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Bioinformatics Advance Access originally published online on June 25, 2008
Bioinformatics 2008 24(16):1798-1804; doi:10.1093/bioinformatics/btn326
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Intrinsic disorder prediction from the analysis of multiple protein fold recognition models

Liam J. McGuffin

School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK


  Abstract

Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top five methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified.

Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested.

Availability: http://www.reading.ac.uk/bioinf/DISOclust/

Contact: l.j.mcguf.n{at}reading.ac.uk

Supplementary information: Supplementary data are available at http://www.reading.ac.uk/bioinf/DISOclust/suppl.pdf

Associate Editor: Burkhard Rost

Received on March 9, 2008; revised on June 4, 2008; accepted on June 20, 2008

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