Stochastic model for protein flexibility analysis.
Stochastic model for protein flexibility analysis.
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DOI：
10.1103/physreve.88.062709
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
Kelin Xia;G. Wei
中科院分区：
文献类型：
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作者：
Kelin Xia;G. Wei
蛋白质的柔性是蛋白质的固有特性，在蛋白质功能中起着重要作用。蛋白质柔性的计算分析对于蛋白质功能预测、大分子柔性对接和合理药物设计具有重要意义。目前蛋白质柔性分析的大多数方法都是基于哈密顿力学。我们引入了一个随机模型来研究蛋白质的柔性。其基本思想是分析满足主方程的扰动蛋白质结构概率的自由诱导衰减。转移概率矩阵是通过使用包括单调递减径向基函数的概率密度估计来构造的。我们发现，建议的随机模型产生了一些最好的预测Debye-Waller因素或B因素的三组蛋白质数据在文献中介绍。
Protein flexibility is an intrinsic property and plays a fundamental role in protein functions. Computational analysis of protein flexibility is crucial to protein function prediction, macromolecular flexible docking, and rational drug design. Most current approaches for protein flexibility analysis are based on Hamiltonian mechanics. We introduce a stochastic model to study protein flexibility. The essential idea is to analyze the free induction decay of a perturbed protein structural probability, which satisfies the master equation. The transition probability matrix is constructed by using probability density estimators including monotonically decreasing radial basis functions. We show that the proposed stochastic model gives rise to some of the best predictions of Debye-Waller factors or B factors for three sets of protein data introduced in the literature.