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 journalArticle

Abstract

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
JournalBioinformatics
Volume24
Issue number18
Early online date1 Aug 2008
DOIs
Publication statusPublished - 18 Sep 2008

Fingerprint

G Protein
G-Protein-Coupled Receptors
Grouping
Receptor
Amino Acids
Amino acids
Proteins
Descriptors
Artificial Immune System
Computational Intelligence
Immune system
Artificial Intelligence
Bioinformatics
Classification Algorithm
Computational Biology
Artificial intelligence
Immune System
Optimization Algorithm
Alignment
Paradigm

Keywords

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

Cite this

Davies, M. N., Secker, A., Freitas, A. A., Clark, E., Timmis, J., & Flower, D. R. (2008). Optimizing amino acid groupings for GPCR classification. Bioinformatics, 24(18), 1980-1986. https://doi.org/10.1093/bioinformatics/btn382
Davies, Matthew N. ; Secker, Andrew ; Freitas, Alex A. ; Clark, Edward ; Timmis, Jon ; Flower, Darren R. / Optimizing amino acid groupings for GPCR classification. In: Bioinformatics. 2008 ; Vol. 24, No. 18. pp. 1980-1986.
@article{6e92a6a3ad254d40a5416faf13641ebd,
title = "Optimizing amino acid groupings for GPCR classification",
abstract = "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.",
keywords = "algorithms, amino acids, artificial intelligence, computational biology, protein databases, G-protein-coupled receptors, protein sequence analysis",
author = "Davies, {Matthew N.} and Andrew Secker and Freitas, {Alex A.} and Edward Clark and Jon Timmis and Flower, {Darren R.}",
year = "2008",
month = "9",
day = "18",
doi = "10.1093/bioinformatics/btn382",
language = "English",
volume = "24",
pages = "1980--1986",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "18",

}

Davies, MN, Secker, A, Freitas, AA, Clark, E, Timmis, J & Flower, DR 2008, 'Optimizing amino acid groupings for GPCR classification', Bioinformatics, vol. 24, no. 18, pp. 1980-1986. https://doi.org/10.1093/bioinformatics/btn382

Optimizing amino acid groupings for GPCR classification. / Davies, Matthew N.; Secker, Andrew; Freitas, Alex A.; Clark, Edward; Timmis, Jon; Flower, Darren R.

In: Bioinformatics, Vol. 24, No. 18, 18.09.2008, p. 1980-1986.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Optimizing amino acid groupings for GPCR classification

AU - Davies, Matthew N.

AU - Secker, Andrew

AU - Freitas, Alex A.

AU - Clark, Edward

AU - Timmis, Jon

AU - Flower, Darren R.

PY - 2008/9/18

Y1 - 2008/9/18

N2 - 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.

AB - 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.

KW - algorithms

KW - amino acids

KW - artificial intelligence

KW - computational biology

KW - protein databases

KW - G-protein-coupled receptors

KW - protein sequence analysis

UR - http://www.scopus.com/inward/record.url?scp=51749097799&partnerID=8YFLogxK

UR - http://bioinformatics.oxfordjournals.org/content/24/18/1980.abstract

U2 - 10.1093/bioinformatics/btn382

DO - 10.1093/bioinformatics/btn382

M3 - Article

C2 - 18676973

VL - 24

SP - 1980

EP - 1986

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 18

ER -

Davies MN, Secker A, Freitas AA, Clark E, Timmis J, Flower DR. Optimizing amino acid groupings for GPCR classification. Bioinformatics. 2008 Sep 18;24(18):1980-1986. https://doi.org/10.1093/bioinformatics/btn382