Figure 7.

Deep learning clusters T-REMD simulations of the NSP15 hexameric complex into conformational states that are potentially relevant for docking studies. (A) A 3D-representation of the CVAE learned from the T-REMD simulations shows the presence of multiple conformational states. Each conformation from the simulation is painted using the RMSD to the starting structure and shows the presence of distinct directions in the conformational landscape where low- and high-RMSD structures are distributed. To understand this representation better, we use an at-stochastic neighbor embedding (t-SNE) algorithm to embed the data into a low-dimensional space, where we can clearly visualize how the conformational landscape is organized. In this twodimensional space, we visualize various observables from the simulations, including (B) RMSD to the native structure, (C) SASA, and (D) radius of gyration. In each of these cases, we can observe the presence of at least three dominant sub-states with distinct structural characteristics, which can be further used for docking simulations.