Contrastive Learning Enables Epitope Overlap Predictions for Targeted Antibody Discovery.

ABSTRACT: Computational epitope prediction remains an unmet need for therapeutic antibody development. We present three complementary approaches for predicting epitope relationships from antibody amino acid sequences. First, we analyze ~18 million antibody pairs targeting ~250 protein families and establish that a threshold of >70% CDRH3 sequence identity among antibodies sharing both heavy and light chain V-genes reliably predicts overlapping-epitope antibody pairs. Next, we develop a supervised contrastive fine-tuning framework for antibody large language models which results in embeddings that better correlate with epitope information than those from pretrained models. Applying this contrastive learning approach to SARS-CoV-2 receptor binding domain antibodies, we achieve 82.7% balanced accuracy

SUBMITTER: Holt CM 

PROVIDER: S-EPMC11888244 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature