蛋白质NMR中的化学交换诱导放松的机制模型
1Computational Molecular Biology, FB Mathematik und Informatik, Freie Universität Berlin , Berlin 14195, Germany.
Journal of the American Chemical Society
|December 14, 2016
概括
用马尔科夫状态模型进行的分子动力学模拟提供了蛋白质动力学的微观视图. 这种方法有助于解释NMR放松数据和验证计算模型.
科学领域:
- 生物物理
- 计算生物学
- 结构生物学
背景情况:
- 蛋白质的结构状态和相互转换率对于功能至关重要.
- 核磁共振 (NMR) 放松测量可以探测蛋白质结构和动态.
- 由于对多种因素的敏感性,使用传统模型来解释NMR数据具有挑战性.
研究的目的:
- 开发一种微观解读NMR放松数据的方法.
- 通过模拟建立蛋白质动态的机制模型.
- 验证蛋白质结构和动态预测的计算方法.
主要方法:
- 使用分子动力学 (MD) 模拟.
- 使用马尔科夫状态模型 (MSM) 和隐藏的马尔科夫状态模型 (HMSM) 分析模拟轨迹.
- 将这种方法应用于无素和牛胰腺素抑制剂 (BPTI) 系统.
主要成果:
- 证明MD模拟和MSM可以为NMR放松数据提供机械模型.
- 展示了将实验数据分解为元稳定状态之间的动态过程的能力.
- 使用良好的蛋白质系统验证了该方法.
结论:
- 与MSM相结合的MD模拟为了解微观水平的蛋白质动态提供了强大的工具.
- 这种方法有助于对NMR放松数据进行机械解释.
- 该方法作为验证分子力学力场和化学转移预测算法的下一代工具.
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