Fast kinetic experiments with dramatically improved time resolution have contributed significantly to understanding the fundamental processes in protein folding pathways involving the formation of a-helices and b-hairpin, contact formation, and overall collapse of the peptide chain. Interpretation of experimental results through application of a simple statistical mechanical model was key to this understanding. Atomistic description of all events observed in the experimental findings was challenging. Recent advancements in theory, more sophisticated algorithms, and a true long-term trajectory made way for an atomically detailed description of kinetics, examining folding pathways, validating experimental results, and reporting new findings for a wide range of molecular processes in biophysical chemistry. This review describes how optimum dimensionality reduction theory can construct a simplified coarse-grained model with low dimensionality involving a kinetic matrix that captures novel insights into folding pathways. A set of metastable states derived from molecular dynamics analysis generate an optimally reduced dimensionality rate matrix following transition pathway analysis. Analysis of the actual long-term simulation trajectory extracts a relaxation time directly comparable to the experimental results and confirms the validity of the combined approach. The application of the theory is discussed and illustrated using several examples of helix <==> coil transition pathways. This paper focuses primarily on a combined approach of time-resolved experiments and long-term molecular dynamics simulation from our ongoing work.

中文翻译：

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剖析螺旋弛豫动力学中的多个路径<==>具有最佳降维的线圈转变


时间分辨率显着提高的快速动力学实验极大地有助于理解蛋白质折叠途径的基本过程，包括 a-螺旋和 b-发夹的形成、接触形成和肽链的整体折叠。通过应用简单的统计力学模型来解释实验结果是这种理解的关键。对实验结果中观察到的所有事件进行原子描述具有挑战性。理论的最新进展、更复杂的算法和真正的长期轨迹为动力学的原子详细描述、检查折叠路径、验证实验结果以及报告生物物理化学中各种分子过程的新发现铺平了道路。这篇综述描述了最佳降维理论如何构建一个低维的简化粗粒度模型，涉及一个动力学矩阵，该矩阵捕获了对折叠路径的新见解。由分子动力学分析得出的一组亚稳态在转变路径分析后生成最佳降维率矩阵。对实际长期模拟轨迹的分析提取了与实验结果直接可比较的弛豫时间，并证实了组合方法的有效性。使用螺旋<==>线圈过渡路径的几个例子讨论和说明了该理论的应用。本文主要关注我们正在进行的工作中时间分辨实验和长期分子动力学模拟的组合方法。