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Structure-based design of bitopic ligands for the µ-opioid receptor

Nature volume 613, pages 767–774 (2023)Cite this article

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Abstract

Mu-opioid receptor (µOR) agonists such as fentanyl have long been used for pain management, but are considered a major public health concern owing to their adverse side effects, including lethal overdose1. Here, in an effort to design safer therapeutic agents, we report an approach targeting a conserved sodium ion-binding site2 found in µOR3 and many other class A G-protein-coupled receptors with bitopic fentanyl derivatives that are functionalized via a linker with a positively charged guanidino group. Cryo-electron microscopy structures of the most potent bitopic ligands in complex with µOR highlight the key interactions between the guanidine of the ligands and the key Asp2.50 residue in the Na+ site. Two bitopics (C5 and C6 guano) maintain nanomolar potency and high efficacy at Gi subtypes and show strongly reduced arrestin recruitment—one (C6 guano) also shows the lowest Gz efficacy among the panel of µOR agonists, including partial and biased morphinan and fentanyl analogues. In mice, C6 guano displayed µOR-dependent antinociception with attenuated adverse effects, supporting the µOR sodium ion-binding site as a potential target for the design of safer analgesics. In general, our study suggests that bitopic ligands that engage the sodium ion-binding pocket in class A G-protein-coupled receptors can be designed to control their efficacy and functional selectivity profiles for Gi, Go and Gz subtypes and arrestins, thus modulating their in vivo pharmacology.

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Fig. 1: Targeting the Na+ site with fentanyl-based bitopic ligands and characterization of lead compounds in binding, G-protein and arrestin signalling assays.
Fig. 2: Structures of bitopic ligands bound to µOR.
Fig. 3: Profiling of C5 guano, C6 guano and µOR using TRUPATH Gαβγ biosensors and β-arrestin-1 and β-arrestin-2 efficacy.
Fig. 4: C6 guano exhibits µOR-mediated antinociception without CPP, CPA or hyperlocomotor effects.

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Data availability

The cryo-EM maps and corresponding coordinates have been deposited in the Electron Microscopy Data Bank (EMDB) under accession codes EMD-26314 (C5 guano–μOR–Gi–scFv16) and EMD-26313 (C6 guano–μOR–Gi) and the Protein Data Bank (PDB) under accession codes 7U2L (C5 guano–μOR–Gi–scFv16) and 7U2K (C6 guano–μOR–Gi). The authors declare that all the data supporting the findings of this study are available within the article, extended data and supplementary information files. All compounds can be made available on reasonable requests from the authors. Source data are provided with this paper.

References

  1. DeWeerdt, S. Tracing the US opioid crisis to its roots. Nature 573, S10–S12 (2019).

    Article  ADS  CAS  Google Scholar 

  2. Zarzycka, B., Zaidi, S. A., Roth, B. L. & Katritch, V. Harnessing ion-binding sites for GPCR pharmacology. Pharmacol. Rev. 71, 571–595 (2019).

    Article  CAS  Google Scholar 

  3. Huang, W. et al. Structural insights into μ-opioid receptor activation. Nature 524, 315–321 (2015).

    Article  ADS  CAS  Google Scholar 

  4. Hilger, D., Masureel, M. & Kobilka, B. K. Structure and dynamics of GPCR signaling complexes. Nat. Struct. Mol. Biol. 25, 4–12 (2018).

    Article  CAS  Google Scholar 

  5. Pasternak, G. W. & Pan, Y.-X. Mu opioids and their receptors: evolution of a concept. Pharmacol. Rev. 65, 1257–317 (2013).

    Article  CAS  Google Scholar 

  6. Varga, B. R., Streicher, J. M. & Majumdar, S. Strategies towards safer opioid analgesics—a review of old and upcoming targets. Br. J. Pharmacol. https://doi.org/10.1111/bph.15760 (2021).

  7. Manglik, A. et al. Structure-based discovery of opioid analgesics with reduced side effects. Nature 537, 185–190 (2016).

    Article  ADS  CAS  Google Scholar 

  8. DeWire, S. M. et al. A G protein-biased ligand at the μ-opioid receptor is potently analgesic with reduced gastrointestinal and respiratory dysfunction compared with morphines. J. Pharmacol. Exp. Ther. 344, 708–717 (2013).

