Proteomic applications of automated GPCR classification

Davies, Matthew N. and Gloriam, David E. and Secker, Andrew D. and Freitas, Alex A. and Mendao, Miguel and Timmis, Jon and Flower, Darren R. (2007) Proteomic applications of automated GPCR classification. Proteomics, 7 (16). pp. 2800-2814. ISSN 1615-9853. (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)

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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
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: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04
Last Modified: 19 May 2014 16:00
Resource URI: (The current URI for this page, for reference purposes)
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