Molecular simulation trajectories represent high-dimensional data. Such data can be visualized by methods of dimensionality reduction. Non-linear dimensionality reduction methods are likely to be more efficient than linear ones due to the fact that motions of atoms are non-linear. Here we test a popular non-linear t-distributed Stochastic Neighbor Embedding (t-SNE) method on analysis of trajectories of 200 ns alanine dipeptide dynamics and 208 μs Trp-cage folding and unfolding. Furthermore, we introduce a time-lagged variant of t-SNE in order to focus on rarely occurring transitions in the molecular system. This time-lagged t-SNE efficiently separates states according to distance in time. Using this method it is possible to visualize key states of studied systems (e.g., unfolded and folded protein) as well as possible kinetic traps using a two-dimensional plot. Time-lagged t-SNE is a visualization method and other applications, such as clustering and free energy modeling, must be done with caution.

分子模拟轨迹代表高维数据。这样的数据可以通过降维方法可视化。由于原子的运动是非线性的，非线性降维方法可能比线性方法更有效。在这里，我们测试了一种流行的非线性t分布随机邻居嵌入（t-SNE）方法，用于分析200 ns丙氨酸二肽动力学和208μsTrp笼折叠和展开的轨迹。此外，我们引入了t-SNE的时变变量，以关注分子系统中很少发生的跃迁。带有时间滞后的t-SNE根据时间间隔有效地分离状态。使用此方法可以可视化研究系统的关键状态（例如，展开和折叠的蛋白质）以及可能的动力学陷阱（使用二维图）。时滞t-SNE是一种可视化方法，必须谨慎执行其他应用程序，例如聚类和自由能建模。