How kinetics within the unfolded state affects protein folding: an analysis based on markov state models and an ultra-long MD trajectory.
How kinetics within the unfolded state affects protein folding: an analysis based on markov state models and an ultra-long MD trajectory.
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DOI：
10.1021/jp401962k
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发表时间：
2013-10-24
期刊：
影响因子：
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通讯作者：
Levy RM
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文献类型：
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作者：
Deng NJ;Dai W;Levy RM
了解未折叠状态下的动力学如何影响蛋白质折叠是一个从根本上重要但却不太为人所知的问题。在这里，我们使用三个不同的模型来分析微小蛋白Trp-Cage的展开景观和折叠动力学。第一个是D.E.Shaw Research的208μS显式溶剂分子动力学(MD)模拟，包含数十个折叠事件。第二种是马尔可夫状态模型(MSM-MD)，该模型由相同的超长MD模拟而成；MSM-MD可用于生成数千个折叠事件。第三种是隐式溶剂中温度副本交换分子动力学模拟建立的马尔可夫状态模型(MSM-REMD)。所有模型都显示了多条折叠路径，直接分子动力学计算的折叠路径与MSM计算的折叠路径有很好的对应关系。与≤5≈μS的折叠时间相比，在40 ns的时间尺度上，未折叠群体在伸展构象和折叠构象之间快速相互转换，折叠速率与折叠从何处开始无关。约90%的展开态是在超长MD轨迹的前40μS内采样的，平均而言，在连续的折叠事件之间探索了约27%的展开态集合。我们根据结构相似性将折叠路径聚集成“管”，并动态地将未折叠状态划分为沿不同管折叠的种群。通过对模拟结果和一个简单的动力学模型的分析，我们发现，当未折叠状态下的混合与折叠相当或快于折叠时，所有折叠管的折叠等待时间相似，折叠动力学本质上是单指数的，尽管存在具有非均匀势垒的多相折叠路径。当混合比折叠慢得多时，不同的未折叠群体独立折叠，导致非指数动力学。构造了Trp笼展开态的动力学划分，揭示了不同的未折叠群体沿着多条折叠路径中的任何一条折叠的概率几乎相同。我们正在研究20个残基Trp-Cage在展开状态下的动力学结果是否代表更大的单域蛋白。
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 ultra-long 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 ultra-long 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 non-uniform barriers. When the mixing is much slower than folding, different unfolded populations fold independently leading to non-exponential 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 twenty-residue Trp-cage is representative of larger single domain proteins.