Peptide design through binding interface mimicry with PepMimic

Peptides offer advantages for targeted therapy, including oral bioavailability, cellular permeability and high specificity, setting them apart from conventional small molecules and biologics. Here we develop an artificial intelligence algorithm, PepMimic, to transform a known receptor or an existing antibody of a target into a short peptide binder by mimicking the binding interfaces between targets and known binders. We apply PepMimic to drug targets PD-L1, CD38, BCMA, HER2 and CD4. Surface plasmon resonance imaging results show that 8% of the peptides exhibit dissociation constant (K_D) values at the 10^-8 M level, and 26 peptides achieving K_D values as low as 10^-9 M, substantially higher than random library screening conducted under identical conditions. We apply PepMimic to target proteins lacking available binders by first using existing algorithms to design protein binders, followed by designing peptide through simulating these artificial interfaces. We extensively validate the top-ranked peptides using tail vein injections in breast, myeloma and lung tumour mouse models. Experimental results demonstrate effective membrane binding and highlight their strong potential for clinical diagnostic imaging and targeted therapeutic applications.