Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Structural basis of ketamine action on human NMDA receptors

An Author Correction to this article was published on 14 October 2021

This article has been updated

Abstract

Ketamine is a non-competitive channel blocker of N-methyl-d-aspartate (NMDA) receptors1. A single sub-anaesthetic dose of ketamine produces rapid (within hours) and long-lasting antidepressant effects in patients who are resistant to other antidepressants2,3. Ketamine is a racemic mixture containing equal parts of (R)- and (S)-ketamine, with the (S)-enantiomer having greater affinity for the NMDA receptor4. Here we describe the cryo-electron microscope structures of human GluN1–GluN2A and GluN1–GluN2B NMDA receptors in complex with S-ketamine, glycine and glutamate. Both electron density maps uncovered the binding pocket for S-ketamine in the central vestibule between the channel gate and selectivity filter. Molecular dynamics simulation showed that S-ketamine moves between two distinct locations within the binding pocket. Two amino acids—leucine 642 on GluN2A (homologous to leucine 643 on GluN2B) and asparagine 616 on GluN1—were identified as key residues that form hydrophobic and hydrogen-bond interactions with ketamine, and mutations at these residues reduced the potency of ketamine in blocking NMDA receptor channel activity. These findings show structurally how ketamine binds to and acts on human NMDA receptors, and pave the way for the future development of ketamine-based antidepressants.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: S-ketamine-bound cryo-EM structures of human GluN1–GluN2A and GluN1–GluN2B NMDA receptors.
Fig. 2: MD simulation of the S-ketamine-bound TMD of the GluN1–GluN2A receptor.
Fig. 3: Mechanism by which ketamine inhibits GluN1–GluN2A and GluN1–GluN2B NMDA receptors.

Data availability

The cryo-EM maps and structure coordinates for GluN1–GluN2A and GluN1–GluN2B receptors have been deposited in the Electron Microscopy Data Bank under accession numbers EMD-31308 and EMD-31309, and in the Protein Data Bank under accession numbers 7EU7 and 7EU8, respectively. The structures of S-ketamine and R-ketamine are accessible from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) under compound CIDs 182137 and 644025, respectively. The human missense mutation c.1925T>G (L642R) in GRIN2A was retrieved from the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/) under variation ID 985631. Additional data that support the findings of this study are available from the corresponding author upon request.

Change history

References

  1. 1.

    Autry, A. E. et al. NMDA receptor blockade at rest triggers rapid behavioural antidepressant responses. Nature 475, 91–95 (2011).

    CAS  Article  Google Scholar 

  2. 2.

    Berman, R. M. et al. Antidepressant effects of ketamine in depressed patients. Biol. Psychiatry 47, 351–354 (2000).

    CAS  Article  Google Scholar 

  3. 3.

    Fava, M. et al. Double-blind, placebo-controlled, dose-ranging trial of intravenous ketamine as adjunctive therapy in treatment-resistant depression (TRD). Mol. Psychiatry 25, 1592–1603 (2020).

    CAS  Article  Google Scholar 

  4. 4.

    Ebert, B., Mikkelsen, S., Thorkildsen, C. & Borgbjerg, F. M. Norketamine, the main metabolite of ketamine, is a non-competitive NMDA receptor antagonist in the rat cortex and spinal cord. Eur. J. Pharmacol. 333, 99–104 (1997).

    CAS  Article  Google Scholar 

  5. 5.

    Malhi, G. S. & Mann, J. J. Depression. Lancet 392, 2299–2312 (2018).

    Article  Google Scholar 

  6. 6.

    Trivedi, M. H. et al. Medication augmentation after the failure of SSRIs for depression. N. Engl. J. Med. 354, 1243–1252 (2006).

    CAS  Article  Google Scholar 

  7. 7.

    Turner, E. H. Esketamine for treatment-resistant depression: seven concerns about efficacy and FDA approval. Lancet Psychiatry 6, 977–979 (2019).

    Article  Google Scholar 

  8. 8.

    Wilkinson, S. T. et al. The effect of a single dose of intravenous ketamine on suicidal ideation: a systematic review and individual participant data meta-analysis. Am. J. Psychiatry 175, 150–158 (2018).

    Article  Google Scholar 

  9. 9.

    Yang, Y. et al. Ketamine blocks bursting in the lateral habenula to rapidly relieve depression. Nature 554, 317–322 (2018).

