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未折叠状态下的动力学如何影响蛋白质折叠:基于马科夫状态模型和超长 MD 轨迹的分析。

How kinetics within the unfolded state affects protein folding: an analysis based on markov state models and an ultra-long MD trajectory.

机构信息

BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers, the State University of New Jersey , Piscataway, New Jersey 08854, United States.

出版信息

J Phys Chem B. 2013 Oct 24;117(42):12787-99. doi: 10.1021/jp401962k. Epub 2013 Jun 13.

Abstract

Understanding how kinetics in the unfolded state affects protein folding is a fundamentally important yet less well-understood issue. Here we employ three different models to analyze the unfolded landscape and folding kinetics of the miniprotein Trp-cage. The first is a 208 μs explicit solvent molecular dynamics (MD) simulation from D. E. Shaw Research containing tens of folding events. The second is a Markov state model (MSM-MD) constructed from the same ultralong MD simulation; MSM-MD can be used to generate thousands of folding events. The third is a Markov state model built from temperature replica exchange MD simulations in implicit solvent (MSM-REMD). All the models exhibit multiple folding pathways, and there is a good correspondence between the folding pathways from direct MD and those computed from the MSMs. The unfolded populations interconvert rapidly between extended and collapsed conformations on time scales ≤40 ns, compared with the folding time of ∼5 μs. The folding rates are independent of where the folding is initiated from within the unfolded ensemble. About 90% of the unfolded states are sampled within the first 40 μs of the ultralong MD trajectory, which on average explores ∼27% of the unfolded state ensemble between consecutive folding events. We clustered the folding pathways according to structural similarity into "tubes", and kinetically partitioned the unfolded state into populations that fold along different tubes. From our analysis of the simulations and a simple kinetic model, we find that, when the mixing within the unfolded state is comparable to or faster than folding, the folding waiting times for all the folding tubes are similar and the folding kinetics is essentially single exponential despite the presence of heterogeneous folding paths with nonuniform barriers. When the mixing is much slower than folding, different unfolded populations fold independently, leading to nonexponential kinetics. A kinetic partition of the Trp-cage unfolded state is constructed which reveals that different unfolded populations have almost the same probability to fold along any of the multiple folding paths. We are investigating whether the results for the kinetics in the unfolded state of the 20-residue Trp-cage is representative of larger single domain proteins.

摘要

理解未折叠状态中的动力学如何影响蛋白质折叠是一个非常重要但尚未被充分理解的问题。在这里,我们使用三种不同的模型来分析小型蛋白 Trp-cage 的未折叠状态和折叠动力学。第一种是来自 D. E. Shaw Research 的长达 208 μs 的显式溶剂分子动力学 (MD) 模拟,其中包含数十次折叠事件。第二种是从相同的超长时间 MD 模拟构建的马尔可夫状态模型 (MSM-MD);MSM-MD 可用于生成数千次折叠事件。第三种是从隐式溶剂的温度复制交换 MD 模拟构建的马尔可夫状态模型 (MSM-REMD)。所有模型都显示出多种折叠途径,直接 MD 和 MSM 计算得到的折叠途径之间存在很好的对应关系。在时间尺度 ≤40 ns 内,未折叠的种群在伸展和折叠构象之间快速转换,而折叠时间约为 5 μs。折叠速率与从无折叠集合中何处开始折叠无关。在超长时间 MD 轨迹的前 40 μs 内,约有 90%的无折叠状态被采样,平均在连续折叠事件之间探索约 27%的无折叠状态集合。我们根据结构相似性将折叠途径聚类为“管”,并将未折叠状态在动力学上分为沿着不同管折叠的种群。从对模拟的分析和一个简单的动力学模型中,我们发现,当未折叠状态中的混合速度与折叠速度相当或更快时,所有折叠管的折叠等待时间相似,尽管存在具有不均匀势垒的异质折叠途径,但折叠动力学基本上是单指数的。当混合速度远慢于折叠速度时,不同的无折叠种群独立折叠,导致非指数动力学。构建了 Trp-cage 无折叠状态的动力学分区,结果表明不同的无折叠种群几乎以相同的概率沿着任何多条折叠途径折叠。我们正在研究 20 残基 Trp-cage 未折叠状态的动力学是否代表更大的单域蛋白。

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