Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

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

Research output: Contribution to journalReview articlepeer-review

8 Citations (SciVal)

Abstract

The G-protein coupled receptors - or GPCRs - comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence relations, implicit within the family, into an adaptive approach to classification. Importantly, we also allude to some of the key innate problems in developing an effective approach to classifying the GPCRs: the lack of sequence similarity between the six classes that comprise the GPCR family and the low sequence similarity to other family members evinced by many newly revealed members of the family.

Original languageEnglish
Pages (from-to)1994-2009
Number of pages16
JournalCurrent Topics in Medicinal Chemistry
Volume11
Issue number15
DOIs
Publication statusPublished - Aug 2011

Keywords

  • Alignment
  • Bioinformatics
  • Classification
  • GPCR
  • Tools
  • Amino Acid Sequence
  • Sequence Analysis, Protein
  • Sequence Alignment
  • Receptors, G-Protein-Coupled/chemistry
  • Artificial Intelligence
  • Ligands
  • Protein Conformation

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