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

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Authors:
  • Davies, Matthew N ;
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    King's College London
  • Gloriam, David E ;
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    Orcid logo0000-0002-4299-7561
    Biostructural Research, Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, Københavns Universitet
  • Secker, Andrew ;
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    University of Kent
  • Freitas, Alex A. ;
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    University of Kent
  • Timmis, Jon ;
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    University of York
  • Flower, Darren R.
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    Aston University
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.
Type:
Journal article
Language:
English
Published in:
Current Topics in Medicinal Chemistry, 2011, Vol 11, Issue 15, p. 1994-2009
Keywords:
The Faculty of Pharmaceutical Sciences
Main Research Area:
Medical science
Publication Status:
Published
Review type:
Peer Review
Submission year:
2011
Scientific Level:
Scientific
ID:
184041471
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