Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.

ABSTRACT: Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuou

SUBMITTER: Makowski EK 

PROVIDER: S-EPMC9249733 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature