Fig. 15
From: A beginner’s approach to deep learning applied to VS and MD techniques
Overview of the DL-RP-MDS method developed by Tam et al. [183]. To measure the impact of missense variations on protein function, an AE architecture was built and trained. It takes as input the Ramachandran plots of conformations of the query protein with a missense variation of interest, generated using MD simulations. Through its reconstruction of the input via its encoder and decoder layers, it learns a low-dimensional latent representation of the Ramachandran plot input data. This latent space forms the input of a DFCNN classifier that predicts the variants of the protein to either be deleterious or undefined (i.e., benign)