    ADS  CAS  Article  Google Scholar 

  10. 10.

    Duman, R. S., Aghajanian, G. K., Sanacora, G. & Krystal, J. H. Synaptic plasticity and depression: new insights from stress and rapid-acting antidepressants. Nat. Med. 22, 238–249 (2016).

    CAS  Article  Google Scholar 

  11. 11.

    Moda-Sava, R. N. et al. Sustained rescue of prefrontal circuit dysfunction by antidepressant-induced spine formation. Science 364, eaat8078 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Paoletti, P., Bellone, C. & Zhou, Q. NMDA receptor subunit diversity: impact on receptor properties, synaptic plasticity and disease. Nat. Rev. Neurosci. 14, 383–400 (2013).

    CAS  Article  Google Scholar 

  13. 13.

    Karakas, E. & Furukawa, H. Crystal structure of a heterotetrameric NMDA receptor ion channel. Science 344, 992–997 (2014).

    ADS  CAS  Article  Google Scholar 

  14. 14.

    Zhu, S. et al. Mechanism of NMDA receptor inhibition and activation. Cell 165, 704–714 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    Jalali-Yazdi, F., Chowdhury, S., Yoshioka, C. & Gouaux, E. Mechanisms for zinc and proton inhibition of the GluN1/GluN2A NMDA receptor. Cell 175, 1520–1532.e15 (2018).

    CAS  Article  Google Scholar 

  16. 16.

    Chou, T. H., Tajima, N., Romero-Hernandez, A. & Furukawa, H. Structural basis of functional transitions in mammalian NMDA receptors. Cell 182, 357–371.e13 (2020).

    CAS  Article  Google Scholar 

  17. 17.

    Jelen, L. A., Young, A. H. & Stone, J. M. Ketamine: a tale of two enantiomers. J. Psychopharmacol. 35, 109–123 (2021).

  18. 18.

    Erreger, K., Dravid, S. M., Banke, T. G., Wyllie, D. J. & Traynelis, S. F. Subunit-specific gating controls rat NR1/NR2A and NR1/NR2B NMDA channel kinetics and synaptic signalling profiles. J. Physiol. 563, 345–358 (2005).

    CAS  Article  Google Scholar 

  19. 19.

    Burnashev, N. et al. Control by asparagine residues of calcium permeability and magnesium blockade in the NMDA receptor. Science 257, 1415–1419 (1992).

    ADS  CAS  Article  Google Scholar 

  20. 20.

    Song, X. et al. Mechanism of NMDA receptor channel block by MK-801 and memantine. Nature 556, 515–519 (2018).

    ADS  CAS  Article  Google Scholar 

  21. 21.

    Durham, R. J. et al. Conformational spread and dynamics in allostery of NMDA receptors. Proc. Natl Acad. Sci. USA 117, 3839–3847 (2020).

    CAS  Article  Google Scholar 

  22. 22.

    Iacobucci, G. J. et al. Cross-subunit interactions that stabilize open states mediate gating in NMDA receptors. Proc. Natl Acad. Sci. USA 118, e2007511118 (2021).

    CAS  Article  Google Scholar 

  23. 23.

    Kashiwagi, K. et al. Channel blockers acting at N-methyl-d-aspartate receptors: differential effects of mutations in the vestibule and ion channel pore. Mol. Pharmacol. 61, 533–545 (2002).

    CAS  Article  Google Scholar 

  24. 24.

    Chang, L. et al. Comparison of antidepressant and side effects in mice after intranasal administration of (R,S)-ketamine, (R)-ketamine, and (S)-ketamine. Pharmacol. Biochem. Behav. 181, 53–59 (2019).

    CAS  Article  Google Scholar 

  25. 25.

    Lü, W., Du, J., Goehring, A. & Gouaux, E. Cryo-EM structures of the triheteromeric NMDA receptor and its allosteric modulation. Science 355, eaal3729 (2017).

    Article  Google Scholar 

  26. 26.

    Zanos, P. et al. NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Nature 533, 481–486 (2016).

    ADS  CAS  Article  Google Scholar 

  27. 27.

    Lumsden, E. W. et al. Antidepressant-relevant concentrations of the ketamine metabolite (2R,6R)-hydroxynorketamine do not block NMDA receptor function. Proc. Natl Acad. Sci. USA 116, 5160–5169 (2019).

