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

摘要

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 G_(i/o), G_(q/11) and G_s, not including G_(12/13)-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 G_(12/13), whereas unlike other methods avoids overpredictions (false positives) when non-GPCR sequences are encountered.