Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Pertinent background knowledge for learning protein grammars
ECML'06 Proceedings of the 17th European conference on Machine Learning
A neural network based approach for GPCR protein prediction using pattern discovery
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
| Hi-index | 3.84 |
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). 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 5-fold cross-validation test. The method predicts also promiscuous coupling preferences, including coupling to G12/13, whereas unlike other methods avoids overpredictions (false positives) when non-GPCR sequences are encountered. Availability: A webserver for academic users is available at http://bioinformatics.biol.uoa.gr/PRED-COUPLE2 Contact: shamodr@cc.uoa.gr Supplementary information: Results for promiscuous receptors can be found at: http://bioinformatics.biol.uoa.gr/PRED-COUPLE2/tables