Proteomic applications of automated GPCR classification

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

Research output: Contribution to journal › Literature review › peer-review

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.

Original language	English
Pages (from-to)	2800-2814
Number of pages	15
Journal	Proteomics
Volume	7
Issue number	16
DOIs	https://doi.org/10.1002/pmic.200700093
Publication status	Published - Aug 2007

Keywords

alignment
bioinformatics
classification
GPCR
tools
PROTEIN-COUPLED-RECEPTORS
SUPPORT VECTOR MACHINE
FAST FOURIER-TRANSFORM
HIDDEN MARKOV MODEL
DRUG DISCOVERY
UROTENSIN-II
HUMAN GENOME
PREDICTION
IDENTIFICATION
REPERTOIRE

Access to Document

10.1002/pmic.200700093

Cite this

@article{e4766e0f7d9e4ea3a49522db97f97bfd,

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.",

keywords = "alignment, bioinformatics, classification, GPCR, tools, PROTEIN-COUPLED-RECEPTORS, SUPPORT VECTOR MACHINE, FAST FOURIER-TRANSFORM, HIDDEN MARKOV MODEL, DRUG DISCOVERY, UROTENSIN-II, HUMAN GENOME, PREDICTION, IDENTIFICATION, REPERTOIRE",

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

year = "2007",

month = aug,

doi = "10.1002/pmic.200700093",

language = "English",

volume = "7",

pages = "2800--2814",

journal = "Proteomics",

issn = "1615-9853",

publisher = "Wiley-Blackwell",

number = "16",

}

TY - JOUR

T1 - Proteomic applications of automated GPCR classification

AU - Davies, Matthew N.

AU - Gloriam, David E.

AU - Secker, Andrew

AU - Freitas, Alexa 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.

KW - alignment

KW - bioinformatics

KW - classification

KW - GPCR

KW - tools

KW - PROTEIN-COUPLED-RECEPTORS

KW - SUPPORT VECTOR MACHINE

KW - FAST FOURIER-TRANSFORM

KW - HIDDEN MARKOV MODEL

KW - DRUG DISCOVERY

KW - UROTENSIN-II

KW - HUMAN GENOME

KW - PREDICTION

KW - IDENTIFICATION

KW - REPERTOIRE

U2 - 10.1002/pmic.200700093

DO - 10.1002/pmic.200700093

M3 - Literature review

SN - 1615-9853

VL - 7

SP - 2800

EP - 2814

JO - Proteomics

JF - Proteomics

IS - 16

ER -