Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation.
PLoS Comput Biol
; 18(6): e1010271, 2022 06.
Article
in English
| MEDLINE | ID: covidwho-1910466
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 protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
RNA, Viral
/
COVID-19
Limits:
Humans
Language:
English
Journal:
PLoS Comput Biol
Journal subject:
Biology
/
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
Journal.pcbi.1010271
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