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- W4283588088 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." @default.
- W4283588088 created "2022-06-28" @default.
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- W4283588088 date "2022-06-27" @default.
- W4283588088 modified "2023-10-17" @default.
- W4283588088 title "Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation" @default.
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- W4283588088 doi "https://doi.org/10.1371/journal.pcbi.1010271" @default.
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