Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning.

ABSTRACT: The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-delet

SUBMITTER: Clark T 

PROVIDER: S-EPMC10636138 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature