Rettie, S.; Juergens, D.; Adebomi, V.; Bueso, Y. F.; Zhao, Q.; Leveille, A.; Liu, A.; Bera, A.; Wilms, J.; Üffing, A.; Kang, A.; Brackenbrough, E.; Lamb, M.; Gerben, S.; Murray, A.; Levine, P.; Schneider, M.; Vasireddy, V.; Ovchinnikov, S.; Weiergräber, O.; Willbold, D.; Kritzer, J.; Mougous, J.; Baker, D.; DiMaio, F.; Bhardwaj, G.

The development of macrocyclic binders to therapeutic proteins has typically relied on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest. We test 20 or fewer designed macrocycles against each of four diverse proteins and obtain medium to high-affinity binders against all selected targets. Designs against MCL1 and MDM2 demonstrate KD between 1-10 uM, and the best anti-GABARAP macrocycle binds with a KD of 6 nM and a sub-nanomolar IC50 in vitro. For one of the targets, RbtA, we obtain a high-affinity binder with KD < 10 nM despite starting from the target sequence alone due to the lack of an experimentally determined target structure. X-ray structures determined for macrocycle-bound MCL1, GABARAP, and RbtA complexes match very closely with the computational design models (RMSD < 2 [A]). In contrast to library screening approaches for which determining binding mode can be a major bottleneck, the binding modes of RFpeptides-generated macrocycles are known by design, which should greatly facilitate downstream optimization. RFpeptides thus provides a powerful framework for rapid and custom design of macrocyclic peptides for diagnostic and therapeutic applications.