    Article  CAS  Google Scholar 

  9. Faouzi, A., Varga, B. R. & Majumdar, S. Biased opioid ligands. Molecules 25, 4257 (2020).

    Article  CAS  Google Scholar 

  10. Uprety, R. et al. Controlling opioid receptor functional selectivity by targeting distinct subpockets of the orthosteric site. eLife 10, e56519 (2021).

    Article  CAS  Google Scholar 

  11. Schmid, C. L. et al. Bias factor and therapeutic window correlate to predict safer opioid analgesics. Cell 171, 1165–1175.e13 (2017).

    Article  CAS  Google Scholar 

  12. Eans, S. O. et al. Parallel synthesis of hexahydrodiimidazodiazepines heterocyclic peptidomimetics and their in vitro and in vivo activities at μ (MOR), δ (DOR), and κ (KOR) opioid receptors. J. Med. Chem. 58, 4905–4917 (2015).

    Article  CAS  Google Scholar 

  13. Majumdar, S. et al. Truncated G protein-coupled mu opioid receptor MOR-1 splice variants are targets for highly potent opioid analgesics lacking side effects. Proc. Natl Acad. Sci. USA 108, 19778–19783 (2011).

    Article  ADS  CAS  Google Scholar 

  14. Kiguchi, N. et al. BU10038 as a safe opioid analgesic with fewer side-effects after systemic and intrathecal administration in primates. Br. J. Anaesth. 122, e146–e156 (2019).

    Article  CAS  Google Scholar 

  15. Váradi, A. et al. Mitragynine/corynantheidine pseudoindoxyls as opioid analgesics with mu agonism and delta antagonism, which do not recruit β-arrestin-2. J. Med. Chem. 59, 8381–8397 (2016).

    Article  Google Scholar 

  16. Massaly, N., Temp, J., Machelska, H. & Stein, C. Uncovering the analgesic effects of a PH-dependent mu-opioid receptor agonist using a model of nonevoked ongoing pain. Pain 161, 2798–2804 (2020).

    Article  CAS  Google Scholar 

  17. Kandasamy, R. et al. Positive allosteric modulation of the mu-opioid receptor produces analgesia with reduced side effects. Proc. Natl Acad. Sci. USA 118, e2000017118 (2021).

    Article  CAS  Google Scholar 

  18. Fenalti, G. et al. Molecular control of δ-opioid receptor signalling. Nature 506, 191–196 (2014).

    Article  ADS  CAS  Google Scholar 

  19. Pert, C. B., Pasternak, G. & Snyder, S. H. Opiate agonists and antagonists discriminated by receptor binding in brain. Science 182, 1359–1361 (1973).

    Article  ADS  CAS  Google Scholar 

  20. Hu, X. et al. Kinetic and thermodynamic insights into sodium ion translocation through the μ-opioid receptor from molecular dynamics and machine learning analysis. PLoS Comput. Biol. 15, e1006689 (2019).

    Article  Google Scholar 

  21. Shang, Y. et al. Mechanistic insights into the allosteric modulation of opioid receptors by sodium ions. Biochemistry 53, 5140–5149 (2014).

    Article  CAS  Google Scholar 

  22. Liu, W. et al. Structural basis for allosteric regulation of GPCRS by sodium ions. Science 337, 232–236 (2012).

    Article  ADS  CAS  Google Scholar 

  23. Selvam, B., Shamsi, Z. & Shukla, D. Universality of the sodium ion binding mechanism in class A G-protein-coupled receptors. Angew. Chem. Int. Ed. Engl. 57, 3048–3053 (2018).

    Article  CAS  Google Scholar 

  24. Manglik, A. et al. Crystal structure of the μ-opioid receptor bound to a morphinan antagonist. Nature 485, 321–326 (2012).

    Article  ADS  CAS  Google Scholar 

  25. Chakraborty, S. et al. A novel mitragynine analog with low-efficacy mu opioid receptor agonism displays antinociception with attenuated adverse effects. J. Med. Chem. 64, 13873–13892 (2021).

    Article  CAS  Google Scholar 

  26. Overdose Death Rates. National Institute on Drug Abuse https://www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates (accessed 23 September 2019).

  27. Lipiński, P. F. J., Jarończyk, M., Dobrowolski, J. C. & Sadlej, J. Molecular dynamics of fentanyl bound to μ-opioid receptor. J. Mol. Model. 25, 144 (2019).

