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Bioinformatics Advance Access published online on April 20, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn195
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Prediction of disordered regions in proteins based on the meta approach

Takashi Ishida 1,* and Kengo Kinoshita 1,2

1Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
2Structure and Function of Biomolecules, SORST JST, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan

*To whom correspondence should be addressed. Dr. Takashi Ishida, E-mail: t-ishida{at}hgc.jp


  Abstract

Motivation: Intrinsically disordered regions in proteins have no unique stable structures without their partner molecules, thus these regions sometimes prevent high quality structure determination. Furthermore, proteins with disordered regions are often involved in important biological processes, and the disordered regions are considered to play important roles in molecular interactions. Therefore, identifying disordered regions is important to obtain high-resolution structural information and to understand the functional aspects of these proteins.

Results: We developed a new prediction method for disordered regions in proteins based on the meta approach and implemented a web-server for this prediction method named "metaPrDOS". The method predicts the disorder tendency of each residue using support vector machines from the prediction results of the seven independent predictors. Evaluation of the meta approach was performed using the CASP7 prediction targets to avoid an overestimation due to the inclusion of proteins used in the training set of some component predictors. As a result, the meta approach achieved higher prediction accuracy than all methods participating in CASP7.

Availability: http://prdos.hgc.jp/meta/

Contact: t-ishida{at}hgc.jp

Associate Editor: Prof. Anna Tramontano

Received on November 9, 2007; revised on March 25, 2008; accepted on April 16, 2008

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