USING MARKOV MODELS TO SIMULATE ESR SPECTRA FROM MD TRAJECTORIES

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心赠款提供。次级项目的主要支助 子项目的主要研究者可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， NCRR赠款不直接向子项目或子项目工作人员提供资金。 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道构建自旋标签动力学的精确随机模型的可能性。一个系统的，两个步骤的过程，基于隐马尔可夫模型的概率框架，建立一个离散时间马尔可夫链过程，忠实地捕捉内部自旋标签动态的时间尺度上长于约150 ps。所构建的马尔可夫模型被用来深入了解的自旋标签的长寿命的构象，并产生所需的ESR谱模拟的随机轨迹。该方法已被说明与应用程序的情况下，自旋标记的聚丙氨酸α螺旋在明确的溶剂。

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.