    CAS  Article  Google Scholar 

  28. 28.

    Lee, C. H. et al. NMDA receptor structures reveal subunit arrangement and pore architecture. Nature 511, 191–197 (2014).

    ADS  CAS  Article  Google Scholar 

  29. 29.

    Zhang, J. B. et al. Structural basis of the proton sensitivity of human GluN1-GluN2A NMDA receptors. Cell Rep. 25, 3582–3590.e4 (2018).

    CAS  Article  Google Scholar 

  30. 30.

    Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    CAS  Article  Google Scholar 

  31. 31.

    Zhang, K. Gctf: real-time CTF determination and correction. J. Struct. Biol. 193, 1–12 (2016).

    ADS  CAS  Article  Google Scholar 

  32. 32.

    Zivanov, J., Nakane, T. & Scheres, S. H. W. Estimation of high-order aberrations and anisotropic magnification from cryo-EM data sets in RELION-3.1. IUCrJ 7, 253–267 (2020).

    CAS  Article  Google Scholar 

  33. 33.

    Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    CAS  Article  Google Scholar 

  34. 34.

    Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    CAS  Article  Google Scholar 

  35. 35.

    Morris, G. M. et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009).

    CAS  Article  Google Scholar 

  36. 36.

    Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics 54, 5.6.1–5.6.37 (2016).

    Article  Google Scholar 

  37. 37.

    Schrodinger, LLC Maestro, Version 9.0 (2009).

  38. 38.

    Vanommeslaeghe, K. & MacKerell, A. D., Jr. Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J. Chem. Inf. Model. 52, 3144–3154 (2012).

    CAS  Article  Google Scholar 

  39. 39.

    Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).

    CAS  Article  Google Scholar 

  40. 40.

    Kumari, R., Kumar, R., Open Source Drug Discovery Consortium & Lynn, A. g_mmpbsa—a GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model. 54, 1951–1962 (2014).

    CAS  Article  Google Scholar 

  41. 41.

    Chen, S. et al. Activation and desensitization mechanism of AMPA receptor-TARP complex by cryo-EM. Cell 170, 1234–1246.e14 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    Meyerson, J. R. et al. Structural basis of kainate subtype glutamate receptor desensitization. Nature 537, 567–571 (2016).

    ADS  CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank B. Zhu, X. Li, C. Liu, F. Meng and Z. Guo for their assistance with cryo-EM data collection; H. Wang, J. Zhang and Y. Gu for assistance with experiments; Y. Sun for advice on human NMDA receptor variants; and M. Poo and E. Xu for proofreading. Financial support is gratefully acknowledged from the National Natural Science Foundation of China (31771115), the National Key R&D Program of China (2017YFA0505700), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB32020000), the Shanghai Municipal Science and Technology Major Project (2018SHZDZX05) and the Thousand Young Talents Program to S.Z.; and the National Natural Science Foundation of China (81625022, 91853205, 81821005) and the Shanghai Municipal Health Commission in China (18431907100 and 19XD1404700) to C.L.

Author information

Affiliations

Authors

Contributions

Y.Z. and T.Z. purified and froze the protein, collected and analysed the cryo-EM data, built the atomic model and conducted electrophysiology on GluN1–GluN2A and GluN1–GluN2B receptors, respectively; F.Y., L.Z., and C.L. carried out the docking and MD simulation; D.D. assisted with binding assays; S.L. and F.G. participated in the data analysis; H.L. provided ketamine compounds; Y.Z., F.Y., T.Z. and S.Z wrote the manuscript with inputs from all the authors. S.Z. conceived the project and supervised the research.

Corresponding authors

Correspondence to Cheng Luo or Shujia Zhu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Expression profile and functional validation of human GluN1–GluN2A and GluN1–GluN2B NMDA receptors.

a, Schematic representation of human GluN1EM (grey), GluN2AEM (green) and GluN2BEM (blue) CTD-truncated constructs. b, Representative fluorescence SEC and Coomassie blue gel staining of the purified GluN1–GluN2AEM (left) and GluN1–GluN2BEM receptors (right). Experiments were performed three times independently with similar results. c, d, Representative recording traces (left) and the fitted DRCs (right; mean ± s.d.) for (R,S)-ketamine inhibition on wild-type GluN1–GluN2A and GluN1–GluN2AEM (c), and wild-type GluN1–GluN2B and GluN1–GluN2BEM (d) receptors activated with 100 μM coagonists. Ketamine IC50 values, Hill slopes and numbers are listed in Extended Data Table 3.

