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Insights into distinct signaling profiles of the µOR activated by diverse agonists

Nature Chemical Biology volume 19, pages 423–430 (2023)Cite this article

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Abstract

Drugs targeting the μ-opioid receptor (μOR) are the most effective analgesics available but are also associated with fatal respiratory depression through a pathway that remains unclear. Here we investigated the mechanistic basis of action of lofentanil (LFT) and mitragynine pseudoindoxyl (MP), two μOR agonists with different safety profiles. LFT, one of the most lethal opioids, and MP, a kratom plant derivative with reduced respiratory depression in animal studies, exhibited markedly different efficacy profiles for G protein subtype activation and β-arrestin recruitment. Cryo-EM structures of μOR-Gi1 complex with MP (2.5 Å) and LFT (3.2 Å) revealed that the two ligands engage distinct subpockets, and molecular dynamics simulations showed additional differences in the binding site that promote distinct active-state conformations on the intracellular side of the receptor where G proteins and β-arrestins bind. These observations highlight how drugs engaging different parts of the μOR orthosteric pocket can lead to distinct signaling outcomes.

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Fig. 1: Distinct ligands for μOR.
Fig. 2: Structures of μOR-Gi complex activated by MP and LFT.
Fig. 3: Semiconserved ligand interaction network for MP and LFT.
Fig. 4: Simulations of the μOR reveal distinct binding pocket conformations favored by MP, DAMGO and LFT.
Fig. 5: In simulations with the G protein removed, the μOR adopts two active intracellular conformations, with MP and LFT favoring different conformations.

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Data availability

The atomic coordinates for MP–μOR-Gi1 and LFT–μOR-Gi1-scFv complexes have been deposited in the Protein Data Bank with the accession codes 7T2G and 7T2H, respectively. The EM maps for MP–μOR-Gi1 and LFT–μOR-Gi1-scFv complexes have been deposited in EMDB with the accession codes EMD-25612 and EMD-25613, respectively. The composite non-model-based density-modified map for MP–µOR-Gi1 is deposited to the Electron Microscopy Data Bank (EMDB) as the main map and used for model building. The locally refined individual maps are deposited as additional maps.

Change history

  • 01 December 2022

    In the version of this article initially published, due to a processing error, additional traces appeared in Figure 4b, which has since been corrected in the HTML and PDF versions of the article.

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Acknowledgements

This work was supported by the Swiss National Science Foundation Early Postdoctoral Mobility grant no. P2ELP3_187989 (D.A.); the European Molecular Biology Organization Long-Term Fellowship grant no. ALTF 544-2019 (D.A.); a Stanford Graduate Fellowship (J.M.P.); the Human Frontier Science Program Long-Term Fellowship grant no. LT000916/2018-L (C.-M.S.); the National Institutes of Health grants no. R01GM127359 (R.O.D.), no. DA045884 (S.M.) and no. R37DA036246 (B.K.K. and G.S.); and the Mathers Foundation (G.S. and B.K.K.). B.K.K. is a Chan Zuckerberg Biohub Investigator. An award of computer time was provided by the INCITE program. This research used resources of the Oak Ridge Leadership Computing Facility, which is a US Department of Energy Office of Science User Facility supported under contract no. DE-AC05-00OR22725. A.I. was funded by grant nos. PRIME 19gm5910013, LEAP 19gm0010004 and BINDS JP20am0101095 from the Japan Agency for Medical Research and Development (AMED); grant nos. KAKENHI 21H04791 and 21H05113 from the Japan Society for the Promotion of Science (JSPS); and JST Moonshot Research and Development Program grant no. JPMJMS2023 from Japan Science and Technology Agency (JST). We thank F. M. N. Kadji, K. Sato, Y. Sugamura and A. Inoue at Tohoku University for plasmid construction and the cell-based GPCR assays; and Y. Laloudakis, S. Hollingsworth and N. Latorraca for helpful discussions.

