首页> 外文期刊>International Journal of High Performance Computing Applications >MIMICKING PROTEIN DYNAMICS BY THE INTEGRATION OF ELASTIC NETWORK MODEL WITH TIME SERIES ANALYSIS
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MIMICKING PROTEIN DYNAMICS BY THE INTEGRATION OF ELASTIC NETWORK MODEL WITH TIME SERIES ANALYSIS

机译:弹性网络模型与时间序列分析相结合的模拟蛋白质动力学

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摘要

Anisotropic network model (ANM) is a coarse-grained normal mode analysis that is widely used for describing the collective motions of proteins around their native structure. In this work, protein dynamics along ANM modes are constructed by linear stochastic time series models extracted from molecular dynamics (MD) simulations, and these models are simulated to mimic the dynamics of a relatively small protein, tendamistat. It is found that ANM modes are at least as successful as principal components analysis in explaining different conformational subspaces, and the nonstationary character of ANM modes is a further advan-tage. The significant reduction in computation time makes time series simulations a promising alternative to the standard requirement of performing multiple long MD runs especially in water, provided that a single short MD is available for extracting the models and some energy restrictions are performed during the moves.
机译:各向异性网络模型(ANM)是一种粗粒度的正常模式分析,广泛用于描述蛋白质围绕其天然结构的集体运动。在这项工作中,通过从分子动力学(MD)模拟中提取的线性随机时间序列模型构建了沿ANM模式的蛋白质动力学,并对这些模型进行了模拟,以模拟相对较小的蛋白质(tendamistat)的动力学。发现在解释不同的构象子空间方面,ANM模式至少与主成分分析一样成功,并且ANM模式的非平稳特性是进一步的优势。计算时间的显着减少使得时间序列仿真成为执行多次长MD运行(尤其是在水中)的标准要求的有希望的替代方法,前提是可以使用单个短MD来提取模型,并且在移动过程中执行一些能量限制。

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