• DocumentCode
    1136774
  • Title

    Peptide binding to major histocompatibility complex

  • Author

    Rajapakse, Menaka ; Feng, Lin

  • Author_Institution
    Lilly Singapore Center for Drug Discovery, Singapore, Singapore
  • Volume
    28
  • Issue
    4
  • fYear
    2009
  • Firstpage
    73
  • Lastpage
    77
  • Abstract
    Peptide binding to major histocompatibility complex (MHC) molecules is a prerequisite for initiating an immune response. This article first describes an approach to predict MHC-peptide-binding sites by using an evolutionary algorithm (EA). The predicted binders are subsequently characterized for their physicochemical properties. The details and implementation issues of the peptide-binding prediction technique are discussed, and the performance comparison with the existing methods is provided. The binding motif derived in silico is used to characterize the physicochemical properties of the experimentally determined binders.
  • Keywords
    biochemistry; biology computing; evolutionary computation; molecular biophysics; proteins; evolutionary algorithm; immune response; major histocompatibility complex; peptide binding; physicochemical properties; Artificial neural networks; Cancer; Evolutionary computation; Hidden Markov models; Immune system; Peptides; Predictive models; Sequences; Support vector machine classification; Support vector machines; Algorithms; Amino Acids; Animals; Artificial Intelligence; Computational Biology; Computer Simulation; Histocompatibility Antigens Class II; Humans; Hydrophobicity; Major Histocompatibility Complex; Mice; Mice, Inbred NOD; Models, Biological; Models, Genetic; Models, Immunological; Peptides; Reproducibility of Results; Thermodynamics;
  • fLanguage
    English
  • Journal_Title
    Engineering in Medicine and Biology Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    0739-5175
  • Type

    jour

  • DOI
    10.1109/MEMB.2009.932922
  • Filename
    5165228