PREDICTING PROTEIN AMINO ACID SEQUENCES USING GENERATIVE MODELS CONDITIONED ON PROTEIN STRUCTURE EMBEDDINGS
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United States of America Patent
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Issued Date -
Apr 11, 2024
app pub date -
Jan 27, 2022
filing date -
Feb 5, 2021
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Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing protein design. In one aspect, a method comprises: processing an input characterizing a target protein structure of a target protein using an embedding neural network having a plurality of embedding neural network parameters to generate an embedding of the target protein structure of the target protein; determining a predicted amino acid sequence of the target protein based on the embedding of the target protein structure, comprising: conditioning a generative neural network having a plurality of generative neural network parameters on the embedding of the target protein structure; and generating, by the generative neural network conditioned on the embedding of the target protein structure, a representation of the predicted amino acid sequence of the target protein.
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