Peptide Ligand Discovery of G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) represent the largest class of therapeutic targets, and peptides have become an increasingly important and versatile ligand type for studying receptor biology and developing new drugs. The development of novel GPCR therapies relies heavily on the discovery of effective peptide ligands that can selectively bind to specific GPCRs. Recent advances in peptide-focused approaches have substantially broadened the GPCR drug discovery toolbox, enabling researchers to design and synthesize peptides with improved specificity, affinity, and pharmacokinetic properties. These advancements include innovative discovery strategies, combinatorial library synthesis, computational design, and structural integration. Experimental workflows for peptide–GPCR interactions involve in silico peptide mining, deorphanization strategies, library-based screening platforms, modern pathway-resolved biosensor assays, and approaches for peptide stabilization and optimization. Computational methods, such as diffusion-based de novo design, molecular dynamics simulations, and free energy calculations, are also being employed to predict peptide–GPCR interactions and optimize ligand design. Artificial intelligence-guided or machine learning-guided screening frameworks connect peptide sequence space with receptor binding and functional signalling outcomes. Despite these advances, challenges persist, including reproducibility issues, ambiguity in sequence annotation and post-translational modifications, peptide instability, assay artefacts, and limitations of computational mining approaches. To address these challenges, practical strategies are proposed to improve the accuracy and reliability of peptide–GPCR interactions. Future perspectives emphasize integrated workflows that combine experimental pharmacology, structural biology, and computational modelling to accelerate the discovery of next-generation peptide probes and therapeutic ligands targeting GPCRs.