Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation.

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 with Rosetta 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 pro

SUBMITTER: Eguchi RR 

PROVIDER: S-EPMC9269947 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature