Journal Article

Augmented training of hidden Markov models to recognize remote homologs via simulated evolution

Anoop Kumar and Lenore Cowen

in Bioinformatics

Volume 25, issue 13, pages 1602-1608
Published in print July 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp265

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Motivation: While profile hidden Markov models (HMMs) are successful and powerful methods to recognize homologous proteins, they can break down when homology becomes too distant due to lack of sufficient training data. We show that we can improve the performance of HMMs in this domain by using a simple simulated model of evolution to create an augmented training set.

Results: We show, in two different remote protein homolog tasks, that HMMs whose training is augmented with simulated evolution outperform HMMs trained only on real data. We find that a mutation rate between 15 and 20% performs best for recognizing G-protein coupled receptor proteins in different classes, and for recognizing SCOP super-family proteins from different families.

Contacts: anoop.kumar@tufts.edu;lenore.cowen@tufts.edu

Journal Article.  5324 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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