This Primer outlines experimental and computational workflows tailored to peptide–GPCR interactions, including in silico peptide mining, deorphanization strategies, library-based screening platforms, modern pathway-resolved biosensor assays, and approaches for peptide stabilization and optimization strategies to address their pharmacokinetic limitations.
Peptide‐responsive G protein‐coupled receptors (GPCRs) often recognize endogenous peptide ligands through extended receptor interfaces, providing opportunities for peptide‐based ligands to engage receptor contacts that conventional small molecules struggle to access effectively. In this context, cyclic peptides are more than stabilized peptide analogs: their constrained topologies can preorganize key pharmacophoric elements, support engagement with extended orthosteric or allosteric receptor interfaces, and allow precise tuning of selectivity and signaling output. In this review, we discuss cyclic peptides targeting peptide‐responsive GPCRs through three connected dimensions: natural macrocyclic ligands and scaffolds, chemical strategies for topological and functional optimization, and emerging discovery platforms for GPCR‐active macrocycles. Specifically, we examine how endogenous cyclic peptides, venom‐derived peptides, plant cyclotides, and microbial macrocycles provide structurally defined templates for probing GPCR recognition, and how engineering approaches, including bridge replacement, conformational locking, residue modification, and half‐life extension, have expanded their utility as both bioactive ligands and molecular probes. We further highlight discovery technologies (e.g., mRNA display, phage display, cell‐based screening, and structure‐ and computation‐guided design) that are advancing the discovery and optimization of macrocycles with improved receptor specificity and pharmacological properties. Together, these advances position cyclic peptides as a topology‐guided molecular strategy for dissecting peptide–GPCR recognition and developing next‐generation therapeutics.
Yingxin Zhou, Ni Li, Jishen Zheng· ChemBioChem· 0 citations
Peptide-responsive G protein-coupled receptors (GPCRs) play pivotal roles in a wide variety of physiological regulatory systems in animals. Despite the substantial expansion of the GPCR and endogenous peptide repertoire identified through large-scale genomic and peptidomic studies, a significant number of receptors remain orphan, particularly those for non-homologous or species-specific peptides. Conventional deorphanization approaches based on sequence or ligand similarity, or receptor structure, are frequently ineffective. These current shortcomings in elucidation of peptide-GPCR interactions result in a persistent gap between sequence information and functional characterization. This review consolidates recent advances in computational approaches that address this challenge by focusing on peptide-GPCR pairs rather than on sequences or structures of peptides and receptors. Pair-centric machine learning frameworks, originally developed in the field of chemical genomics, enable systematic prediction of peptide-GPCR interactions without sequence similarity. Our originally developed machine learning system, PD-incorporated SVM, which incorporates peptide-specific descriptors into the frameworks, has enabled the experimentally validated identification of novel peptide-GPCR pairs in a tunicate and a nematode, and has predicted GPCRs for lineage-specific neuropeptides in ctenophore. In addition to interaction prediction, the present study explores the interaction determinant likelihood (IDL) framework, which offers a mechanistic interpretation of predicted interactions. IDL extracts residue-level determinants of ligand recognition directly from trained models, thereby enabling the identification of peptide features and receptor residues that collectively govern interaction specificity. The application of this approach to closely related GPCR paralogs demonstrates the emergence of distinct peptide specificities through limited, context-dependent residue substitutions, without significant alterations in overall receptor structure. Together, these findings highlight interaction determinants as a unifying framework linking deorphanization, molecular mechanism, and evolutionary diversification of peptide-GPCR signaling.
A. Shiraishi, H. Satake· General and Comparative Endo...· 0 citations
Peptide-activated G protein-coupled receptors (GPCRs) play crucial roles in numerous diseases, but remain difficult therapeutic targets due to the challenges in developing small-molecule drugs. Here, we explore structure-based strategies to identify small-molecule agonists of neurotensin (NTS) receptors, which hold promise for developing non-opioid analgesics. Chemical libraries of drug-like molecules are first designed based on a receptor-peptide complex, and then 14.5 million compounds are computationally docked to the orthosteric binding site of the NTS1 receptor. A set of 39 top-ranked compounds is synthesized, and seven of these are experimentally confirmed to activate the NTS1 receptor. Structure-guided optimization yields NTS1 ligands with signaling signatures distinct from the endogenous peptide, and these compounds also exhibit high affinity for the NTS2 receptor. High-resolution crystal structures of two agonists bound to the NTS1 receptor confirm predicted binding modes and reveal key determinants of activation. In vivo, the compounds produce robust antinociception in rodents without inducing hypotension, consistent with a contribution of NTS2 receptor activity. To facilitate broader application of our virtual screening approach to peptide-binding GPCRs, we provide access to tailored chemical libraries containing billions of readily synthesizable compounds. In this work, small-molecule ligands of neurotensin receptors were identified using structure-based virtual screening, leading to the discovery of potent agonists with in vivo antinociceptive effects and to insights into the molecular basis of receptor activation.
Nicolas Panel, D. D. Vo, H. Hübner et al.· Nature Communications· 0 citations
GaMD ensemble docking improved early AM enrichment across all four targets under at least one program, and the Boltz-2 deep-learning program showed minimal sensitivity to GaMD templates and underperformed conventional docking, suggesting its affinity predictions complement rather than replace physics- and empirical-based docking approaches for GPCR AM screening.
T.D. Thompson, Yinglong Miao· bioRxiv· 0 citations
This study provides potential lead compounds for the design of small-molecule allosteric drugs targeting class B1 GPCRs and performs conformational sampling and combined dynamic pocket detection algorithms, MDpocket and FTMove, to identify six characteristic cryptic pockets within the dynamic trajectories.
Zhi Dong, Long Cheng, Qingxin Shi et al.· International Journal of Bio...· 0 citations
This review provides an updated overview of cyclic peptides as modulators of PPIs, outlining current opportunities, methodological advances, and remaining challenges in the development of cyclic peptide-based PPI modulators.
Sara Salvi, P. Linciano, S. Collina et al.· International Journal of Mol...· 0 citations
