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Bioinformatics Advance Access published online on September 20, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti679
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 16, 2005
Revised September 12, 2005
Accepted September 12, 2005

Article

Prediction of the coupling specificity of GPCRs to four families of G-proteins using hidden Markov models and artificial neural networks

Nikolaos G. Sgourakis 1, Pantelis G. Bagos 1, and Stavros J. Hamodrakas 1*

1 Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece

* To whom correspondence should be addressed.
Stavros J. Hamodrakas, E-mail: shamodr{at}cc.uoa.gr


  Abstract

Motivation: G-protein coupled receptors are a major class of eukaryotic cell-surface receptors. A very important aspect of their function is the specific interaction (coupling) with members of four G-protein families. A single GPCR may interact with members of more than one G-protein families (promiscuous coupling). Up to date all published methods that predict the coupling specificity of GPCRs are restricted to three main coupling groups Gi/o, Gq/11 and Gs, not including G12/13 - coupled or other promiscuous receptors.

Results: We present a method that combines Hidden Markov Models and a feed-forward Artificial Neural Network to overcome these limitations, while producing the most accurate predictions currently available. Using an up to date curated dataset, our method yields a 94% correct classification rate in a five-fold cross validation test. The method predicts also promiscuous coupling preferences, including coupling to G12/13, whereas unlike other methods avoids over-predictions (false-positives) when non-GPCR sequences are encountered.

Availability: A web-server for academic users is available at: http://bioinformatics.biol.uoa.gr/PRED-COUPLE2.

Supplementary information: Results for promiscuous receptors can be found at: http://bioinformatics.biol.uoa.gr/PRED-COUPLE2/tables.


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