By the same authors

From the same journal

Proteomic applications of automated GPCR classification

Research output: Contribution to journalLiterature review

Published copy (DOI)

Author(s)

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

Department/unit(s)

Publication details

JournalProteomics
DatePublished - Aug 2007
Issue number16
Volume7
Number of pages15
Pages (from-to)2800-2814
Original languageEnglish

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.

    Research areas

  • 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

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