POODLE-L: A two-level SVM prediction system for reliably predicting long disordered regions

Shuichi Hirose, Kana Shimizu, Satoru Kanai, Yutaka Kuroda, Tamotsu Noguchi

Research output: Contribution to journalArticle

112 Citations (Scopus)

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 10 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.

Original languageEnglish
Pages (from-to)2046-2053
Number of pages8
JournalBioinformatics
Volume23
Issue number16
DOIs
Publication statusPublished - 2007 Aug 15
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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