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Proteomic applications of automated GPCR classification

Davies, Matthew N., Gloriam, David E., Secker, Andrew D., Freitas, Alex A., Mendao, Miguel, Timmis, Jon, Flower, Darren R. (2007) Proteomic applications of automated GPCR classification. Proteomics, 7 (16). pp. 2800-2814. ISSN 1615-9853. (doi:10.1002/pmic.200700093) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:14555)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1002/pmic.200700093

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.

Item Type: Article
DOI/Identification number: 10.1002/pmic.200700093
Uncontrolled keywords: data mining, classification, bioinformatics alignment; bioinformatics; classification; GPCR; tools
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04 UTC
Last Modified: 16 Feb 2021 12:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14555 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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