Computationally Evidence‐Grounded Sequence‐First Design of Peptide Binders

Abstract

ABSTRACT Peptide binders provide a versatile modality for modulating protein targets that are poorly addressed by small molecules, but their discovery is constrained by sample‐intensive screening or reliance on structural templates. Sequence‐first generation offers a scalable alternative for targets lacking stable or representative structures, yet existing approaches often sacrifice target‐specific control for diversity and are further limited by the imperfect transfer of protein language‐model priors to short peptides. Here, we report BOND‐PEP, an evidence‐grounded framework for sequence‐only peptide binder generation. BOND‐PEP retrieves target‐relevant peptide exemplars, aligns them with the query protein through bipartite message passing, and uses the resulting protein‐centric representation to guide conditional decoding. In a matched AlphaFold‐Multimer evaluation on a non‐homologous held‐out benchmark, BOND‐PEP improved reference‐beating ipTM success over RFdiffusion, PepPrCLIP and PepMLM. It further transferred to a compact external panel of targets with previously reported experimentally supported peptide binders. These results establish retrieval‐augmented, topology‐conditioned decoding as a practical route to controllable peptide binder design.