    Article  Google Scholar 

  28. Subramanian, G., Paterlini, M. G., Portoghese, P. S. & Ferguson, D. M. Molecular docking reveals a novel binding site model for fentanyl at the mu-opioid receptor. J. Med. Chem. 43, 381–391 (2000).

    Article  CAS  Google Scholar 

  29. Qu, Q. et al. Structural insights into distinct signaling profiles of the μOR activated by diverse agonists. Nat. Chem. Biol. https://doi.org/10.1038/s41589-022-01208-y (2022).

  30. Ballasteros, J. A. & Weinstein, H. Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Meth. Neurosci. 25, 366–428 (1995).

  31. Dardonville, C. et al. Synthesis and pharmacological studies of new hybrid derivatives of fentanyl active at the μ-opioid receptor and I2–imidazoline binding sites. Bioorg. Med. Chem. 14, 6570–6580 (2006).

    Article  CAS  Google Scholar 

  32. Gallivan, J. P. & Dougherty, D. A. Cation–π interactions in structural biology. Proc. Natl Acad. Sci. USA 96, 9459–9464 (1999).

    Article  ADS  CAS  Google Scholar 

  33. Olsen, R. H. J. et al. TRUPATH, an open-source biosensor platform for interrogating the GPCR transducerome. Nat. Chem. Biol. 16, 841–849 (2020).

    Article  CAS  Google Scholar 

  34. Raehal, K. M., Walker, J. K. L. & Bohn, L. M. Morphine side effects in β-arrestin 2 knockout mice. J. Pharmacol. Exp. Ther. 314, 1195–1201 (2005).

    Article  CAS  Google Scholar 

  35. Gillis, A. et al. Low intrinsic efficacy for g protein activation can explain the improved side effect profiles of new opioid agonists. Sci. Signal. 13, 31 (2020).

    Article  Google Scholar 

  36. Hill, R., Kruegel, A. C., Javitch, J. A., Lane, J. R. & Canals, M. The respiratory depressant effects of mitragynine are limited by its conversion to 7-OH mitragynine. Br. J. Pharmacol. 179, 3875–3885 (2022).

    Article  CAS  Google Scholar 

  37. He, L. et al. Pharmacological and genetic manipulations at the μ-opioid receptor reveal arrestin-3 engagement limits analgesic tolerance and does not exacerbate respiratory depression in mice. Neuropsychopharmacology 46, 2241–2249 (2021).

  38. Anesthetic and Analgesic Drug Products Advisory Committee. Oliceridine FDA Advisory Committee Briefing Document (FDA, 2018).

  39. Kliewer, A. et al. Phosphorylation-deficient G-protein-biased μ-opioid receptors improve analgesia and diminish tolerance but worsen opioid side effects. Nat. Commun. 10, 367 (2019).

    Article  ADS  CAS  Google Scholar 

  40. Besnard, J. et al. Automated design of ligands to polypharmacological profiles. Nature 492, 215–220 (2012).

    Article  ADS  CAS  Google Scholar 

  41. Chakraborty, S. et al. Oxidative metabolism as a modulator of kratom’s biological actions. J. Med. Chem. 64, 16553–16572 (2021).

  42. Hill, R. et al. The novel μ-opioid receptor agonist PZM21 depresses respiration and induces tolerance to antinociception. Br. J. Pharmacol. 175, 2653 (2018).

    Article  CAS  Google Scholar 

  43. Wilson, L. L. et al. Characterization of CM-398, a novel selective sigma-2 receptor ligand, as a potential therapeutic for neuropathic pain. Molecules 27, 3617 (2022).

    Article  CAS  Google Scholar 

  44. Wingler, L. et al. Angiotensin analogs with divergent bias stabilize distinct receptor conformations. Cell 176, 468–478.e11 (2019).

    Article  CAS  Google Scholar 

  45. Wootten, D., Christopoulos, A., Marti-Solano, M., Babu, M. M. & Sexton, P. M. Mechanisms of signalling and biased agonism in G protein-coupled receptors. Nat. Rev. Mol. Cell Biol. 19, 638–653 (2018).

    Article  CAS  Google Scholar 

  46. de Waal, P. W. et al. Molecular mechanisms of fentanyl mediated β-arrestin biased signaling. PLoS Comput. Biol. 16, e1007394 (2020).

    Article  Google Scholar 

  47. Liu, J.J, Horst, R, Katritch, V, Stevens, R.C., & Wuthrich, K. Biased signaling pathways in β2-adrenergic receptor characterized by 19F-NMR. Science 335, 1106–1110 (2012).

