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Bioinformatics Advance Access published online on August 29, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl453
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received April 12, 2006
Revised August 1, 2006
Accepted August 18, 2006

Article

A new representation for protein secondary structure prediction based on frequent patterns

Fabian Birzele 1 and Stefan Kramer 2 *

1 Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University, Amalienstr. 17, D-80333 München, Germany
2 Technische Universität München, Institut für Informatik, Boltzmannstr. 3, D-85748 Garching b. München, Germany

* To whom correspondence should be addressed.
Stefan Kramer, E-mail: kramer{at}in.tum.de


  Abstract

Motivation: A new representation for protein secondary structure prediction based on frequent amino acid patterns is described and evaluated. We discuss in detail how to identify frequent patterns in a protein sequence database using a level-wise search technique, how to define a set of features from those patterns and how to use those features in the prediction of the secondary structure of a protein sequence using Support Vector Machines (SVMs).

Results: Three different sets of features based on frequent patterns are evaluated in a blind testing setup using 150 targets from the EVA contest and compared to predictions of PSI-PRED, PHD and PROFsec. Even though being trained on only 940 proteins, a simple SVM classifier based on this new representation yields results comparable to PSI-PRED and PROFsec. Finally, we show that the method contributes significant information to consensus predictions.

Availability: The method is available from the authors upon request.

Associate Editor: Martin Bishop
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[Abstract] [Full Text] [PDF]


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