• DocumentCode
    3497226
  • Title

    Chaos of protein folding

  • Author

    Bahi, Jacques M. ; Côté, Nathalie ; Guyeux, Christophe

  • Author_Institution
    Lab. LIFC, Univ. of Franche-Comte, Besancon, France
  • fYear
    2011
  • fDate
    July 31 2011-Aug. 5 2011
  • Firstpage
    1948
  • Lastpage
    1954
  • Abstract
    As protein folding is a NP-complete problem, artificial intelligence tools like neural networks and genetic algorithms are used to attempt to predict the 3D shape of an amino acids sequence. Underlying these attempts, it is supposed that this folding process is predictable. However, to the best of our knowledge, this important assumption has been neither proven, nor studied. In this paper the topological dynamic of protein folding is evaluated. It is mathematically established that protein folding in 2D hydrophobic-hydrophilic (HP) square lattice model is chaotic as defined by Devaney. Consequences for both structure prediction and biology are then outlined.
  • Keywords
    artificial intelligence; biology computing; computational complexity; genetic algorithms; neural nets; proteins; 2D hydrophobic-hydrophilic square lattice model; 3D shape prediction; NP-complete problem; amino acids sequence; artificial intelligence tools; biology; genetic algorithm; neural network; protein folding chaos; structure prediction; topological dynamic; Amino acids; Chaos; Encoding; Lattices; Proteins; Three dimensional displays; Tin;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2011 International Joint Conference on
  • Conference_Location
    San Jose, CA
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4244-9635-8
  • Type

    conf

  • DOI
    10.1109/IJCNN.2011.6033463
  • Filename
    6033463