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Bioinformatics Advance Access first published online on June 1, 2007
This version published online on June 14, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm302
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

POODLE-L: A Two-level SVM Prediction System for Reliably Predicting Long Disordered Regions

Shuichi Hirose a,c,*, Kana Shimizu b, Satoru Kanai a, Yutaka Kuroda c and Tamotsu Noguchi b

aPharmaDesign, Inc., Tokyo 104-0032, Japan
bComputational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan
cDepartment of Biotechnology and Life Science, Graduate School of Engineering, Tokyo University of Agriculture and Technology, Koganei 184-8588, Japan

*To whom correspondence should be addressed. Shuichi Hirose, E-mail: poodle{at}cbrc.jp


  Abstract

Motivation: Recent experimental and theoretical studies have revealed several proteins containing sequence segments that are unfolded under physiological conditions. These segments are called disordered regions. They are actively investigated because of their possible involvement in various biological processes such as cell signaling, transcriptional and translational regulation. Additionally, disordered regions can represent a major obstacle to high throughput proteome analysis and often need to be removed from experimental targets. The accurate prediction of long disordered regions is thus expected to provide annotations that are useful for a wide range of applications.

Results: We developed Prediction Of Order and Disorder by machine LEarning (POODLE-L; L stands for long), the Support Vector Machines (SVMs) based method for predicting long disordered regions using ten kinds of simple physico-chemical properties of amino acid. POODLE-L assembles the output of 10 two-level SVM predictors into a final prediction of disordered regions. The performance of POODLE-L for predicting long disordered regions, which exhibited a Matthew’s correlation coefficient of 0.658, was the highest when compared with eight well-established publicly available disordered region predictors.

Availability: POODLE-L is freely available at http://mbs.cbrc.jp/poodle/poodle-l.html.

Associate Editor: Prof. Dmitrij Frishman

Received on January 31, 2007; revised on May 29, 2007; accepted on May 30, 2007

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