USING MARKOV MODELS TO SIMULATE ESR SPECTRA FROM MD TRAJECTORIES

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 直接从自旋标记蛋白质的分子动力学模拟来模拟电子自旋共振(ESR)谱需要大量(数百或数千)相对长(数百ns)的轨迹。为了迎接这一挑战，我们探索了从原子轨迹构建准确的自旋标签动力学随机模型的可能性。基于隐马尔可夫模型的概率框架，开发了一个系统的两步过程，以建立一个离散时间马尔可夫链过程，该过程忠实地捕获了时间尺度上长于约150ps的内部自旋标号动力学。所构建的马尔可夫模型被用来洞察自旋标记物的长寿命构象，并产生模拟ESR谱所需的随机轨迹。以显性溶剂中自旋标记的聚丙氨酸α螺旋为例，说明了该方法的应用。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Simulating electron spin resonance (ESR) spectra directly from molecular dynamics simulations of a spin labeled protein necessitates a large number(hundreds or thousands) of relatively long (hundreds of ns) trajectories. To meet this challenge, we explored the possibility of constructing accurate stochastic models of the spin label dynamics from atomistic trajectories. A systematic, two-step procedure, based on the probabilistic framework of hidden Markov models,is developed to build a discrete-time Markov chain process that faithfully captures the internal spin label dynamics on time scales longer than about 150 ps. The constructed Markov model was used both to gain insight into the long-lived conformations of the spin label and to generate the stochastic trajectories required for the simulation of ESR spectra. The methodology has been illustrated with an application to the case of a spin labeled poly-alanine alpha helix in explicit solvent.