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Inactive and active state structures template selective tools for the human 5-HT5A receptor

Abstract

Serotonin receptors are important targets for established therapeutics and drug development as they are expressed throughout the human body and play key roles in cell signaling. There are 12 serotonergic G protein-coupled receptor members encoded in the human genome, of which the 5-hydroxytryptamine (5-HT)5A receptor (5-HT5AR) is the least understood and lacks selective tool compounds. Here, we report four high-resolution (2.73–2.80 Å) structures of human 5-HT5ARs, including an inactive state structure bound to an antagonist AS2674723 by crystallization and active state structures bound to a partial agonist lisuride and two full agonists, 5-carboxamidotryptamine (5-CT) and methylergometrine, by cryo-EM. Leveraging the new structures, we developed a highly selective and potent antagonist for 5-HT5AR. Collectively, these findings both enhance our understanding of this enigmatic receptor and provide a roadmap for structure-based drug discovery for 5-HT5AR.

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Fig. 1: Structures of the 5-HT5AR complexes.
Fig. 2: Ligand-specific interactions with 5-HT5AR.
Fig. 3: Structural comparison between inactive state 5-HT5AR and 5-HT1BR and 5-HT2AR.
Fig. 4: The 5-HT5AR–miniGo interface.
Fig. 5: Activation of 5-HT5AR.
Fig. 6: Selective antagonist development of 5-HT5AR.

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Data availability

The structures of 5-HT5AR–AS2674723, 5-HT5AR–miniGo–5-CT, 5-HT5AR–miniGo–lisuride and 5-HT5AR–miniGo–methylergometrine have been deposited in the PDB (EMDB) under accession codes 7UM4, 7UM5 (EMD-26597), 7UM6 (EMD-26598) and 7UM7 (EMD-26599). The cryo-EM micrographs of 5-HT5AR–miniGo–5-CT, 5-HT5AR–miniGo–lisuride and 5-HT5AR–miniGo–methylergometrine have been deposited in the EMPIAR database (https://www.ebi.ac.uk/empiar/) with accession numbers EMPIAR-11033, EMPIAR-11036 and EMPIAR-11039, respectively. Source data are provided with this paper.

References

  1. Barnes, N. M. et al. International Union of Basic and Clinical Pharmacology. CX. Classification of receptors for 5-hydroxytryptamine; pharmacology and function. Pharmacol. Rev. 73, 310–520 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Berger, M., Gray, J. A. & Roth, B. L. The expanded biology of serotonin. Annu. Rev. Med. 60, 355–366 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Grailhe, R. et al. Increased exploratory activity and altered response to LSD in mice lacking the 5-HT5A receptor. Neuron 22, 581–591 (1999).

    Article  CAS  PubMed  Google Scholar 

  4. Pierce, P. A., Xie, G. X., Levine, J. D. & Peroutka, S. J. 5-Hydroxytryptamine receptor subtype messenger RNAs in rat peripheral sensory and sympathetic ganglia: a polymerase chain reaction study. Neuroscience 70, 553–559 (1996).

    Article  CAS  PubMed  Google Scholar 

  5. Sagi, Y. et al. Emergence of 5-HT5A signaling in parvalbumin neurons mediates delayed antidepressant action. Mol. Psychiatry 25, 1191–1201 (2020).

    Article  CAS  PubMed  Google Scholar 

  6. Pasqualetti, M. et al. Distribution of the 5-HT5A serotonin receptor mRNA in the human brain. Brain Res. Mol. Brain Res. 56, 1–8 (1998).

    Article  CAS  PubMed  Google Scholar 

  7. Corbett, D. F. et al. Discovery of a potent and selective 5-HT5A receptor antagonist by high-throughput chemistry. Bioorg. Med. Chem. Lett. 15, 4014–4018 (2005).

    Article  CAS  PubMed  Google Scholar 

  8. Yamazaki, M., Okabe, M., Yamamoto, N., Yarimizu, J. & Harada, K. Novel 5-HT5A receptor antagonists ameliorate scopolamine-induced working memory deficit in mice and reference memory impairment in aged rats. J. Pharmacol. Sci. 127, 362–369 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Yamazaki, M. et al. ASP5736, a novel 5-HT5A receptor antagonist, ameliorates positive symptoms and cognitive impairment in animal models of schizophrenia. Eur. Neuropsychopharmacol. 24, 1698–1708 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Levit Kaplan, A. et al. Structure-based design of a chemical probe set for the 5-HT5A serotonin receptor. J. Med. Chem. 65, 4201–4217 (2022).

