Hallucinating structure-conditioned antibody libraries for target-specific binders.

ABSTRACT: Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the F_vHallucinator for designing antibody sequen

SUBMITTER: Mahajan SP 

PROVIDER: S-EPMC9635398 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature