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Bioinformatics Advance Access published online on August 1, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn382
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Optimizing amino acid groupings for GPCR Classification

Matthew N. Davies 1,*, Andrew Secker 2, Alex A. Freitas 2, Edward Clark 3, Jon Timmis 3 and Darren R. Flower 1

1Edward Jenner Institute, Compton, Newbury, Berkshire, RG20 7NN, U.K.
2Department of Computing and Centre for BioMedical Informatics, University of Kent, Canterbury, Kent CT2 7NF, U.K.
3Departments of Computer Science and Electronics, University of York, Heslington, York YO10 5DD, U.K.

*To whom correspondence should be addressed. Dr. Matthew N. Davies, E-mail: m.davies{at}mail.cryst.bbk.ac.uk


  Abstract

Motivation: There is much interest in reducing the complexity inherent in the representation of the twenty standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally-applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings.

Results: Within the context of G-protein coupled receptor (GPCR) classification, an optimisation algorithm was developed able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of Artificial Immune Systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

Contact: m.davies{at}mail.cryst.bbk.ac.uk

Associate Editor: Prof. John Quackenbush

Received on February 8, 2008; revised on July 3, 2008; accepted on July 21, 2008

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