  48. Kliewer, A. et al. Morphine-induced respiratory depression is independent of β-arrestin2 signalling. Br. J. Pharmacol. 177, 2923–2931 (2020).

    Article  CAS  Google Scholar 

  49. Standifer, K. M., Rossi, G. C. & Pasternak, G. W. Differential blockade of opioid analgesia by antisense oligodeoxynucleotides directed against various G protein alpha subunits. Mol. Pharmacol. 50, 293–298 (1996).

    CAS  Google Scholar 

  50. Sánchez-Blázquez, P., Rodríguez-Díaz, M., DeAntonio, I. & Garzón, J. Endomorphin-1 and endomorphin-2 show differences in their activation of μ opioid receptor-regulated G proteins in supraspinal antinociception in mice. J. Pharmacol. Exp. Ther. 291, 12–18 (1999).

    Google Scholar 

  51. Sánchez-Blázquez, P., Gómez-Serranillos, P. & Garzón, J. Agonists determine the pattern of G-protein activation in μ-opioid receptor-mediated supraspinal analgesia. Brain Res. Bull. 54, 229–235 (2001).

    Article  Google Scholar 

  52. Yang, J. et al. Loss of signaling through the G protein, Gz, results in abnormal platelet activation and altered responses to psychoactive drugs. Proc. Natl Acad. Sci. USA 97, 9984–9989 (2000).

    Article  ADS  CAS  Google Scholar 

  53. Hendry, I. A. et al. Hypertolerance to morphine in G(Zα)-deficient mice. Brain Res. 870, 10–19 (2000).

    Article  CAS  Google Scholar 

  54. Leck, K. J. et al. Deletion of guanine nucleotide binding protein αz subunit in mice induces a gene dose dependent tolerance to morphine. Neuropharmacology 46, 836–846 (2004).

    Article  CAS  Google Scholar 

  55. Lamberts, J. T., Jutkiewicz, E. M., Mortensen, R. M. & Traynor, J. R. Mu-opioid receptor coupling to Gαo plays an important role in opioid antinociception. Neuropsychopharmacology 36, 2041–2053 (2011).

    Article  CAS  Google Scholar 

  56. Katritch, V. et al. Allosteric sodium in class A GPCR signaling. Trends Biochem. Sci 39, 233–244 (2014).

    Article  CAS  Google Scholar 

  57. Schöppe, J. et al. Crystal structures of the human neurokinin 1 receptor in complex with clinically used antagonists. Nat. Commun. 10, 17 (2019).

    Article  ADS  Google Scholar 

  58. Massink, A. et al. Sodium ion binding pocket mutations and adenosine A2A receptor function. Mol. Pharmacol. 87, 305–313 (2015).

    Article  Google Scholar 

  59. Capaldi, S. et al. Allosteric sodium binding cavity in GPR3: a novel player in modulation of aβ production. Sci. Rep. 8, 11102 (2018).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

This work was supported by an American Heart Association Postdoctoral Fellowship (H.W.), NIH grants R33045884 (S.M.), R01DA042888 and R01DA007242 (Y.X.P.), R37DA036246 (B.K.K. and G.S.), R33DA038858 and P01DA035764 (V.K.), and R21DA048650 and R00DA038725 (R.A.-H.). B.K.K. and G.S. are additionally supported by the Mathers Foundation and R.A.-H. is supported through the Brain and Behavior Research Foundation. The State of Florida, Executive Office of the Governor’s Office of Tourism, Trade, and Economic Development provides funding to J.P.M. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748 to MSKCC. Cryo-EM data collection was performed at the Stanford-SLAC Cryo-EM Facilities, supported by Stanford University, SLAC and the National Institutes of Health S10 Instrumentation Programs. The authors thank E. Montabana and C. Zhang for their support with cryo-EM data collection; and Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results. Cryo-EM data processing for this project was performed on the Sherlock cluster. The authors acknowledge the Center for Advanced Research Computing (CARC) at the University of Southern California for providing computing resources that have contributed to the research results reported in this study. Receptor binding profiles were generously provided by the National Institute of Mental Health’s Psychoactive Drug Screening Program (NIMH PDSP), contract no. HHSN-271-2018-00023-C. B.L.R. is director of NIMH PDSP at the University of North Carolina at Chapel Hill and J.D. is project officer of NIMH PDSP at NIMH, Bethesda.

