GPCRTree: Online hierarchical classification of GPCR function

Matthew N. Davies, Andrew Secker, Mark Halling-Brown, David S. Moss, Alex A. Freitas, Jon Timmis, Edward Clark, Darren R. Flower

Research output: Contribution to journalArticlepeer-review

31 Citations (SciVal)

Abstract

Background: G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext. cryst.bbk.ac.uk/gpcrtree/.

Original languageEnglish
Article number67
JournalBMC Research Notes
Volume1
DOIs
Publication statusPublished - 21 Aug 2008

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