title: Proteomic applications of automated GPCR classification.
abstract: The G-protein coupled receptor (GPCR) superfamily fulfils various metabolic functions and interacts with a diverse range of ligands. There is a lack of sequence similarity between the six classes that comprise the GPCR superfamily. Moreover, most novel GPCRs found have low sequence similarity to other family members which makes it difficult to infer properties from related receptors. Many different approaches have been taken towards developing efficient and accurate methods for GPCR classification, ranging from motif-based systems to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of their amino acid sequences. This review describes the inherent difficulties in developing a GPCR classification algorithm and includes techniques previously employed in this area.
authors.affiliation: Edward Jenner Institute, Compton, Newbury, Berkshire, UK. m.davies@mail.cryst.bbk.ac.uk
authors.forename: Matthew N
authors.initials: MN
authors.lastname: Davies
authors.forename: David E
authors.initials: DE
authors.lastname: Gloriam
authors.forename: Andrew
authors.initials: A
authors.lastname: Secker
authors.forename: Alex A
authors.initials: AA
authors.lastname: Freitas
authors.forename: Miguel
authors.initials: M
authors.lastname: Mendao
authors.forename: Jon
authors.initials: J
authors.lastname: Timmis
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower
mesh_terms: D001331:Automation; D008390:Markov Chains; D040901:Proteomics; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry / Q000145:classification*
chemical_list: D043562:Receptors, G-Protein-Coupled