Extended Data Fig. 2 Overview of cryo-EM image processing and 3D reconstruction of GluN1–GluN2A and GluN1–GluN2B NMDA receptors.

a, Flowchart of the image processing and 3D reconstruction of human GluN1–GluN2A receptors in complex with S-ketamine, glycine and glutamate. Typical single particles are circled in red from raw micrographs. The 2D class average images show characteristic 2D views in various orientations. The 3D classes with similar conformations were selected and combined through several rounds of 3D classification for final refinement. b, Fourier shell correlation (FSC) curve for the resolution estimation. c, Side view of the cryo-EM density map of GluN1–GluN2A receptor coloured by local resolution estimated by Relion 3.1. d, Pipelines for single-particle analysis and reconstruction of human GluN1–GluN2B receptor in complex with S-ketamine, glycine and glutamate. Same workflow as in a. e, FSC curve for the resolution estimation. f, Side view of the cryo-EM density map of GluN1–GluN2B receptor coloured by local resolution estimated by Relion 3.1.

Extended Data Fig. 3 Representative local densities in GluN1–GluN2A cryo-EM map with fitted atomic model.

a, b, Zoomed-in views of the GluN1-LBD cleft with EM density for glycine (a), and the GluN2A-LBD cleft with glutamate (b). Key binding residues are shown as sticks. c, d, Local densities in extracellular domains of the GluN1 (a) and GluN2A (b) subunits. EM densities are shown as light grey mesh, while the side chains and N-glycosylation sites of residues are represented as sticks.

Extended Data Fig. 4 Functional validation of the ketamine-binding pocket of NMDA receptors.

a, Representative IV curves for Mg2+ blockage of wild-type GluN1–GluN2A receptors or receptors incorporating a substitution (A, N or V) at GluN2A-L642, recorded in the presence of 100 μM MgCl2. b, c, Plot of 3 μM ketamine inhibition level for wild-type (open circle) and mutant (filled circles) amino acid volume (I, V, A or G) at position GluN2B-L643 or GluN2D-L667, shown with a linear regression. R2 values equal to 0.72 for GluN1–GluN2B and 0.95 for GluN1–GluN2D receptors. d, e, (R,S)-Ketamine DRCs for the GluN1–GluN2A and GluN1–GluN2B wild-type or mutant receptors incorporating a substitution (A, L or T) at GluN1-V644. All IC50 values, Hill slopes and numbers of oocytes are listed in Extended Data Table 3. f, Schematic representation of the S-ketamine binding site on GluN1–GluN2B receptors analysed by Ligplot+.

Extended Data Fig. 5 Molecular basis of S- and R-ketamine-induced inhibition of GluN1–GluN2A receptors.

