Proteomic applications of automated GPCR classification

Matthew N Davies; David E. Gloriam; Andrew Secker; Alex A Freitas; Miguel Mendao; Jon Timmis; Darren R Flower

doi:10.1002/pmic.200700093

Proteomic applications of automated GPCR classification

Matthew N Davies, David E. Gloriam, Andrew Secker, Alex A Freitas, Miguel Mendao, Jon Timmis, Darren R Flower

33 Citationer (Scopus)

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.

Originalsprog	Engelsk
Tidsskrift	Proteomics
Vol/bind	7
Udgave nummer	16
Sider (fra-til)	2800-14
Antal sider	15
ISSN	1615-9853
DOI	https://doi.org/10.1002/pmic.200700093
Status	Udgivet - aug. 2007
Udgivet eksternt	Ja

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10.1002/pmic.200700093

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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.",

author = "Davies, {Matthew N} and Gloriam, {David E.} and Andrew Secker and Freitas, {Alex A} and Miguel Mendao and Jon Timmis and Flower, {Darren R}",

year = "2007",

month = aug,

doi = "10.1002/pmic.200700093",

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AU - Davies, Matthew N

AU - Gloriam, David E.

AU - Secker, Andrew

AU - Freitas, Alex A

AU - Mendao, Miguel

AU - Timmis, Jon

AU - Flower, Darren R

PY - 2007/8

Y1 - 2007/8

N2 - 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.

AB - 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.

U2 - 10.1002/pmic.200700093

DO - 10.1002/pmic.200700093

M3 - Journal article

C2 - 17639603

SN - 1615-9853

VL - 7

SP - 2800

EP - 2814

JO - Proteomics

JF - Proteomics

IS - 16

ER -

Proteomic applications of automated GPCR classification

Abstract

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