Exploring the latent space of deep generative models: Applications to G-protein coupled receptors

Deep generative models have shown promising results in protein sequence modeling given their ability to learn distribution over complex high-dimensional spaces. However, tools for analyzing the rich representations they are learning remain limited. We present a methodology for analyzing the latent representations of such models, and show how this analysis can be used to make predictions about ligand interactions and downstream signalling for a clinically important and functionally diverse family of membrane proteins, the G-protein coupled receptors.