Author information

Author notes

  1. Qianhui Qu

    Present address: Shanghai Stomatological Hospital, Fudan University and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Department of Systems Biology for Medicine, Fudan University, Shanghai, China

  2. These authors contributed equally: Qianhui Qu, Weijiao Huang, Deniz Aydin, Joseph M. Paggi, Alpay B. Seven.

Authors and Affiliations

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

    Qianhui Qu, Weijiao Huang, Deniz Aydin, Joseph M. Paggi, Alpay B. Seven, Haoqing Wang, Michael J. Robertson, Carl-Mikael Suomivuori, Ron O. Dror, Brian K. Kobilka & Georgios Skiniotis

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

    Qianhui Qu, Deniz Aydin, Joseph M. Paggi, Alpay B. Seven, Michael J. Robertson, Carl-Mikael Suomivuori, Ron O. Dror & Georgios Skiniotis

  3. Department of Computer Science, Stanford University, Stanford, CA, USA

    Deniz Aydin, Joseph M. Paggi, Carl-Mikael Suomivuori & Ron O. Dror

  4. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA

    Deniz Aydin, Joseph M. Paggi, Carl-Mikael Suomivuori & Ron O. Dror

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

    Soumen Chakraborty & Susruta Majumdar

  6. Department of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA

    Soumen Chakraborty, Tao Che & Susruta Majumdar

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

    Tao Che, Jeffrey F. DiBerto & Bryan L. Roth

  8. Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan

    Asuka Inoue

  9. Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA

    Bryan L. Roth

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  1. Qianhui Qu
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Contributions

W.H. and H.W. prepared protein samples for structural studies and conducted biochemical assays. Q.Q. and A.B.S. collected and processed the cryo-EM data and generated the maps. Q.Q., M.J.R. and A.B.S. built and refined models. S.C. synthesized MP. T.C. and J.F.D. performed signaling profile assays under the supervision of B.L.R. A.I. performed NanoBiT experiments. D.A. and J.M.P. performed and analyzed molecular dynamics simulations, with input from C.-M.S., under the supervision of R.O.D. Q.Q., W.H., D.A., J.M.P., A.B.S., S.M., R.O.D., B.K.K. and G.S. interpreted the data and wrote the manuscript with inputs from all authors. B.K.K. and G.S. supervised the project.

Corresponding authors

Correspondence to Susruta Majumdar, Ron O. Dror, Brian K. Kobilka or Georgios Skiniotis.

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Competing interests

G.S. is a cofounder of and consultant for Deep Apple Therapeutics. B.K.K. is a cofounder of and consultant for ConfometRx. S.M. is a cofounder of Sparian biosciences. S.M. has filed a provisional patent (US20220024923A1) on MP and related molecules.

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Nature Chemical Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Activity characterization of diverse ligands on μOR.

a, Efficacy of compounds LFT, MP and Morphine at human μOR of Gi1, GoA, GoB and Gz activation, and recruitment of β-arrestin-2 using the BRET assay are shown as a percentage of receptor activation relative to the full agonist, DAMGO.MP had significantly lower G-protein as well as arrestin among ligands tested (****p < 0.0001). Statistical significance was determined using one-way ANOVA followed by Dunnett’s multiple comparison test. Data represent mean + /- SEM from five independent experiments. b, Dose-dependent activation of Gi1 signaling in NanoBiT Gi1-dissociation assay and activation of β-arr1 and β-arr2 signaling in NanoBiT Arrestin-recruitment assay on wild type human μOR. Data are the means (+/- SEM) from four independent experiments and summarized at bottom panel. c, Efficacy of LFT, MP, and DAMGO at mouse μOR for Gi1, Gi3, GoA, GoB and Gz activation measured by GTPase-GloTM assay (n = 3 independent replicates, means + /-SEM are represented).

Extended Data Fig. 2 Cryo-EM process for LFT-μOR-Gi1-scFv and MP-μOR-Gi1 complexes.

