PepMimic
v1.0.0Peptide binder design through binding interface mimicry with a latent diffusion model
Peptide binder design through binding interface mimicry with a latent diffusion model
PepMimic designs short peptides that mimic the binding interface of a known protein binder (such as an antibody, nanobody, or natural receptor) on its target protein. A latent diffusion model co-designs each peptide's sequence and all-atom structure to reproduce the reference interface, enabling high-affinity target binding in a small peptide format.
The method is validated across multiple drug targets (PD-L1, CD38, HER2, BCMA, CD4, TROP2) in Kong et al., Nature Biomedical Engineering (2025).
Use PepMimic when you already have at least one structure of your target bound to a binder (an antibody, nanobody, receptor, or any protein partner), and you want to convert that binding interface into a short therapeutic-style peptide.
If you only have an apo target structure, design a binder first (for example with RFdiffusion) and then run PepMimic on the resulting complex.
A,B).B,C for an antibody heavy + light chain).Each generated candidate is returned as a complete protein–peptide complex PDB (the target receptor chains plus the designed peptide), together with:
Candidates are ranked by interface hits (descending), matching the source's native interface_hit.txt sort order.
PepMimic runs through its documented generation entry point (mimic_design.py) and scores candidates with its documented interface-mimicry scorer (evaluation.runner.interface_hit). No scientific logic is reimplemented.
The source's full selection pipeline additionally runs Rosetta and FoldX interface free-energy scoring and a final top-K selection. Those steps require proprietary academic licenses (PyRosetta via Salilab, FoldX via the CRG license with manual yearly renewal) and are therefore not part of the hosted run. Use the returned interface-hit ranking and perplexity to narrow candidates, and run licensed scoring locally if needed for final synthesis decisions.
If you use PepMimic results in published work, please cite the source paper.
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