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

Nikolaos G. Sgourakis | Zendy; Pantelis G. Bagos | Zendy; Stavros J. Hamodrakas | Zendy

Open Access

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

Author(s) -

Nikolaos G. Sgourakis,

Pantelis G. Bagos,

Stavros J. Hamodrakas

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti679

Subject(s) - g protein coupled receptor , false positive paradox , coupling (piping) , artificial intelligence , computer science , artificial neural network , computational biology , markov chain , hidden markov model , machine learning , receptor , biology , genetics , mechanical engineering , engineering

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.

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