Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
Engineering Research Center of Cell & Therapeutic Antibody School of Pharmacy, Shanghai Jiaotong University, Shanghai 200240, China.
Biomolecules. 2022 Jul 21;12(7):1011. doi: 10.3390/biom12071011.
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.
在过去的十年中，马尔可夫状态模型（MSM）已经成为构建从分子动力学轨迹中获得的结构上的动力学离散模型的强大方法。MSM 的宏观状态的识别是一个关键决策，它会影响 MSM 的质量，但它既取决于所选择的结构表示，也取决于在特征化结构上使用的聚类算法。受自由和结合状态下的大分子系统的启发，本文研究了两个研究方向，以非参数、数据驱动的方式进一步降低表示的维数，并在计算中包含更多的结构。通过比较环境中的各种统计测试对所获得的 MSM 进行严格评估，结果明确表明，较少的维度和更多的结构可以产生更好的 MSM。从最佳 MSM 中出现了许多有趣的发现，这些发现推进了我们对抗体动力学与抗体-抗原识别之间关系的理解。
