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Bioinformatics Advance Access published online on March 15, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti384
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received December 28, 2004
Revised March 4, 2005
Accepted March 8, 2005

Article

Fold recognition by combining profile-profile alignment and support vector machine

Sangjo Han 1, Byung-chul Lee 1, Seung Taek Yu 1, Chan-seok Jeong 1, Soyoung Lee 1, and Dongsup Kim 1*

1 Department of Biosystems, Korea Advanced Institute of Science and Technology Daejeon, 305-701, Korea

* To whom correspondence should be addressed.
Dongsup Kim, E-mail: kds{at}kaist.ac.kr


  Abstract

Motivation: Currently, the most accurate fold recognition method is to perform profile-profile alignments and estimate the statistical significances of those alignments by calculating z-score or E-value. Although this scheme is reliable in recognizing relatively close homologs related at the family level, it has difficulty in finding the remote homologs that are related at the superfamily or fold level.

Results: Here, we present an alternative way to estimate the significance of the alignments. The alignment between a query protein and a template of length n in the fold library is transformed into a feature vector of length n+1, which is then evaluated by support vector machine (SVM). The output from SVM is converted to a posterior probability that a query sequence is related to a template given SVM output. Results show that a new method shows significantly better performance than PSI-BLAST and profile-profile alignment with z-score scheme. While PSI-BLAST and z-score scheme detect 16% and 20% of superfamily-related proteins, respectively, at 90% specificity, a new method detects 46% of these proteins, resulting in more than two fold increase in sensitivity. More significantly, at the fold level, a new method can detect 14% of remotely related proteins at 90% specificity, remarkable result considering the fact that the other methods can detect almost none at the same level of specificity.


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