Proteomic applications of automated GPCR classification

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Standard

Proteomic applications of automated GPCR classification. / Davies, Matthew N; Gloriam, David E.; Secker, Andrew; Freitas, Alex A; Mendao, Miguel; Timmis, Jon; Flower, Darren R.

In: Proteomics, Vol. 7, No. 16, 08.2007, p. 2800-14.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Davies, MN, Gloriam, DE, Secker, A, Freitas, AA, Mendao, M, Timmis, J & Flower, DR 2007, 'Proteomic applications of automated GPCR classification', Proteomics, vol. 7, no. 16, pp. 2800-14. https://doi.org/10.1002/pmic.200700093

APA

Davies, M. N., Gloriam, D. E., Secker, A., Freitas, A. A., Mendao, M., Timmis, J., & Flower, D. R. (2007). Proteomic applications of automated GPCR classification. Proteomics, 7(16), 2800-14. https://doi.org/10.1002/pmic.200700093

Vancouver

Davies MN, Gloriam DE, Secker A, Freitas AA, Mendao M, Timmis J et al. Proteomic applications of automated GPCR classification. Proteomics. 2007 Aug;7(16):2800-14. https://doi.org/10.1002/pmic.200700093

Author

Davies, Matthew N ; Gloriam, David E. ; Secker, Andrew ; Freitas, Alex A ; Mendao, Miguel ; Timmis, Jon ; Flower, Darren R. / Proteomic applications of automated GPCR classification. In: Proteomics. 2007 ; Vol. 7, No. 16. pp. 2800-14.

Bibtex

@article{fbb4d0f38d6842dd8048ae66a29d54b4,
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",
language = "English",
volume = "7",
pages = "2800--14",
journal = "Proteomics",
issn = "1615-9853",
publisher = "Wiley - V C H Verlag GmbH & Co. KGaA",
number = "16",

}

RIS

TY - JOUR

T1 - Proteomic applications of automated GPCR classification

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

VL - 7

SP - 2800

EP - 2814

JO - Proteomics

JF - Proteomics

SN - 1615-9853

IS - 16

ER -

ID: 45811488