Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.07.242347v1?rss=1 Authors: Eguchi, R. R., Anand, N., Choe, C. A., Huang, P.-S. Abstract: While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation -- an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model's generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model. Copy rights belong to original authors. Visit the link for more info