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
Link To Document