A Motif Detection and Classification Method for Peptide Sequences Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Yasuyuki_Tomita2008154,
  title =        "A Motif Detection and Classification Method for
                 Peptide Sequences Using Genetic Programming",
  author =       "Yasuyuki Tomita and Ryuji Kato and Mina Okochi and 
                 Hiroyuki Honda",
  journal =      "Journal of Bioscience and Bioengineering",
  volume =       "106",
  number =       "2",
  pages =        "154--161",
  year =         "2008",
  publisher =    "The Society for Biotechnology, Japan",
  keywords =     "genetic algorithms, genetic programming, property
                 motif, peptide, alignment, fuzzy neural network",
  DOI =          "doi:10.1263/jbb.106.154",
  abstract =     "An exploration of common rules (property motifs) in
                 amino acid sequences has been required for the design
                 of novel sequences and elucidation of the interactions
                 between molecules controlled by the structural or
                 physical environment. In the present study, we
                 developed a new method to search property motifs that
                 are common in peptide sequence data. Our method
                 comprises the following two characteristics: (i) the
                 automatic determination of the position and length of
                 common property motifs by calculating the
                 physicochemical similarity of amino acids, and (ii) the
                 quick and effective exploration of motif candidates
                 that discriminates the positives and negatives by the
                 introduction of genetic programming (GP). Our method
                 was evaluated by two types of model data sets. First,
                 the intentionally buried property motifs were searched
                 in the artificially derived peptide data containing
                 intentionally buried property motifs. As a result, the
                 expected property motifs were correctly extracted by
                 our algorithm. Second, the peptide data that interact
                 with MHC class II molecules were analysed as one of the
                 models of biologically active peptides with buried
                 motifs in various lengths. Twofold MHC class II binding
                 peptides were identified with the rule using our
                 method, compared to the existing scoring matrix method.
                 In conclusion, our GP based motif searching approach
                 enabled to obtain knowledge of functional aspects of
                 the peptides without any prior knowledge.",
  notes =        "Department of Biotechnology, School of Engineering,
                 Nagoya University, Nagoya, Japan.

                 PMID: 18804058 [PubMed - in process]",
}

Genetic Programming entries for Yasuyuki Tomita Ryuji Kato Mina Okochi Hiroyuki Honda

Citations