Fujisaki Hiroshi, Suetani Hiromichi, Maragliano Luca, Mitsutake Ayori
Department of Physics, Nippon Medical School, 1-7-1 Kyonan-cho, Musashino, Tokyo 180-0023, Japan.
AMED-CREST, Japan Agency for Medical Research and Development, 1-1-5 Sendagi, Bunkyo-ku, Tokyo 113-8603, Japan.
Life (Basel). 2022 Aug 3;12(8):1188. doi: 10.3390/life12081188.
We apply the non-Markov-type analysis of state-to-state transitions to nearly microsecond molecular dynamics (MD) simulation data at a folding temperature of a small artificial protein, chignolin, and we found that the time scales obtained are consistent with our previous result using the weighted ensemble simulations, which is a general path-sampling method to extract the kinetic properties of molecules. Previously, we also applied diffusion map (DM) analysis, which is one of a manifold of learning techniques, to the same trajectory of chignolin in order to cluster the conformational states and found that DM and relaxation mode analysis give similar results for the eigenvectors. In this paper, we divide the same trajectory into shorter pieces and further apply DM to such short-length trajectories to investigate how the obtained eigenvectors are useful to characterize the conformational change of chignolin.
我们将态到态跃迁的非马尔可夫型分析应用于小人工蛋白奇诺林在折叠温度下近微秒的分子动力学(MD)模拟数据,并且我们发现所获得的时间尺度与我们之前使用加权系综模拟的结果一致,加权系综模拟是一种用于提取分子动力学性质的通用路径采样方法。此前,我们还将扩散映射(DM)分析(它是多种学习技术之一)应用于奇诺林的相同轨迹,以便对构象状态进行聚类,并且发现DM和弛豫模式分析对于特征向量给出了相似的结果。在本文中,我们将相同轨迹划分为更短的片段,并进一步将DM应用于这些短长度轨迹,以研究所得特征向量如何用于表征奇诺林的构象变化。