a, Chemical structures of left-handed S-ketamine and right-handed R-ketamine. b, r.m.s.d. trajectories for each chain (excluding M1–M2 loops) and R-ketamine were calculated on Cα atoms based on the initial structure within the whole simulation time of 500 ns. c, Left, R-ketamine poses obtained in MD simulation along the whole simulation time. Right, schematic diagram of R-ketamine and TMD interactions at 500 ns snapshot extracted from MD simulation. Residues involved in the hydrophobic interactions are shown as starbursts. d, Inhibition by 2 μM S-ketamine of NMDA receptor activity induced by saturating agonists in wild-type GluN1–GluN2A (0.606 ± 0.018, n = 9 oocytes), GluN1(N616A)–GluN2A (0.020 ± 0.004, n = 3), GluN1(N616Q)–GluN2A (0.027 ± 0.006, n = 3), GluN1–GluN2A(L642A) (0.041 ± 0.012, n = 3), GluN1–GluN2A(L642V) (0.152 ± 0.007, n = 3), GluN1–GluN2A(L642N) (0.020 ± 0.001, n = 3), GluN1(V644A)–GluN2A (0.333 ± 0.038, n = 3), GluN1(V644L)–GluN2A (0.455 ± 0.012, n = 3), GluN1(V644T)–GluN2A (0.602 ± 0.020, n = 3), GluN1(T648V)–GluN2A (0.621 ± 0.015, n = 3), GluN1–GluN2A(T646V) (0.532 ± 0.024, n = 4) and GluN1–GluN2A(N615A) (0.597 ± 0.008, n = 3) receptors. e, Inhibition by 2 μM R-ketamine of wild-type GluN1–GluN2A (0.404 ± 0.017, n = 9), GluN1(N616A)–GluN2A (0.008 ± 0.001, n = 3), GluN1(N616Q)–GluN2A (0.049 ± 0.008, n = 3), GluN1–GluN2A(L642A) (0.014 ± 0.001, n = 3), GluN1–GluN2A(L642V) (0.065 ± 0.009, n = 3), GluN1–GluN2A(L642N) (0.019 ± 0.001, n = 3), GluN1(T648V)–GluN2A (0.330 ± 0.021, n = 3), GluN1–GluN2A(T646V) (0.423 ± 0.021, n = 4) and GluN1–GluN2A(N615A) (0.344 ± 0.01, n = 3) receptors. f, Superposition of the 500 ns snapshots extracted from MD simulation of S-ketamine (pink) and R-ketamine (cyan) systems, respectively. g, Inhibition by 2 μM R- and S-ketamine of GluN1–GluN2A(N614A) (0.138 ± 0.009, 0.509 ± 0.014, n = 3) and GluN1–GluN2A(N614Q) (0.140 ± 0.006, 0.633 ± 0.014, n = 3) receptors. In d, e, g, all data shown are mean ± s.e.m.; P values are determined by one-way ANOVA with Dunnett’s multiple comparison test (****P < 0.0001). Each data point represents the result of one oocyte. h, R-ketamine (left) and S-ketamine (right) DRCs (mean ± s.d.) for wild-type GluN1–GluN2A (IC50: 2.39 ± 0.45 μM, n = 4 oocytes; 0.60 ± 0.03 μM, n = 3), GluN1–GluN2A(N614A) (13.66 ± 2.49 μM, n = 4; 1.22 ± 0.44 μM, n = 4) and GluN1–GluN2A(N614Q) (39.94 ± 6.72 μM, n = 4; 1.41 ± 0.38 μM, n = 4) receptors.

Extended Data Fig. 6 Sequence alignment and structural comparison of TMD in ionotropic GluRs.

a, Sequence alignment of TM2–TM3 segments in human GluN1, GluN2A, GluN2B, GluN2C, GluN2D, GluA2 and GluK2, Xenopus GluN1, GluN2B and rat GluA2 subunits. The critical residues involved in ketamine binding are highlighted in yellow, and their homologous sites in ionotropic GluRs are marked in rectangles. b, Superposition of the TM2 and TM3 segments between the S-ketamine (in brick red)-bound GluN1–GluN2BEM receptor and the MK-801 (in red)-bound GluN1–GluN2B(ΔNTD) receptor (PDB code: 5UN1)20. c, MK-801-bound TMD of the triheteromeric GluN1–GluN2A–GluN2B NMDA receptor, viewed parallel to the membrane (PDB code: 5UOW)25. MK-801 binding residues were analysed by LigPlot+ (right). d, e, Superposition of the TM2 and TM3 segments between the S-ketamine bound GluN1–GluN2AEM receptor and GluA2 AMPA receptor (PDB code: 5VOT)41 (d) or the GluK2 kainate receptor (PDB code: 5KUF)42 (e). All superpositions are based on the α-carbon atoms of the conserved SYTANL region.

Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics
Extended Data Table 2 Per-residue decomposition of relative binding energy for residues within 10 Å of S-ketamine
Extended Data Table 3 Summary of potency of (R,S)-ketamine on human GluN1–GluN2A and GluN1–GluN2B receptors
Extended Data Table 4 Per-residue decomposition of relative binding energy for residues within 10 Å of R-ketamine

Supplementary information

Supplementary Figure 1

Uncropped gels presented in the Extended Data Fig. 1.

Reporting Summary

Peer Review File

Video 1

Motions of GluN1-GluN2A TMD in complex with S-ketamine (brick-red) and R-ketamine (blue) during the 500 ns MD simulation. The TMD of GluN1 and GluN2A subunits are shown in grey and green, respectively.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Ye, F., Zhang, T. et al. Structural basis of ketamine action on human NMDA receptors. Nature 596, 301–305 (2021). https://doi.org/10.1038/s41586-021-03769-9

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing