Abstract：
Over the past decade, Markov State Models (MSM) have emerged as powerful methodologies to build discrete models of dynamics over structures obtained from Molecular Dynamics trajectories. The identification of macrostates for the MSM is a central decision that impacts the quality of the MSM but depends on both the selected representation of a structure and the clustering algorithm utilized over the featurized structures. Motivated by a large molecular system in its free and bound state, this paper investigates two directions of research, further reducing the representation dimensionality in a non-parametric, data-driven manner and including more structures in the computation. Rigorous evaluation of the quality of obtained MSMs via various statistical tests in a comparative setting firmly shows that fewer dimensions and more structures result in a better MSM. Many interesting findings emerge from the best MSM, advancing our understanding of the relationship between antibody dynamics and antibody-antigen recognition.
摘要：
在过去的十年里，马尔可夫状态模型（MSM）已经成为强大的方法，可以在从分子动力学轨迹获得的结构上建立动力学的离散模型。MSM的宏观状态的识别是影响MSM的质量的中心决策,但取决于所选择的结构表示和在特征化结构上利用的聚类算法两者。受到处于自由和束缚状态的大分子系统的激励，本文研究了两个研究方向，进一步降低非参数中的表示维数，数据驱动的方式，并在计算中包含更多的结构。在比较设置中通过各种统计测试对获得的MSM的质量进行严格评估，这牢固地表明，更少的尺寸和更多的结构会导致更好的MSM。许多有趣的发现来自最好的MSM，提高我们对抗体动力学和抗体-抗原识别之间关系的理解。
