Improved De Novo Peptide Binder Design with Target-Conditioned Inverse Folding

Abstract

Abstract Inverse protein folding methods have become central to the computational design of de novo proteins, but existing models struggle when tasked with generating high-affinity peptide binders. By combining peptide-specific finetuning with a novel decoding order strategy, we enhance pocket conditioning and enable more accurate sequence design for peptide-binding interfaces. Our approach delivers gains in computational metrics, increasing sequence recovery and improving in silico binder design success rate by 16% ™ 30%. In vitro validation finds that our method greatly improves the success rate of designing novel peptide agonists of the OPRM1 receptor, generating at least twice as many top-ranking agonists as the prevailing standard method ProteinMPNN.