Ibio Limited

Kevin Eduard Hauser

Matthew Paul Greving

Mohan Srinivasan

Angeles Estelles

Phung Tu Gip

Alexander T Taguchi

Cody A Moore

Alex T Taguchi

Christian Barrett

Andrew Morin

Jordan R Willis

Provided herein engineered polypeptides that comprise a combination of spatially-associated topological constraints, wherein at least one constraint is derived from a CD25 reference target, and methods of selecting said engineered polypeptides. Further provided are methods of using the engineered polypeptides, including as positive and/or negative selection molecules in methods of screening a library of binding molecules such as antibodies. Further provided herein are CD25 antibodies selected using these engineered polypeptides.

Engineered CD25 polypeptides and uses thereof

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.

Machine learning method for protein modelling to design engineered peptides

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Jupiter, FL, United States of America

Kevin Eduard Hauser