Author information

Author notes

  1. These authors contributed equally: Abdelfattah Faouzi, Haoqing Wang, Saheem A. Zaidi, Jeffrey F. DiBerto, Tao Che, Qianhui Qu

Authors and Affiliations

  1. Center for Clinical Pharmacology, University of Health Sciences and Pharmacy and Washington University School of Medicine, St Louis, MO, USA

    Abdelfattah Faouzi, Tao Che, Manish K. Madasu, Amal El Daibani, Balazs R. Varga, Kevin Appourchaux, Ream Al-Hasani & Susruta Majumdar

  2. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA

    Haoqing Wang, Qianhui Qu, Michael J. Robertson, Georgios Skiniotis & Brian K. Kobilka

  3. Department of Quantitative and Computational Biology, Department of Chemistry, Bridge Institute and Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, USA

    Saheem A. Zaidi & Vsevolod Katritch

  4. Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA

    Jeffrey F. DiBerto, Tao Che, Samuel T. Slocum & Bryan L. Roth

  5. Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA

    Qianhui Qu, Michael J. Robertson & Georgios Skiniotis

  6. Department of Neurology and Molecular Pharmacology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Tiffany Zhang & Ying Xian Pan

  7. Department of Chemistry, Scripps Research, Jupiter, FL, USA

    Claudia Ruiz & Michael D. Cameron

  8. Department of Anesthesiology, Rutgers New Jersey Medical School, Newark, NJ, USA

    Shan Liu, Jin Xu & Ying Xian Pan

  9. Department of Pharmacodynamics, University of Florida, Gainesville, FL, USA

    Shainnel O. Eans & Jay P. McLaughlin

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  1. Abdelfattah Faouzi
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Contributions

S.M., B.K.K., V.K. and G.S. conceived the study. A.F. and B.R.V. synthesized the compounds, aided in their characterization under the supervision of S.M. H.W. prepared the μOR–Gi complex, obtained and processed cryo-EM data, and refined the structure from cryo-EM density maps under the supervision of B.K.K. and G.S. S.A.Z. performed the docking, ligand design, and molecular dynamics simulations under the supervision of V.K. Q.Q. and M.J.R obtained and processed cryo-EM data under the supervision of G.S. S.T.S. and J.F.D. performed the profiling studies under the supervision of B.L.R. A.E.D. performed the profiling studies under the supervision of T.C. K.A. carried out TRUPATH and pharmacokinetics assays under the supervision of S.M. T.Z. carried out binding assays under the supervision of Y.X.P. S.L. and J.X. performed the antinociception assay with ICV administration under the supervision of Y.X.P. C.R. carried out mouse brain stability assays under the supervision of M.D.C. S.O.E. and M.K.M. carried out behavioural assays under the supervision of J.P.M. and R.A.H., respectively. A.F., H.W., S.A.Z., J.F.D, J.P.M., V.K., G.S., B.K.K. and S.M. wrote the paper with contributions from the other authors.

Corresponding authors

Correspondence to Georgios Skiniotis, Vsevolod Katritch, Brian K. Kobilka or Susruta Majumdar.

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Competing interests

S.M. and Y.X.P. are founders of Sparian Biosciences. B.K.K. is a founder of and consultant for ConfometRx. G.S. is a cofounder of Deep Apple. All other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Docking of a fentanyl based bitopic targeting the Na+ binding site.

Molecular docking of a fentanyl based bitopic ligand shows that the functional head group can target the Na+ pocket.

Extended Data Fig. 2 Cryo-EM data processing work-flows.

Representative micrographs, 2D classes, 3D classes and data processing procedures for (A) C5-guano and (B) C6-guano bound µOR–Gi-scFv16 complex.

Extended Data Fig. 3 Global and local resolutions for cryo-EM maps.

(A) Gold-standard FSC curves for C5-guano and C6-guano bound μOR–Gi structures. Overall resolution is 3.2 Å for C5-guano bound μOR–Gi-scFv16 and 3.3 Å for C6-guano bound μOR–Gi using the gold Standard FSC = 0.143 criterion. (B) Local resolution map of C5 guano and C6 guano bound μOR–Gi structures. (C) Data collection, refinement, and model statistic of two structures. Extended Data Table 2. Cryo-EM data collection, refinement and validation statistics.

Extended Data Fig. 4 Comparison of bitopic structures to BU72 structure.

