- 专利标题: MACHINE LEARNING METHOD FOR PROTEIN MODELLING TO DESIGN ENGINEERED PEPTIDES
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申请号: US17961942申请日: 2022-10-07
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公开(公告)号: US20230095685A1公开(公告)日: 2023-03-30
- 发明人: Matthew P. Greving , Alexander T. Taguchi , Kevin E. Hauser
- 申请人: iBio, Inc.
- 申请人地址: US TX Bryan
- 专利权人: iBio, Inc.
- 当前专利权人: iBio, Inc.
- 当前专利权人地址: US TX Bryan
- 主分类号: G16B40/20
- IPC分类号: G16B40/20 ; G16B5/00 ; G16B5/30 ; G06N20/00 ; C07K14/00 ; G06N5/04
摘要:
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.
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