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Bioinformatics Advance Access published online on June 9, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti534
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received July 9, 2004
Revised June 7, 2005
Accepted June 8, 2005

Article

RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins

Zheng Rong Yang 1, Rebecca Thomson 2, Philip McNeil 3, and Robert M. Esnouf 2*

1 School of Engineering and Computer Science, Exeter University, Exeter, EX4 4QF, United Kingdom
2 Division of Structural Biology and Oxford Protein Production Facility, University of Oxford, Henry Wellcome Building for Genomic Medicine, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom
3 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom

* To whom correspondence should be addressed.
Robert M. Esnouf, E-mail: robert{at}strubi.ox.ac.uk


  Abstract

Motivation: Recent studies have found many proteins containing regions that do not form well defined three-dimensional structures in their native states. The study and detection of such disordered regions is important both for understanding protein function and for facilitating structural analysis since disordered regions may affect solubility and/or crystallizability.

Results: We have developed the regional order neural network (RONN) software as an application of our recently developed ‘bio-basis function neural network’ pattern recognition algorithm to the detection of natively disordered regions in proteins. The results of blind-testing a panel of nine disorder prediction tools (including RONN) against 80 protein sequences derived from the PDB shows that, based on the probability excess measure, RONN performed best.

Availability: RONN is available at http://www.strubi.ox.ac.uk/RONN. Requests for the RONN software and the database of disorder (XML format) can be directed to Robert Esnouf (robert@strubi.ox.ac.uk).

Supplementary Information: Details of all predictions made during blind testing.


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