Representative raw micrographs (a) and 2D classification averages (b) for LFT-μOR-Gi1-scFv and MP-μOR-Gi1, respectively. c, Workflow of cryo-EM data processing of LFT (left) and MP (right). d, Angular distribution of projections for cryo-EM maps and gold-standard FSC curves of half-maps (0.143 cutoff). e, Local resolution of LFT-μOR-Gi1-scFv cryo-EM map. f, Local resolution of composite cryo-EM maps of MP-μOR-Gi1 after local refinement and non-model based density modification (Phenix Resolve Cryo-EM).

Extended Data Fig. 3 Conserved μOR-Gi1 conformation activated by diverse agonists.

a, Alignment of MP (orange), LFT (red) and DAMGO (blue) bound μOR-Gi1 complexes onto BU72-bound μOR (grey), with nanobody and scFv removed for clarity. b, Close view of the ligand binding orthosteric pocket show nearly identical poses for residues involved in different agonists interaction, except that Q214 orients its side-chain towards TM3 upon MP engagement, which results in the loss of Q214-Y326 interaction observed among LFT, DAMGO and BU72. Orientation of GPCR activation feature motifs P5.50-I3.40-F6.44 (c), DR3.50Y and NP7.50xxY7.53 (d), and residues lined on the major interface between μOR intracellular site composed of TM2-3, TM5-7 and ICL1-3 (e) and C-terminal α5 helix of Gα subunit (f) are quite similar, suggesting a canonical conformation for Gi1 heterotrimer coupled μOR activated by DAMGO, LFT and MP.

Extended Data Fig. 4 Interaction network comparison among diverse μOR ligands.

a, Structurally distinct agonists such as morphinan BU72 (grey), enkephalin-like DAMGO (blue), synthetic LFT (magenta), novel alkaloid MP (green) and morphinan antagonist βFNA (black) all occupy the central pocket (cp) of wide orthosteric binding site. b, Viewed from extracellular side, functional moieties of the BU72, DAMGO and LFT penetrate into a sub-pocket between TM2 and TM3 (sp1), while MP indole ring explores a new arena composed of TM1, TM2 and TM7 (sp2). c, Schematic interaction diagrams highlight a conserved salt-bridge/hydrogen-bond interaction between μOR D147 (red sphere) and a tertiary amine (NH+) on BU72, DAMGO, LFT and MP, in addition to the major hydrophobic interaction network, calculated by Maestro (Schrödinger Release 2018-4: Maestro, Schrödinger, LLC, New York, NY, 2018).

Extended Data Fig. 5 Binding poses of fentanyl analogues revealed by docking and simulation.

a, Chemical structures of fentanyl and its derivatives, carfentanil and lofentanil. The 4-carbomethoxy moiety added to the piperidinyl group of fentanyl makes carfentanil over 100 times more potent at the μOR, while further addition of the 3-methyl group slightly enhances lofentanil in efficacy compared to carfentanil. b-e, Potential binding poses of carfentanil and fentanyl. The binding pose of LFT in the LFT-μOR cryo-EM structure (magenta sticks) is overlaid with (b) the docked pose of carfentanil (teal sticks), (c) the fentanyl pose modelled using the cryo-EM pose of LFT (pink sticks), (d) the first docked pose of fentanyl (orange stick), and (e) the second docked pose of fentanyl (yellow sticks). The first docked pose is in an orientation similar to that of LFT. The second docked pose is in an orientation opposite to that of LFT and has more favorable GlideScore and Emodel than the first. f, Simulations with fentanyl initiated in the poses of panels c, d, and e are shown in the first, second, and third columns, respectively (see Methods). For each simulation frame, the RMSD of fentanyl relative to the initial pose in that simulation is computed after alignment on the receptor. The first docked pose shifts to the pose shown in panel c in eight out of ten simulations.

