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US 2025/0378917 A1Published application
Biological structure tokenizer
OwnerSalvatore J. Candido, Roshan M. Rao +3inventors
InventorsSalvatore J. Candido, Roshan M. Rao, Zeming Lin, Alexander W. Rives, Thomas F. Hayes
Life of this patent
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Abstract
For a specific amino acid in a protein, physically neighboring amino acids of the specific amino acid are determined in a local physical protein structure. Representations of the determined physically neighboring amino acids are included in a structure encoder input for the specific amino acid. The structure encoder input is provided to an autoencoder trained using geometric loss to determine a token representing the local physical protein structure for the specific amino acid.
Claims20
A method, comprising:
- for a specific amino acid in a protein, determining physically neighboring amino acids of the specific amino acid based on physical distances with respect to the specific amino acid in a local physical protein structure;
- including representations of the determined physically neighboring amino acids in a structure encoder input for the specific amino acid;
- providing the structure encoder input to an autoencoder trained using geometric loss to determine a local structure token representing the local physical protein structure for the specific amino acid separate from an individual amino acid token for the specific amino acid;
- combining the local structure token in a second input track with the individual amino acid token in a first input track into a combined sequence data and using the combined sequence data to train a biological language reasoning machine learning model;
- predicting a protein property including by unmasking a masked token in an input token sequence of the biological language reasoning machine learning model;
- and
- causing physical synthesis of a physical protein having assembled physical amino acids and having the protein property predicted using the biological language reasoning machine learning model.
4 more elements
A system, comprising:
- one or more processors configured to:
- for a specific amino acid in a protein, determine physically neighboring amino acids of the specific amino acid based on physical distances with respect to the specific amino acid in a local physical protein structure;
- include representations of the determined physically neighboring amino acids in a structure encoder input for the specific amino acid;
- provide the structure encoder input to an autoencoder trained using geometric loss to determine a local structure token representing the local physical protein structure for the specific amino acid separate from an individual amino acid token for the specific amino acid;
- combine the local structure token in a second input track with the individual amino acid token in a first input track into a combined sequence data and use the combined sequence data to train a biological language reasoning machine learning model;
- predict a protein property including by being configured to unmask a masked token in an input token sequence of the biological language reasoning machine learning model;
- and
- cause physical synthesis of a physical protein having assembled physical amino acids and having the protein property predicted using the biological language reasoning machine learning model;
- and
- a memory coupled to at least one of the one or more processors and configured to provide instructions.
7 more elements
A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:
- for a specific amino acid in a protein, determining physically neighboring amino acids of the specific amino acid based on physical distances with respect to the specific amino acid in a local physical protein structure;
- including representations of the determined physically neighboring amino acids in a structure encoder input for the specific amino acid;
- providing the structure encoder input to an autoencoder trained using geometric loss to determine a local structure token representing the local physical protein structure for the specific amino acid separate from an individual amino acid token for the specific amino acid;
- combining the local structure token in a second input track with the individual amino acid token in a first input track into a combined sequence data and using the combined sequence data to train a biological language reasoning machine learning model;
- predicting a protein property including by unmasking a masked token in an input token sequence of the biological language reasoning machine learning model;
- and
- causing physical synthesis of a physical protein having assembled physical amino acids and having the protein property predicted using the biological language reasoning machine learning model.
4 more elements
Dependencies are inferred from the claim text.