NDLI: Protein Folding in the 2D Hydrophobic–Hydrophilic (HP) Square Lattice Model is Chaotic

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Protein Folding in the 2D Hydrophobic–Hydrophilic (HP) Square Lattice Model is Chaotic

Content Provider	Springer Nature Link
Author	Bahi, Jacques M. Côté, Nathalie Guyeux, Christophe Salomon, Michel
Copyright Year	2011
Abstract	Among the unsolved problems in computational biology, protein folding is one of the most interesting challenges. To study this folding, tools like neural networks and genetic algorithms have received a lot of attention, mainly due to the NP completeness of the folding process. The background idea that has given rise to the use of these algorithms is obviously that the folding process is predictable. However, this important assumption is disputable as chaotic properties of such a process have been recently highlighted. In this paper, which is an extension of a former work accepted to the 2011 International Joint Conference on Neural Networks (IJCNN11), the topological behavior of a well-known dynamical system used for protein folding prediction is evaluated. It is mathematically established that the folding dynamics in the 2D hydrophobic–hydrophilic (HP) square lattice model, simply called the 2D model in this document, is indeed a chaotic dynamical system as defined by Devaney. Furthermore, the chaotic behavior of this model is qualitatively and quantitatively deepened, by studying other mathematical properties of disorder, namely: the indecomposability, instability, strong transitivity, and constants of expansivity and sensitivity. Some consequences for both biological paradigms and structure prediction using this model are then discussed. In particular, it is shown that some neural networks seems to be unable to predict the evolution of this model with accuracy, due to its complex behavior.
Starting Page	98
Ending Page	114
Page Count	17
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	4
Issue Number	1
e-ISSN	18669964
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-11-21
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Folding prediction Devaney’s chaos Neural networks Mathematical topology Computational Biology/Bioinformatics Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Neurosciences
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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