Structural basis of μ-opioid receptor targeting by a nanobody antagonist.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
09 Oct 2024
09 Oct 2024
Historique:
received:
16
12
2023
accepted:
24
09
2024
medline:
10
10
2024
pubmed:
10
10
2024
entrez:
9
10
2024
Statut:
epublish
Résumé
The μ-opioid receptor (μOR), a prototypical G protein-coupled receptor (GPCR), is the target of opioid analgesics such as morphine and fentanyl. Due to the severe side effects of current opioid drugs, there is considerable interest in developing novel modulators of μOR function. Most GPCR ligands today are small molecules, however biologics, including antibodies and nanobodies, represent alternative therapeutics with clear advantages such as affinity and target selectivity. Here, we describe the nanobody NbE, which selectively binds to the μOR and acts as an antagonist. We functionally characterize NbE as an extracellular and genetically encoded μOR ligand and uncover the molecular basis for μOR antagonism by determining the cryo-EM structure of the NbE-μOR complex. NbE displays a unique ligand binding mode and achieves μOR selectivity by interactions with the orthosteric pocket and extracellular receptor loops. Based on a β-hairpin loop formed by NbE that deeply protrudes into the μOR, we design linear and cyclic peptide analogs that recapitulate NbE's antagonism. The work illustrates the potential of nanobodies to uniquely engage with GPCRs and describes lower molecular weight μOR ligands that can serve as a basis for therapeutic developments.
Identifiants
pubmed: 39384768
doi: 10.1038/s41467-024-52947-6
pii: 10.1038/s41467-024-52947-6
doi:
Substances chimiques
Receptors, Opioid, mu
0
Single-Domain Antibodies
0
Ligands
0
Analgesics, Opioid
0
Peptides, Cyclic
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8687Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : TMSGI3_211581
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : PCEFP3_181282
Organisme : Vrije Universiteit Brussel (Université Libre de Bruxelles)
ID : Strategic Research Program (SRP50)
Informations de copyright
© 2024. The Author(s).
Références
Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).
doi: 10.1038/nrd.2016.230
Sriram, K. & Insel, P. A. G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol. Pharmacol. 93, 251–258 (2018).
doi: 10.1124/mol.117.111062
Hauser, A. S., Attwood, M. M., Rask-Andersen, M., Schiöth, H. B. & Gloriam, D. E. Trends in GPCR drug discovery: new agents, targets and indications. Nat. Rev. Drug Discov. 16, 829–842 (2017).
doi: 10.1038/nrd.2017.178
Hutchings, C. J., Koglin, M., Olson, W. C. & Marshall, F. H. Opportunities for therapeutic antibodies directed at G-protein-coupled receptors. Nat. Rev. Drug Discov. 16, 661 (2017).
doi: 10.1038/nrd.2017.173
Laeremans, T. et al. Accelerating GPCR drug discovery with conformation-stabilizing VHHs. Front Mol. Biosci. 9, 863099 (2022).
doi: 10.3389/fmolb.2022.863099
Jovčevska, I. & Muyldermans, S. The therapeutic potential of nanobodies. BioDrugs 34, 11–26 (2020).
doi: 10.1007/s40259-019-00392-z
Manglik, A., Kobilka, B. K. & Steyaert, J. Nanobodies to study G protein-coupled receptor structure and function. Annu. Rev. Pharmacol. Toxicol. 57, 19–37 (2017).
doi: 10.1146/annurev-pharmtox-010716-104710
Heukers, R., De Groof, T. W. M. & Smit, M. J. Nanobodies detecting and modulating GPCRs outside in and inside out. Curr. Opin. Cell Biol. 57, 115–122 (2019).
doi: 10.1016/j.ceb.2019.01.003
Huang, W. et al. Structural insights into µ-opioid receptor activation. Nature 524, 315–321 (2015).
doi: 10.1038/nature14886
Rasmussen, S. G. F. et al. Structure of a nanobody-stabilized active state of the β(2) adrenoceptor. Nature 469, 175–180 (2011).
doi: 10.1038/nature09648
Robertson, M. J. et al. Structure determination of inactive-state GPCRs with a universal nanobody. Nat. Struct. Mol. Biol. 29, 1188–1195 (2022).
doi: 10.1038/s41594-022-00859-8
Kruse, A. C. et al. Activation and allosteric modulation of a muscarinic acetylcholine receptor. Nature 504, 101–106 (2013).
doi: 10.1038/nature12735
Irannejad, R. et al. Conformational biosensors reveal GPCR signalling from endosomes. Nature 495, 534–538 (2013).
doi: 10.1038/nature12000
Stoeber, M. et al. A genetically encoded biosensor reveals location bias of opioid drug action. Neuron 98, 963–976.e5 (2018).
doi: 10.1016/j.neuron.2018.04.021
McMahon, C. et al. Synthetic nanobodies as angiotensin receptor blockers. Proc. Natl Acad. Sci. 117, 20284–20291 (2020).
doi: 10.1073/pnas.2009029117
Scholler, P. et al. Allosteric nanobodies uncover a role of hippocampal mGlu2 receptor homodimers in contextual fear consolidation. Nat. Commun. 8, 1967 (2017).