A, C, Side chains of μOR orthosteric pocket residues are shown for the C5-guano (A) and C6-guano (C) bound μOR–Gi complex (green) in comparison with the BU72 bound μOR (PDB code 5C1M; pink). The orthosteric pocket residues of μOR in complex with bitopic ligands and BU72 show nearly identical conformations. B, D, Side chains of μOR site-2 and Na+ site residues are shown for the C5 guano (B) and C6 guano (D) bound μOR–Gi complex (green) in comparison with the BU72 bound μOR (PDB code 5C1M; pink). The site-2 and Na+ site residues of μOR in complex with bitopic ligands and BU72 show nearly identical conformations.

Extended Data Fig. 5 Analysis of dynamics of direct and water mediated interactions of bitopic ligands.

A) Overlay of three examples of C5 guano conformations bound to active state MOR (pink cartoon/sticks) during MD simulations (B) Detailed view of the interactions between guano moiety of C5 guano (orange sticks) and D1142.50 mediated by two water molecule (C) Direct salt bridge interactions between C5 guano (light green sticks) and D1162.50 supplemented by an additional water-mediated hydrogen bond. (D) direct salt bridge interactions between C5 guano (cyan sticks) and D1142.50 (E) Probability densities of distances between guano nitrogen atoms and D1142.50 carboxylate oxygens. Each chart plots probability density for frames with two bridging waters (orange), one bridging water (green), and no bridging waters (cyan). (F) Categorization and relative proportion of D2.50 and D3.32 mediated interactions in 10 independent C5 guano-μOR MD trajectories for 1 μs each. Among the cumulative frames from the 10 μs MD runs, close to 1/3rd of the frames-maintained guano-D2.50 interactions exclusively through water-mediated hydrogen bonds, while ~57% frames formed direct salt- bridges with or without supplementary water mediated interactions. Therefore, close to 90% of the frames maintained D2.50-guano interactions. The piperidine-D3.32 interactions were observed to be even more stable, with over 96% of the frames indicating direct salt bridge or water-mediated hydrogen bonds. (G) Categorization and relative proportion of D2.50 and D3.32 mediated interactions in 5 independent C6-μOR trajectories for 1 μs each. Overall, the number of direct interactions with D2.50 increased from 57% to 85% (compared to C5), perhaps resulting from the increase in linker length by a carbon atom that decreases the overall distances to D2.50 residue.

Extended Data Fig. 6 Profiling of chemically and pharmacologically distinct μOR agonists using TRUPATH, arrestin signaling.

A) Peptides: Endomorphin-1, Leu-enkephalin, Met-enkephalin, Beta- endorphin and Dynorphin A (1-17). Dynorphin A (1-17) showed reduced arrestin recruitment while other peptides retained robust arrestin recruitment among peptides tested. B) Opioid biased agonists and partials: PZM21, TRV130, Gα- subtype selectivity and arrestin recruitment on μOR. PZM21, 7-OH and TRV130 showed <50% efficacy for arrestin1/2. Highest efficacy for all three biased agonists was seen at the Gz-subtype. μOR partial agonist pentazocine was a full agonist at the Gz subtype. C) Oxycodone and Carfentanil, Gα- subtype selectivity and arrestin recruitment on μOR. Carfentanil showed near maximal efficacy at all Gα-subtypes and arrestin1/2. Oxycodone was a full agonist at Gz and showed >50% efficacy at β-arrestin2. D) Fentanyl guano bitopics show differential G-protein and arrestin efficacy with increased chain length.

Source Data

Extended Data Table 1 Summary of binding affinities, cAMP and arrestin recruitment values of fentanyl amino and guano bitopics on μOR
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Extended Data Table 2 Cryo-EM data collection, refinement and validation statistics
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Extended Data Table 3 Potency table for drugs profiled in Extended Data Fig. 6
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Extended Data Table 4 Efficacy table for drugs profiled in Extended Data Fig. 6
Full size table

Supplementary information

Supplementary Information

This file contains the following six sections. Section 1: Material and methods; Section 2: Procedure and synthetic schemes for fentanyl bitopics; Section 3: Spectral data of fentanyl bitopics; Section 4: Additional tables and figures; Section 5: Ethics declaration; and Section 6: References.

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Faouzi, A., Wang, H., Zaidi, S.A. et al. Structure-based design of bitopic ligands for the µ-opioid receptor. Nature 613, 767–774 (2023). https://doi.org/10.1038/s41586-022-05588-y

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