  11. Erlander, M. G. et al. Two members of a distinct subfamily of 5-hydroxytryptamine receptors differentially expressed in rat brain. Proc. Natl Acad. Sci. USA 90, 3452–3456 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Rees, S. et al. Cloning and characterisation of the human 5-HT5A serotonin receptor. FEBS Lett. 355, 242–246 (1994).

    Article  CAS  PubMed  Google Scholar 

  13. Francken, B. J., Jurzak, M., Vanhauwe, J. F., Luyten, W. H. & Leysen, J. E. The human 5-HT5A receptor couples to Gi/Go proteins and inhibits adenylate cyclase in HEK 293 cells. Eur. J. Pharmacol. 361, 299–309 (1998).

    Article  CAS  PubMed  Google Scholar 

  14. Chun, E. et al. Fusion partner toolchest for the stabilization and crystallization of G protein-coupled receptors. Structure 20, 967–976 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Isberg, V. et al. Generic GPCR residue numbers—aligning topology maps while minding the gaps. Trends Pharmacol. Sci. 36, 22–31 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Popov, P. et al. Computational design of thermostabilizing point mutations for G protein-coupled receptors. eLife 7, e34729 (2018).

  17. 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  PubMed  PubMed Central  Google Scholar 

  18. Garcia-Nafria, J., Nehme, R., Edwards, P. C. & Tate, C. G. Cryo-EM structure of the serotonin 5-HT1B receptor coupled to heterotrimeric Go. Nature 558, 620–623 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Duan, J. et al. Cryo-EM structure of an activated VIP1 receptor–G protein complex revealed by a NanoBiT tethering strategy. Nat. Commun. 11, 4121 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Xu, P. et al. Structural insights into the lipid and ligand regulation of serotonin receptors. Nature 592, 469–473 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Bach, T. et al. 5HT4(a) and 5-HT4(b) receptors have nearly identical pharmacology and are both expressed in human atrium and ventricle. Naunyn Schmiedebergs Arch. Pharmacol. 363, 146–160 (2001).

    Article  CAS  PubMed  Google Scholar 

  22. Grailhe, R., Grabtree, G. W. & Hen, R. Human 5-HT5 receptors: the 5-HT5A receptor is functional but the 5-HT5B receptor was lost during mammalian evolution. Eur. J. Pharmacol. 418, 157–167 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Kim, K. et al. Structure of a hallucinogen-activated Gq-coupled 5-HT2A serotonin receptor. Cell 182, 1574–1588 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wan, Q. et al. Mini G protein probes for active G protein-coupled receptors (GPCRs) in live cells. J. Biol. Chem. 293, 7466–7473 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wacker, D., Stevens, R. C. & Roth, B. L. How ligands illuminate GPCR molecular pharmacology. Cell 170, 414–427 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Weis, W. I. & Kobilka, B. K. The molecular basis of G protein-coupled receptor activation. Annu. Rev. Biochem. 87, 897–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Katritch, V., Cherezov, V. & Stevens, R. C. Structure–function of the G protein-coupled receptor superfamily. Annu. Rev. Pharmacol. Toxicol. 53, 531–556 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Che, T. et al. Structure of the nanobody-stabilized active state of the κ opioid receptor. Cell 172, 55–67 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. White, K. L. et al. Structural connection between activation microswitch and allosteric sodium site in GPCR signaling. Structure 26, 259–269 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hamaguchi, W. et al. Tetrahydroisoquinoline derivative. US patent 8,962,612 B2 (2015).

  33. Kinoyama, I. et al. Nitrogenous-ring acylguanidine derivative. US patent 8,853,242 B2 (2014).

  34. Cournia, Z., Allen, B. & Sherman, W. Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J. Chem. Inf. Model. 57, 2911–2937 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. Schrödinger Release 2020-4: FEP+ (Schrödinger, 2021).

  36. Caffrey, M. & Cherezov, V. Crystallizing membrane proteins using lipidic mesophases. Nat. Protoc. 4, 706–731 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Cao, C. et al. Structure, function and pharmacology of human itch GPCRs. Nature 600, 170–175 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Peck, J. V., Fay, J. F. & Strauss, J. D. High-speed high-resolution data collection on a 200 keV cryo-TEM. IUCrJ 9, 243–252 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).

    Article  PubMed  Google Scholar 

  40. Bepler, T., Kelley, K., Noble, A. J. & Berger, B. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat. Commun. 11, 5208 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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).

    Article  CAS  PubMed  Google Scholar 

  42. Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).

    Article  CAS  PubMed  Google Scholar 

  44. Heymann, J. B. & Belnap, D. M. Bsoft: image processing and molecular modeling for electron microscopy. J. Struct. Biol. 157, 3–18 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. Sanchez-Garcia, R. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun. Biol. 4, 874 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  47. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010).

