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arXiv 1608.05855q-bio.BMmath.DS
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二维疏水-亲水(HP)正方格子模型中蛋白质折叠的混沌性

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

Jacques M. Bahi, Nathalie Côté, Christophe Guyeux, Michel Salomon

摘要:

在计算生物学尚未解决的问题中,蛋白质折叠是最有趣的挑战之一。为了研究这种折叠,像神经网络和遗传算法这样的工具受到了很多关注,主要是由于折叠过程的NP完全性。使用这些算法的背景思想显然是折叠过程是可预测的。然而,这个重要的假设是有争议的,因为最近已经强调了这样一个过程的混沌特性。本文是2011年国际神经网络联合会议(IJCNN 11)接受的先前工作的扩展,评估了用于蛋白质折叠预测的著名动力系统的拓扑行为。在数学上建立了2D疏水-亲水(HP)正方形晶格模型(在本文献中简称为“2D模型”)中的折叠动力学确实是如Devaney定义的混沌动力学系统。此外,通过研究无序的其他数学性质,即:不可分解性、不稳定性、强传递性、膨胀性常数和灵敏度常数,定性和定量地深化了该模型的混沌行为。然后讨论了使用该模型的生物学范式和结构预测的一些后果。特别是,它表明,一些神经网络似乎是无法准确地预测这个模型的演变,由于其复杂的行为。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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