doi: 10.1038/s41467-017-01489-1
Ma, Y. et al. Structure-guided discovery of a single-domain antibody agonist against human apelin receptor. Sci. Adv. 6, eaax7379 (2020).
doi: 10.1126/sciadv.aax7379
Wu, A. et al. Structural basis for the allosteric modulation of rhodopsin by nanobody binding to its extracellular domain. Nat. Commun. 14, 5209 (2023).
doi: 10.1038/s41467-023-40911-9
M. A. Skiba et al. Antibodies expand the scope of angiotensin receptor pharmacology. Nat. Chem. Biol. https://doi.org/10.1038/s41589-024-01620-6 (2024).
Schlimgen, R. R. et al. Structural basis for selectivity and antagonism in extracellular GPCR-nanobodies. Nat. Commun. 15, 4611 (2024).
doi: 10.1038/s41467-024-49000-x
Corder, G., Castro, D. C., Bruchas, M. R. & Scherrer, G. Endogenous and exogenous opioids in pain. Annu. Rev. Neurosci. 41, 453–473 (2018).
doi: 10.1146/annurev-neuro-080317-061522
Kieffer, B. L. & Evans, C. J. Opioid receptors: from binding sites to visible molecules in vivo. Neuropharmacology 56, 205–212 (2009).
doi: 10.1016/j.neuropharm.2008.07.033
Manglik, A. et al. Structure-based discovery of opioid analgesics with reduced side effects. Nature 537, 185–190 (2016).
doi: 10.1038/nature19112
Faouzi, A. et al. Structure-based design of bitopic ligands for the µ-opioid receptor. Nature 613, 767–774 (2023).
doi: 10.1038/s41586-022-05588-y
Wang, H. et al. Structure-based evolution of G protein-biased μ-opioid receptor agonists. Angew. Chem. Int. Ed. Engl. 61, e202200269 (2022).
doi: 10.1002/anie.202200269
Volkow, N. D. & Collins, F. S. The role of science in addressing the opioid crisis. N. Engl. J. Med. 377, 391–394 (2017).
doi: 10.1056/NEJMsr1706626
Manglik, A. et al. Crystal structure of the µ-opioid receptor bound to a morphinan antagonist. Nature 485, 321–326 (2012).
doi: 10.1038/nature10954
Zhuang, Y. et al. Molecular recognition of morphine and fentanyl by the human μ-opioid receptor. Cell 185, 4361–4375.e19 (2022).
doi: 10.1016/j.cell.2022.09.041
Koehl, A. et al. Structure of the µ-opioid receptor-Gi protein complex. Nature 558, 547–552 (2018).
doi: 10.1038/s41586-018-0219-7
Wang, Y. et al. Structures of the entire human opioid receptor family. Cell 186, 413–427.e17 (2023).
doi: 10.1016/j.cell.2022.12.026
J. S. Bloch et al. Development of a universal nanobody-binding Fab module for fiducial-assisted cryo-EM studies of membrane proteins. Proc. Natl. Acad. Sci. USA. 118 (2021).
J. A. Ballesteros, H. Weinstein, “Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors” in Methods in Neurosciences, S. C. Sealfon, (ed). 25, 366–428 (Academic Press, 1995).
Manglik, A. Molecular Basis of Opioid Action: From Structures to New Leads. Biol. Psychiatry 87, 6–14 (2020).
doi: 10.1016/j.biopsych.2019.08.028
Z. Li et al. Three-dimensional structural insights have revealed the distinct binding interactions of agonists, partial agonists, and antagonists with the µ opioid receptor. Int. J. Mol. Sci. 24, 7042 (2023).
Chen, C. et al. Determination of the amino acid residue involved in [3H]beta-funaltrexamine covalent binding in the cloned rat mu-opioid receptor. J. Biol. Chem. 271, 21422–21429 (1996).
doi: 10.1074/jbc.271.35.21422
Marie-Pepin, C., Yue, S. Y., Roberts, E., Wahlestedt, C. & Walker, P. Novel “restoration of function” mutagenesis strategy to identify amino acids of the δ-opioid receptor involved in ligand binding. J. Biol. Chem. 272, 9260–9267 (1997).
doi: 10.1074/jbc.272.14.9260
Granier, S. et al. Structure of the δ-opioid receptor bound to naltrindole. Nature 485, 400–404 (2012).
doi: 10.1038/nature11111
Van Holsbeeck, K., Martins, J. C. & Ballet, S. Downsizing antibodies: towards complementarity-determining region (CDR)-based peptide mimetics. Bioorg. Chem. 119, 105563 (2022).
doi: 10.1016/j.bioorg.2021.105563
Obrecht, D., Chevalier, E., Moehle, K. & Robinson, J. A. β-Hairpin protein epitope mimetic technology in drug discovery. Drug Discov. Today Technol. 9, e63–e49 (2012).
