Towards learning transferable embeddings for protein conformations using Variational Autoencoders

Abstract

The analysis of the transitions that a protein conformation can undergo is an important step towards achieving a thorough understanding of protein functions. In this context, learning informative and transferable low-dimensional representations of proteins conformations is of great importance for developing robust computational approaches that can model proteins dynamical systems. In this paper we perform an experimental evaluation of the ability of the latent space representations learned by a Variational Autoencoder (VAE) trained on trajectories of proteins belonging to a superfamily to characterize an unseen protein from the same superfamily. In our study, we compare the latent space encodings obtained using two representations for the protein conformations and evaluate the impact of the latent space dimensionality on the embeddings’ quality. The obtained results outline that the VAE’s latent space can capture structural information characterizing a certain protein superfamily and, consequently, generalize to a different testing protein from that superfamily.