Fig. 8

Overview of the MD/DL iterative workflow developed by Ma et al. for protein folding problems, employing a CVAE architecture [139]. MD simulations are run in parallel for a protein of interest. The conformations generated throughout these simulations are fed to the CVAE as flattened Cartesian coordinates. After learning, the latent space is a low-dimensional representation of the conformational space of the protein, with regions defined by specific latent features/characteristics. This can be used to sample conformations with certain latent features (e.g., the RMSD of a conformation compared to the folded native state/unfolded starting state). Based on these samples, specific simulation runs can be terminated, and new runs can be started from the sampled conformations, in order to speed up the protein folding process