    Article  CAS  PubMed  Google Scholar 

  50. Olsson, M. H., Sondergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput. 7, 525–537 (2011).

    Article  CAS  PubMed  Google Scholar 

  51. Harder, E. et al. OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput. 12, 281–296 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Abel, R., Wang, L., Harder, E. D., Berne, B. J. & Friesner, R. A. Advancing drug discovery through enhanced free energy calculations. Acc. Chem. Res. 50, 1625–1632 (2017).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by US National Institutes of Health grants RO1MH112205 and U24DK1169195 (to B.L.R.) and by R35GM122481 (to B.K.S.). This work also used the NMR spectrometer systems at Mount Sinai, acquired with funding from National Institutes of Health SIG grants 1S10OD025132 and 1S10OD028504 (to J.J.). We gratefully acknowledge M.J. Miley and the UNC macromolecular crystallization core for the use of their equipment for crystal collection and transport along with the UNC Flow Cytometry Core Facility. Both facilities are supported in part by a P30 CA016086 Cancer Center Core Support Grant to the UNC Lineberger Comprehensive Cancer Center. We also thank the staff of GM/CA@APS, which has been funded with federal funds from the National Cancer Institute (ACB-12002) and the National Institute of General Medical Sciences (AGM-12006). This research used resources of the Advanced Photon Source, a US Department of Energy Office of Science User Facility operated for the Department of Energy Office of Science by Argonne National Laboratory under contract no. DE-AC02-06CH11357. We thank J. Peck and J. Strauss of the UNC CryoEM Core Facility for their excellent technical assistance with this project. We are grateful to Schrödinger for the academic grant of FEP+ and multiple other tools in their software suite. The Titan X Pascal used for this research was kindly donated to J.F.F. by Nvidia.

Author information

Authors and Affiliations

Authors

Contributions

S.Z. designed the experiments; performed cloning, expression, purification and preparation of the 5-HT5AR–miniGo complexes, model building and structure refinement in the cryo-EM study; performed purification of the inactive state 5-HT5AR–AS2674723 complex and LCP crystallization, data collection and model building and refinement in the X-ray study; performed mutagenesis and functional studies; performed the binding assay and profiled SAR-designed compounds; and prepared the manuscript. H.C., J.L. and Y.X. performed the SAR study for selective compound development of 5-HT5AR. C.Z. synthesized the AS2674723 compounds for the inactive state structure study. Y.Y. performed the FEP analysis. P.P. and V.K. performed computational predictions of the thermostabilizing point mutations. B.E.K., C.C. and K.K. assisted with protein expression. B.K.S. supervised the FEP analysis. J.J. supervised medicinal chemistry experiments. J.F.F. made the grids and collected and processed cryo-EM data. B.L.R. supervised the entire project, guided the structural and functional work and prepared the manuscript.

Corresponding authors

Correspondence to Jonathan F. Fay or Bryan L. Roth.

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Competing interests

B.K.S. serves on the SAB of Schrödinger. The remaining authors declare no competing interests.

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Nature Structural and Molecular Biology thanks Yunje Cho and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Florian Ullrich, in collaboration with the Nature Structural & Molecular Biology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Sequence alignment and transducerome profiling of 5-HT5AR.

a, Sequencing alignment matrix of the 12 GPCR members in the 5-HT receptor family. Similarities are shown on the lower-left side of the table and identities on the upper-right. b, Transducerome screening of 5-HT5AR using TRUPATH platform by the endogenous agonist 5-HT. Net BRET values of 5-HT5AR together with positive controls of either neurotensin receptor 1 (NTSR1, agonist NT1-13) or β2AR (agonist isoproterenol) are shown in each panel. Data represent mean ± SEM of N = 3 biological replicates.

Source data

Extended Data Fig. 2 CryoEM analysis for 5-HT5AR-miniGo bound to 5-CT, Lisuride, and Methylergometrine.

For each of the respective agonist bound 5-HT5AR heterotrimeric complexes are shown: a, Histograms of defocus values for micrographs used in the single-particle analysis (see Table 2 for more details). b. Representative frame aligned micrograph. The experiment was repeated three times with similar results. c, Orientational distribution heat map. d, 2D plots of the gold-standard Fourier shell correlation (GSFSC) between half maps (black) and FSC between model and the B-factor sharpened map for respective refined model (red) as calculated by Phenix.mitrage. e, Local resolution heat-map calculated using the local windowed FSC method.

