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Minimalist Model for the Dynamics of Helical Polypeptides: A Statistic-Based Parametrization

机译:螺旋多肽动力学的极简模型:基于统计的参数化。

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Low-resolution models are often used to address macroscopic time and size scales in molecular dynamics simulations of biomolecular systems. Coarse graining is often coupled to knowledge-based parametrization to obtain empirical potentials able to reproduce the system thermodynamic behavior. Here, a minimalist coarse grained (GC) model for the helical structures of proteins is reported. A knowledge-based parametrization strategy is coupled to the explicit inclusion of hydrogen-bonding-related terms, resulting in an accurate reproduction of the structure and dynamics of each single helical type, as well as the internal conformational variables correlation. The proposed strategy of basing the force field terms on real physicochemical interactions is transferable to different secondary structures. Thus, this work, though conclusive for helices, is to be considered the first of a series devoted to the application of the knowledge-based, physicochemical model to extended secondary structures and unstructured proteins.
机译:在生物分子系统的分子动力学模拟中,低分辨率模型通常用于解决宏观时间和规模尺度。粗粒度化通常与基于知识的参数化相结合,以获得能够重现系统热力学行为的经验潜能。在此,报告了蛋白质螺旋结构的极简粗粒度(GC)模型。基于知识的参数化策略与氢键相关术语的显式包含结合在一起,从而可以精确再现每个单个螺旋类型的结构和动力学,以及内部构象变量的相关性。将力场项基于实际物理化学相互作用的拟议策略可转移到不同的二级结构。因此,尽管这项工作对于螺旋作用尚无定论,但应被认为是致力于将基于知识的物理化学模型应用于扩展的二级结构和非结构化蛋白的系列文章中的第一篇。

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