Optimizing amino acid groupings for GPCR classification

Matthew N. Davies, Andrew Secker, Alex A. Freitas, Edward Clark, Jon Timmis, Darren R. Flower

Research output: Contribution to journalArticlepeer-review


MOTIVATION: There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings.
RESULTS: Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.
Original languageEnglish
Pages (from-to)1980-1986
Number of pages7
Issue number18
Early online date1 Aug 2008
Publication statusPublished - 18 Sept 2008


  • algorithms
  • amino acids
  • artificial intelligence
  • computational biology
  • protein databases
  • G-protein-coupled receptors
  • protein sequence analysis


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