Extended Data Fig. 3 Comparisons of 5-HT5AR structures and the interface between 5-HT5AR and miniGo protein.

a-c, Extracellular and intracellular views of receptors (a-b) and G proteins (c) from the superpositions of the three agonist-bound 5-HT5AR complexes. d-e, two views of the displacements of three alanines in the 3 A cluster upon the receptor activation. The movements of the residues are indicated by the red arrows. f. superposition of the 5-CT-bound 5-HT5AR structure with the active state 5-HT1AR structures. The Ptdlns4P molecule is shown in sticks. g-h, Interactions between the R4x38 or R34x57 and D3x49 in the active (g) and inactive (h) 5-HT5AR structures. i, Representative density maps of Y225H5.26 and K2796x29. j, Interactions between the V2876x37 and surrounding residues in the inactive 5-HT5AR structure. k, The electrostatic potential surface of 5-HT5AR from the intracellular side and interaction with the α5 helix of miniGo.

Extended Data Fig. 4 Ligand binding pocket of 5-HT5AR.

a-b, Two views of the superposition of ligands AS2674723, 5-CT, lisuride, and methylergometrine in the 5-HT5AR binding site. The hydrogen bonds between T126 and three agonists are shown as dashed lines in the same color as the corresponding ligand, respectively. The positions of EBP1 and EBP2 are also indicated by the purple- and salmon-shaded ovals, respectively. c, Displacement of waters in the binding pocket of the apo 5-HT1AR-Gi complex structure (PDB: 7E2X) compared with the water molecule in the 5-CT bound structure. d, The representative density map of E305 in the 5-CT bound 5-HT5AR structure. e-g, cryo-EM maps of T126 and three agonists. The distances are indicated by the dashed lines. h. Structural alignment of the ligand-binding pockets of three agonist-bound 5-HT5AR complexes. i. Binding affinities of 5-HT and 5-CT to all the members of the human serotonin receptor family from the Ki database of PDSP (https://pdsp.unc.edu/databases/kidb.php). Data represent mean ± SEM of N = 1-10 replicates based on the source from the Ki database.

Source data

Extended Data Fig. 5 Graphical representation of binding affinities.

a, Binding affinities of AS2674723 against the aminergic receptors. Data represent mean ± SEM of N = 3-10 replicates (N = 1 for muscarinic receptors (M1-M5)). b-c, Binding affinities of AS2674723 and LSD for WT and mutant 5-HT5ARs using 3H-LSD. See Supplementary Table 11 for fitted parameter values that represent mean ± SEM of N = 3 biological replicates.

Source data

Extended Data Fig. 6 Sequence alignments of 12 GPCR members of the 5-HT receptor family.

a, Alignment of the residues in the ligand-binding pocket. b, Alignment of the residues in ICL2, 3 A cluster, and receptor-G protein interface.

Extended Data Fig. 7 Functional validation of the double mutant (A226V and A227L).

a-b, BRET2 Gi1-activation assays and c-d, BRET1 β-arrestin2 recruitment of WT and double mutant (A226V and A227L) 5-HT5AR stimulated by eight ligands. See Supplementary Table 12 for fitted parameter values that represent mean ± SEM of N = 3 biological replicates.

Source data

Extended Data Fig. 8 Drug development of 5-HT5AR.

a-b, Chemical structures of all the designed compounds in the second (a) and third rounds (b) of structure-based drug development of 5-HT5AR. Compounds with the ID colored in black or pink are selected from the patent or newly designed, respectively. The starting compound in each cycle is highlighted in a yellow shaded box. The varied moieties are colored in the corresponding color used in the starting compound. The predicted ΔΔG values by the FEP method are also labeled under the corresponding compound.

Extended Data Fig. 9 Functional validation of the new selective compound MS112 and correlation analysis of FEP prediction.

a-b, Agonist and antagonist activities tests of the compound MS112 towards 5-HT5AR. See Supplementary Table 13 for fitted parameter values that represent mean ± SEM of N = 4 biological replicates. c-d, Correlation analysis between the predicted ΔΔG and experimental ΔΔG values. The dotted lines in both panels represent the ±0.5 kcal/mol between the FEP predicted ΔΔG and the experimental ΔΔG, respectively. And the +0.5 kcal/mol and −0.5 kcal/mol dotted lines are colored in magenta and blue, respectively. See Supplementary Table 14 for details.

Source data

Extended Data Fig. 10 Surface expression level of wt and mutant 5-HT5ARs.

Data represent mean ± SEM of N = 3 biological replicates.

Source data

Supplementary information

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Supplementary Fig. 1, Tables 1–14 and Methods.

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Zhang, S., Chen, H., Zhang, C. et al. Inactive and active state structures template selective tools for the human 5-HT5A receptor. Nat Struct Mol Biol 29, 677–687 (2022). https://doi.org/10.1038/s41594-022-00796-6

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