RareFoldGPCR: Agonist Design Beyond Natural Amino Acids

Abstract

Noncanonical amino acids (NCAAs) expand the chemical diversity of peptides beyond the twenty standard residues, offering new opportunities for designing binders with novel interaction modes and functional activity. G protein-coupled receptors (GPCRs) are central to cellular signalling and represent one of the largest classes of therapeutic targets, yet their functional modulation remains challenging. Here, we present RareFoldGPCR (RFG), a GPCR-specialised AI model for structure prediction and design that supports NCAAs. By applying transfer learning on high-resolution GPCR structures, RFG accurately models and rationally designs both linear and cyclic peptides that incorporate NCAAs and modulate GPCR activity. This is achieved without the model ever being trained on NCAA-based GPCR modulators. We showcase the capability of RFG by designing peptide agonists for the glucagon-like peptide-1 receptor (GLP1R) and validating their functional activity experimentally in cell-based assays. We investigate the precise capabilities of generating active agonists by expanding different regions of the native GLP-1 hormone, and further demonstrate the design of cyclic peptide agonists with entirely novel sequences and topologies, creating new agonist modes. We analyse how design metrics relate to pathway specificity, enabling precise modulation of pathway activity, such as activating the cAMP response without recruiting {beta}-arrestin to reduce receptor desensitisation. RFG shows how transfer learning on specific target classes enables generalisation to new chemistry and molecular topology, providing a broadly applicable strategy for designing functional ligands beyond the constraints of natural amino acid chemistry. RFG is freely available: https://github.com/patrickbryant1/RareFoldGPCR