Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories

Vojtěch Spiwok; Pavel Kříž

doi:10.3389/fmolb.2020.00132

Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories

Front Mol Biosci. 2020 Jun 30:7:132. doi: 10.3389/fmolb.2020.00132. eCollection 2020.

Authors

Vojtěch Spiwok¹, Pavel Kříž²

Affiliations

¹ Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czechia.
² Department of Mathematics, University of Chemistry and Technology, Prague, Czechia.

Abstract

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.

Keywords: Time-lagged Independent Component Analysis; dimensionality reduction; molecular dynamics; tSNE; trajectory analysis.