Blind De Novo Design of Dual Cyclic Peptide Agonists Targeting GCGR and GLP1R

Abstract

Agonists of the glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP1R) are key to treating metabolic diseases like type 2 diabetes and obesity, but often limited by being linear. Cyclic peptides offer greater stability and potential oral delivery, but their de novo design with computational methods as agonists is unproven. Here, we used the EvoBind AI platform to design cyclic peptide agonists for GCGR from sequence alone, without any structural templates or prior binding site information. EvoBind consistently identified the receptors activation surface, producing peptides that also activated GLP1R. Of three synthesised peptides, two showed potent dual activation of GCGR and GLP1R in cAMP assays, while all three activate GLP1R, with the 18-residue design achieving EC50 values of 32 nM (GLP1R) and 542 nM (GCGR), rivalling natural hormones. The designed sequences show no similarity to known agonists, and structural modelling indicates that they adopt novel binding modes compatible with active-state stabilisation. This is made possible by EvoBinds purely sequence-based, template-free approach, which enables the discovery of alternative solutions that are inaccessible to structure-guided methods. This study presents the first successful blind de novo design of a cyclic peptide agonist for a G protein-coupled receptor (GPCR), using only the receptor sequence as input. The findings highlight EvoBinds capacity to go beyond static binding, offering a generalisable strategy for therapeutic discovery through target-agnostic, sequence-driven peptide design.