Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 7-9, 55128 Mainz, Germany.
J Chem Phys. 2019 Feb 7;150(5):054103. doi: 10.1063/1.5055818.
Molecular dynamics simulations allow us to study the structure and dynamics of single biomolecules in microscopic detail. However, many processes occur on time scales beyond the reach of fully atomistic simulations and require coarse-grained multiscale models. While systematic approaches to construct such models have become available, these typically rely on microscopic dynamics that obey detailed balance. In vivo, however, biomolecules are constantly driven away from equilibrium in order to perform specific functions and thus break detailed balance. Here we introduce a method to construct Markov state models for systems that are driven through periodically changing one (or several) external parameter. We illustrate the method for alanine dipeptide, a widely used benchmark molecule for computational methods, exposed to a time-dependent electric field.
分子动力学模拟使我们能够在微观细节上研究单个生物分子的结构和动态。然而,许多过程发生的时间尺度超出了全原子模拟的范围,需要粗粒多尺度模型。虽然已经有了构建此类模型的系统方法,但这些方法通常依赖于服从详细平衡的微观动力学。然而,在体内,生物分子为了执行特定的功能而不断地被驱离平衡,从而打破了详细平衡。在这里,我们介绍了一种为通过周期性地改变一个(或几个)外部参数来驱动的系统构建马氏态模型的方法。我们以丙氨酸二肽为例来说明这种方法,丙氨酸二肽是一种广泛用于计算方法的基准分子,它暴露在随时间变化的电场中。