蛋白质拥挤时肽折叠的马尔可夫模型
Markov modeling of peptide folding in the presence of protein crowders
摘要：
我们使用马尔可夫状态模型（MSMs）来分析动态的$β$ -发夹形成肽在Monte Carlo（MC）模拟与相互作用的蛋白质拥挤，两种不同类型的拥挤蛋白质[牛胰胰蛋白酶抑制剂（BPTI）和GB 1]。在这些系统中，在使用的温度下，肽可以折叠或解折叠，并与拥挤分子结合或不结合。可以确定四个或五个主要的自由能极小值。为了估计肽的主要MC弛豫时间，我们使用一系列不同的时间分辨率或滞后时间来构建MSM。我们表明，稳定的弛豫时间的估计，可以通过拟合自相关数据的MSM特征函数。本征函数保持足够的准确性，允许稳定的松弛时间估计到小的滞后时间，在这一点上，简单的估计相应的本征值的基础上有很大的系统不确定性。拥挤物的存在对肽具有稳定作用，特别是对于BPTI拥挤物，这可归因于降低的解折叠速率$k_\text{u}$，而折叠速率$k_\text{f}$基本保持不变。
英文摘要：
We use Markov state models (MSMs) to analyze the dynamics of a $β$-hairpin-forming peptide in Monte Carlo (MC) simulations with interacting protein crowders, for two different types of crowder proteins [bovine pancreatic trypsin inhibitor (BPTI) and GB1]. In these systems, at the temperature used, the peptide can be folded or unfolded and bound or unbound to crowder molecules. Four or five major free-energy minima can be identified. To estimate the dominant MC relaxation times of the peptide, we build MSMs using a range of different time resolutions or lag times. We show that stable relaxation-time estimates can be obtained from the MSM eigenfunctions through fits to autocorrelation data. The eigenfunctions remain sufficiently accurate to permit stable relaxation-time estimation down to small lag times, at which point simple estimates based on the corresponding eigenvalues have large systematic uncertainties. The presence of the crowders have a stabilizing effect on the peptide, especially with BPTI crowders, which can be attributed to a reduced unfolding rate $k_\text{u}$, while the folding rate $k_\text{f}$ is left largely unchanged.
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