USING MARKOV MODELS TO SIMULATE ESR SPECTRA FROM MD TRAJECTORIES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 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. 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.