Machine learning method for protein modelling to design engineered peptides
US11545238B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 1, 2020 |
| Grant date | Jan 3, 2023 |
| Priority date | — |
| Expiry date | Dec 1, 2040 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02A90/10
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Provided herein are methods for design of engineered polypeptides that recapitulate molecular structure features of a predetermined portion of a reference protein structure, e.g., an antibody epitope or a protein binding site. A Machine Learning (ML) model is trained by labeling blueprint records generated from a reference target structure with scores calculated based on computational protein modeling of polypeptide structures generated by the blueprint records. The method may include training an ML model based on a first set of blueprint records, or representations thereof, and a first set of scores, each blueprint record from the first set of blueprint records associated with each score from the first set of scores. After the training, the machine learning model may be executed to generate a second set of blueprint records. A set of engineered polypeptides are then generated based on the second set of blueprint records.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.