Title :
Chaos of protein folding
Author :
Bahi, Jacques M. ; Côté, Nathalie ; Guyeux, Christophe
Author_Institution :
Lab. LIFC, Univ. of Franche-Comte, Besancon, France
fDate :
July 31 2011-Aug. 5 2011
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;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033463