Extended Data Fig. 6 Frequencies of key interactions in the binding pocket with different ligands bound.

a, Frequency of key inter-residue hydrogen bonds in simulations with various ligands bound (see Methods). b, Frequency of key protein-ligand interactions in simulations with various ligands bound (see Methods). Pi-pi interactions are aromatic stacking interactions between Y7.43 and the ligand. Lower and upper box boundaries indicate the 25th and 75th percentiles, respectively. The light gray line inside the box denotes the median. Lower and upper error lines represent 10th and 90th percentiles, respectively. Empty circles represent data points falling outside the 10th to 90th percentiles. Individual data points are frequencies in each independent simulation. Each boxplot is computed over six independent simulations with the same ligand bound (see Methods).

Extended Data Fig. 7 Intracellular TM7 rotation.

a, Intracellular TM7 rotation in Fig. 5d and Extended Data Fig. 11 was calculated as described in Methods. i and i + 1 denote consecutive residues. The view is from the extracellular side. The reference structure is represented in black, and the simulation frame is represented in orange. The computed angle is denoted by θ. b, The counterclockwise rotation in the intracellular portion of TM7 (when viewed from the extracellular side; see Fig. 5d) during the transition from the canonical active state to the alternative state leads to an inward shift as measured by a decrease in the P7.50–L2.46 distance. Relative to DAMGO, MP favors the canonical active state, whereas LFT favors the alternative state. Each distribution is computed over 6 independent simulations with the same ligand bound (see Methods).

Extended Data Fig. 8 Interaction between Q2.60 and Y7.43 increases the probability that the intracellular coupling site adopts the alternative state conformation.

a, The distribution of intracellular TM7 rotation when the Q2.60–Y7.43 interaction is formed (left panel) and when it is not (right panel). b, The distribution of P7.50–L2.46 distance when the Q2.60–Y7.43 interaction is formed (left panel) and when it is not (right panel). Distributions are computed across all 18 simulations (6 independent simulations with each of the 3 ligands bound; see Methods).

Extended Data Fig. 9 The transition from the canonical active conformation to the alternative conformation involves changes in an interaction network in the core of the receptor.

The transition from the canonical active conformation to the alternative conformation involves a shift in the hydrogen bonding network in the sodium binding pocket, as shown through representative frames from our μOR simulations (viewed from the extracellular side; see Methods). In the canonical active conformation, N861.50 forms a hydrogen bond with S3297.46, and D1142.50 forms a hydrogen bond with N3327.49. In the alternative conformation, these interactions are broken and replaced by D1142.50–S3297.46 and D1142.50–N1503.35 hydrogen bonds. The μOR cryo-EM structure (grey) is represented by the MP-µOR-Gi1 structure reported in this manuscript.

Extended Data Fig. 10 Comparison of the intracellular conformations observed for the μOR and the AT1R.

a, Simulations indicate that in both the μOR and the AT1R, the transition from the canonical active conformation to the alternative conformation involves a counterclockwise twist at TM7 (viewed from the extracellular side; bottom panels), leading to relaxation of the kink in the NPxxY region and the inward movement of P7.50 (viewed from the extracellular side; top panels). Both at the μOR and the AT1R, P7.50 is translated inward in the alternative conformation with respect to the canonical active conformation and the inactive conformation (top panels). b, The µOR alternative conformation differs from the AT1R alternative conformation in that the intracellular end of TM7 shows an inward displacement in μOR compared to the AT1R alternative conformation. The interaction between R3.50 and D8.47 does not allow the downward Y7.53 rotamer observed at the AT1R. The canonical active and alternative conformations shown here for the μOR are representative frames from our μOR simulations (see Methods). The canonical active and alternative conformations shown here for the AT1R are representative simulation frames (PDB files included in the Supplementary Material of Suomivuori, et al34. The inactive μOR and AT1R structures are the inactive μOR and AT1R crystal structures, respectively (PDB IDs: 4DKL and 4YAY).

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Supplementary Figs. 1–7 and Tables 1 and 2.

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Qu, Q., Huang, W., Aydin, D. et al. Insights into distinct signaling profiles of the µOR activated by diverse agonists. Nat Chem Biol 19, 423–430 (2023). https://doi.org/10.1038/s41589-022-01208-y

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