Scholarlink

<p>G&#xa0;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. Although many core pharmacological concepts apply to all ligand classes, recent advances in peptide-focused approaches, from innovative discovery strategies and combinatorial library synthesis to computational design and structural integration, have substantially broadened the GPCR drug discovery toolbox. In this Primer, we outline 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. We summarize recent progress in structural modelling, diffusion-based de novo design, molecular dynamics simulations, and free energy calculations, as well as artificial intelligence-guided or machine learning-guided screening frameworks that connect peptide sequence space with receptor binding and functional signalling outcomes. We also discuss key challenges in the field, including reproducibility issues, ambiguity in sequence annotation and post-translational modifications, peptide instability, assay artefacts and current limitations of computational mining approaches, and we propose practical strategies to address them. Finally, we outline future perspectives, emphasizing 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Peptide ligand discovery of G protein-coupled receptors

  • 期刊论文

  • 发表日期：

    2026年7月16日

查看全文
  • Julius Hermes,
  • Marin Matic,
  • Ho Yan Yeung,
  • Jiang Qianru,
  • Lauri Urvas,
  • Elizaveta Korchevaya,
  • Tomasz Stepniewski,
  • Alexandre Murza,
  • Jana Selent,
  • Michael Freissmuth,
  • Christian Heinis,
  • Pierre-Luc Boudreault,
  • Jens Carlsson,
  • Tao Che,
  • Helena Safavi-Hemami,
  • Alexander S. Hauser,
  • Christian W. Gruber

摘要

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. Although many core pharmacological concepts apply to all ligand classes, recent advances in peptide-focused approaches, from innovative discovery strategies and combinatorial library synthesis to computational design and structural integration, have substantially broadened the GPCR drug discovery toolbox. In this Primer, we outline 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. We summarize recent progress in structural modelling, diffusion-based de novo design, molecular dynamics simulations, and free energy calculations, as well as artificial intelligence-guided or machine learning-guided screening frameworks that connect peptide sequence space with receptor binding and functional signalling outcomes. We also discuss key challenges in the field, including reproducibility issues, ambiguity in sequence annotation and post-translational modifications, peptide instability, assay artefacts and current limitations of computational mining approaches, and we propose practical strategies to address them. Finally, we outline future perspectives, emphasizing 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.