Proteomic applications of automated GPCR classification

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

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

36 Citations (SciVal)

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 languageEnglish
Pages (from-to)2800-2814
Number of pages15
JournalProteomics
Volume7
Issue number16
DOIs
Publication statusPublished - Aug 2007

Keywords

  • Alignment
  • Bioinformatics
  • Classification
  • GPCR
  • Tools
  • Automation
  • Markov Chains
  • Receptors, G-Protein-Coupled/chemistry
  • Proteomics

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