USING MARKOV MODELS TO SIMULATE ESR SPECTRA FROM MD TRAJECTORIES

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.