A One-shot Framework for Directed Evolution of Antibodies

Abstract

Improving antibody binding to an antigen without antibody-antigen structure information or antigen-specific data remains a critical challenge in therapeutic protein design. In this work, we propose \textbf{\textsc{AffinityEnhancer}}, a framework to improve the affinity of an antibody in a one-shot setting. In the \emph{one‐shot} setting, we start from a single lead sequence—never fine‐tuning on it or using its structure in complex with the antigen or epitope/paratope information—and seek variants that reliably boost affinity. During training, \textsc{AffinityEnhancer} utilizes pairs of related sequences with higher versus lower measured binding in a pan-antigen dataset comprising diverse “environments” (antigens) and a shared structure-aware module that learns to transform low‐affinity sequences into high‐affinity ones, effectively distilling consistent, causal features that drive binding. By incorporating pretrained sequence-structure embeddings and a sequence decoder, our method enables robust generalization to entirely new antibody seeds. Across multiple unseen internal and public seeds, \textsc{AffinityEnhancer} identifies key affinity enhancing mutations on the paratope, outperforms existing structure‐conditioned and inpainting approaches, achieving substantial (in silico) affinity gains in true, one‐shot experiments without ever seeing antigen data.