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arXiv 1306.1439q-bio.BM
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蛋白质结构预测软件生成两组不同的构象。或者是对展开的自我回避行走的研究

Protein structure prediction software generate two different sets of conformations. Or the study of unfolded self-avoiding walks

Jacques M. Bahi, Christophe Guyeux, Jean-Marc Nicod, Laurent Philippe

摘要:

自避免行走(SAW)是概率和枚举组合学中非常困难的问题的来源。由于它们是生物信息学中蛋白质结构预测的基础,因此也引起了极大的兴趣。这篇文章的作者之前已经表明,根据预测算法,获得的构象集不同:所有的自避免行走可以使用基于拉伸的算法来实现,而只有折叠的SAW可以使用迭代折叠直线的方法来实现。本文首次对(非)折叠式自我回避行走进行了研究。的贡献主要是什么是目前已知的这些集的调查。特别是,我们提供了清晰的定义,各种子集的自避免步行有关的枢轴移动(折叠或unfoldable锯,等)。以及我们在这些集合上从理论上或计算上获得的第一个结果。一个开放的问题列表也提供了,和后果的蛋白质结构预测问题的最后调查。

英文摘要:

Self-avoiding walks (SAW) are the source of very difficult problems in probabilities and enumerative combinatorics. They are also of great interest as they are, for instance, the basis of protein structure prediction in bioinformatics. Authors of this article have previously shown that, depending on the prediction algorithm, the sets of obtained conformations differ: all the self-avoiding walks can be reached using stretching-based algorithms whereas only the folded SAWs can be attained with methods that iteratively fold the straight line. A first study of (un)folded self-avoiding walks is presented in this article. The contribution is majorly a survey of what is currently known about these sets. In particular we provide clear definitions of various subsets of self-avoiding walks related to pivot moves (folded or unfoldable SAWs, etc.) and the first results we have obtained, theoretically or computationally, on these sets. A list of open questions is provided too, and the consequences on the protein structure prediction problem is finally investigated.

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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