Van Holsbeeck, K. et al. Nanobody loop mimetics enhance son of sevenless 1-catalyzed nucleotide exchange on RAS. Angew. Chem. Int. Ed. Engl. 62, e202219095 (2023).
doi: 10.1002/anie.202219095
Martin, C. et al. Rational design of Nanobody80 loop peptidomimetics: Towards biased β2 adrenergic receptor ligands. Chemistry 23, 9632–9640 (2017).
Zamora, J. C. et al. Long-term antagonism and allosteric regulation of mu opioid receptors by the novel ligand, methocinnamox. Pharmacol. Res. Perspect. 9, e00887 (2021).
doi: 10.1002/prp2.887
Maguire, D. R. et al. Effects of acute and repeated treatment with methocinnamox, a mu opioid receptor antagonist, on fentanyl self-administration in rhesus monkeys. Neuropsychopharmacology 45, 1986–1993 (2020).
doi: 10.1038/s41386-020-0698-8
Gerak, L. R. et al. Methocinnamox produces long-lasting antagonism of the behavioral effects of µ-opioid receptor agonists but not prolonged precipitated withdrawal in rats. J. Pharmacol. Exp. Ther. 371, 507–516 (2019).
doi: 10.1124/jpet.119.260331
Toyoda, Y. et al. Structural basis of α1A-adrenergic receptor activation and recognition by an extracellular nanobody. Nat. Commun. 14, 1–13 (2023).
doi: 10.1038/s41467-023-39310-x
Hong, C. et al. Structures of active-state orexin receptor 2 rationalize peptide and small-molecule agonist recognition and receptor activation. Nat. Commun. 12, 815 (2021).
doi: 10.1038/s41467-021-21087-6
Dooley, C. T., Chung, N. N., Schiller, P. W. & Houghten, R. A. Acetalins: opioid receptor antagonists determined through the use of synthetic peptide combinatorial libraries. Proc. Natl Acad. Sci. Usa. 90, 10811–10815 (1993).
doi: 10.1073/pnas.90.22.10811
Schiller, P. W. et al. Conversion of δ-, κ- and μ-receptor selective opioid peptide agonists into δ-, κ- and μ-selective antagonists. Life Sci. 73, 691–698 (2003).
doi: 10.1016/S0024-3205(03)00389-8
Purington, L. C., Pogozheva, I. D., Traynor, J. R. & Mosberg, H. I. Pentapeptides displaying μ opioid receptor agonist and δ opioid receptor partial agonist/antagonist properties. J. Med. Chem. 52, 7724–7731 (2009).
doi: 10.1021/jm9007483
Laschet, C., Dupuis, N. & Hanson, J. A dynamic and screening-compatible nanoluciferase-based complementation assay enables profiling of individual GPCR–G protein interactions. J. Biol. Chem. 294, 4079–4090 (2019).
doi: 10.1074/jbc.RA118.006231
Radoux-Mergault, A., Oberhauser, L., Aureli, S., Gervasio, F. L. & Stoeber, M. Subcellular location defines GPCR signal transduction. Sci. Adv. 9, eadf6059 (2023).
doi: 10.1126/sciadv.adf6059
Götzke, H. et al. The ALFA-tag is a highly versatile tool for nanobody-based bioscience applications. Nat. Commun. 10, 4403 (2019).
doi: 10.1038/s41467-019-12301-7
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).
doi: 10.1038/nmeth.4169
J. Zivanov et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. Elife 7, e42166 (2018).
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
doi: 10.1038/nmeth.4193
Sanchez-Garcia, R. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun. Biol. 4, 874 (2021).
doi: 10.1038/s42003-021-02399-1
Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM density maps. Nat. Methods 11, 63–65 (2014).
doi: 10.1038/nmeth.2727
Meng, E. C. et al. UCSF ChimeraX: Tools for structure building and analysis. Protein Sci. 32, e4792 (2023).
doi: 10.1002/pro.4792
Casañal, A., Lohkamp, B. & Emsley, P. Current developments in Coot for macromolecular model building of electron cryo-microscopy and crystallographic data. Protein Sci. 29, 1069–1078 (2020).
doi: 10.1002/pro.3791
Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr D. Struct. Biol. 75, 861–877 (2019).
doi: 10.1107/S2059798319011471
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).
doi: 10.1002/pro.3330
Kabsch, W. XDS. Acta Crystallogr. D. Biol. Crystallogr. 66, 125–132 (2010).
doi: 10.1107/S0907444909047337
Agirre, J. et al. The CCP4 suite: integrative software for macromolecular crystallography. Acta Crystallogr D. Struct. Biol. 79, 449–461 (2023).
doi: 10.1107/S2059798323003595
McCoy, A. J. Solving structures of protein complexes by molecular replacement with phaser. Acta Crystallogr. D. Biol. Crystallogr. 63, 32–41 (2007).
doi: 10.1107/S0907444906045975
Schwede, T., Kopp, J., Guex, N. & Peitsch, M. C. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31, 3381–3385 (2003).
doi: 10.1093/nar/gkg520
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D. Biol. Crystallogr. 60, 2126–2132 (2004).
doi: 10.1107/S0907444904019158