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bioRxiv · 10.1101/2020.08.07.242347

Ig-VAE: Generative Modeling of Immunoglobulin Proteins by Direct 3D Coordinate Generation

Eguchi, R. R.·Anand, N.·Choe, C. A.·Huang, P.-S.
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Abstract

AO_SCPLOWBSTRACTC_SCPLOWWhile 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 models 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.

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Eguchi, R. R., Anand, N., Choe, C. A., Huang, P.-S.. 2020-08-10. Ig-VAE: Generative Modeling of Immunoglobulin Proteins by Direct 3D Coordinate Generation. https://doi.org/10.1101/2020.08.07.242347

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