Mimic antibodies: leveraging ligand mimicry for epitope-targeted antibody discovery.

Antibodies are renowned for their ability to bind diverse targets with high affinity and specificity, yet identifying binders with predefined epitope specificity remains a major challenge. In this study, we investigate the concept of mimic antibodies-antibodies that recapitulate the binding mode of a target's cognate ligand. Through a systematic analysis of the Protein Data Bank (PDB), we show that such mimicry is widespread and arises through diverse structural mechanisms, such as single-loop, multi-loop and scattered interaction mimicry. These findings indicate that protein interfaces impose strong constraints on binding, leading to convergent interaction solutions that can be independently discovered by antibodies. Building on these findings, we developed a ligand-guided strategy to mine immune repertoire data by selecting antibodies whose predicted binding interfaces mimic the interaction motif of a cognate ligand. Applied to the interaction between interleukin-18 (IL-18) and its receptor alpha (IL-18RA), mimicry-guided screening of a 20,000-sequence repertoire yielded 31 candidates, 11 of which (35% hit rate) bound the IL-18RA D3 domain, with eight reaching sub-nanomolar affinities that surpass the cognate ligand. Our findings establish mimic antibodies as a promising strategy for rational antibody selection, engineering, and design, with broad implications for therapeutic antibody development and drug discovery.