De novo designed cyclic MC4R peptide agonist reduces food intake in mice
Deep learning-based structure prediction enables the design of peptide ligands without relying on naturally occurring scaffolds. However, most computationally generated peptides are not advanced beyond initial activity measurements, leaving the path to drug-like optimization and in vivo validation underexplored. Here we establish an end-to-end workflow for de novo peptide agonist discovery and maturation using the melanocortin-4 receptor (MC4R) as a model target. Using an AlphaFold2-based hallucination protocol implemented in ColabDesign, we generated more than 5,000 linear and head-to-tail cyclic candidate peptides directed towards the MC4R orthosteric pocket. Functional screening of a prioritized subset revealed measurable activity in 74% of linear peptides and 23% of cyclic peptides, from which we identified a cyclic agonist with an EC50 of 340 nM despite lacking the canonical melanocortin activation motif. We then performed systematic in vitro maturation by deep mutational scanning, half-life extender conjugation scanning, and a combinatorial optimization library, coupled with data-driven analysis to map sequence-activity relationships. These experiments identified an alternative activation motif centered on an APWR segment and yielded single-site variants with substantially improved potency. The most effective substitution, a proline at position 5, produced the E5P variant with an EC50 of 6.7 nM against the human melanocortin-4 receptor (hMC4R). Finally, central administration of E5P (10 nmol) reduced acute food intake in mice, providing in vivo proof of concept. Together, our results demonstrate a generalizable design-to-validation strategy for converting de novo peptide designs into optimized, pharmacologically active peptides, and expand the space of MC4R agonist chemotypes beyond endogenous melanocortins.