Mechanistic Models of Chemical Exchange Induced Relaxation in Protein NMR
Mechanistic Models of Chemical Exchange Induced Relaxation in Protein NMR
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DOI：
10.1021/jacs.6b09460
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发表时间：
2017-01-11
影响因子：
15
通讯作者：
Noe, Frank
中科院分区：
文献类型：
--
作者：
Olsson, Simon;Noe, Frank
长寿命的构象状态及其相互转化率决定了蛋白质的功能和调控。当这些态具有不同的化学位移时，用核磁共振测量弛豫可能为我们提供关于它们的结构、动力学和原子分辨率下的热力学的有用信息。然而，由于这些实验数据对许多结构和动力学效应很敏感，即使只涉及几个亚稳态，用唯象模型解释它们也是具有挑战性的。因此，必须经常使用近似和简化，这增加了遗漏隐藏在数据中的重要微观特征的风险。在这里，我们展示了如何使用通过马尔可夫状态模型和相关的隐马尔可夫状态模型分析的分子动力学模拟来建立提供对核磁共振弛豫数据的微观解释的机制模型。以泛素和BPTI为例，我们演示了该方法如何允许我们将实验数据分解为亚稳态之间的一些动态过程。这样的微观观点可以极大地促进对实验数据的机理解释，并作为验证分子力学力场和化学位移预测算法的下一代方法。
Long-lived conformational states and their interconversion rates critically determine protein function and regulation. When these states have distinct chemical shifts, the measurement of relaxation by NMR may provide us with useful information about their structure, kinetics, and thermodynamics at atomic resolution. However, as these experimental data are sensitive to many structural and dynamic effects, their interpretation with phenomenological models is challenging, even if only a few metastable states are involved. Consequently, approximations and simplifications must often be used which increase the risk of missing important microscopic features hidden in the data. Here, we show how molecular dynamics simulations analyzed through Markov state models and the related hidden Markov state models may be used to establish mechanistic models that provide a microscopic interpretation of NMR relaxation data. Using ubiquitin and BPTI as examples, we demonstrate how the approach allows us to dissect experimental data into a number of dynamic processes between metastable states. Such a microscopic view may greatly facilitate the mechanistic interpretation of experimental data and serve as a next-generation method for the validation of molecular mechanics force fields and chemical shift prediction algorithms.