Center Munich, University of Munich (LMU), Feodor-Lynen-Str. 25, 81377 Munich, Germany

*To whom correspondence should be addressed.


  Abstract

Motivation: An estimated 25% of all eukaryotic proteins contain repeats, which underlines the importance of duplication for evolving new protein functions. Internal repeats often correspond to structural or functional units in proteins. Methods capable of identifying diverged repeated segments or domains at the sequence level can therefore assist in predicting domain structures, inferring hypotheses about function and mechanism, and investigating the evolution of proteins from smaller fragments.

Results: We present HHrepID, a method for the de novo identification of repeats in protein sequences. It is able to detect the sequence signature of structural repeats in many proteins that have not yet been known to possess internal sequence symmetry, such as outer membrane β-barrels. HHrepID uses HMM–HMM comparison to exploit evolutionary information in the form of multiple sequence alignments of homologs. In contrast to a previous method, the new method (1) generates a multiple alignment of repeats; (2) utilizes the transitive nature of homology through a novel merging procedure with fully probabilistic treatment of alignments; (3) improves alignment quality through an algorithm that maximizes the expected accuracy; (4) is able to identify different kinds of repeats within complex architectures by a probabilistic domain boundary detection method and (5) improves sensitivity through a new approach to assess statistical significance.

Availability: Server: http://toolkit.tuebingen.mpg.de/hhrepid; Executables: ftp://ftp.tuebingen.mpg.de/pub/protevo/HHrepID

Contact: soeding{at}lmb.uni-muenchen.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Limsoon Wong

Received on November 17, 2007; revised on January 2, 2008; accepted on January 24, 2008

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