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Plasticity in ligand recognition at somatostatin receptors

Nature Structural & Molecular Biology volume 29, pages 210–217 (2022)Cite this article

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Abstract

Somatostatin is a signaling peptide that plays a pivotal role in physiologic processes relating to metabolism and growth through its actions at somatostatin receptors (SSTRs). Members of the SSTR subfamily, particularly SSTR2, are key drug targets for neuroendocrine neoplasms, with synthetic peptide agonists currently in clinical use. Here, we show the cryogenic-electron microscopy structures of active-state SSTR2 in complex with heterotrimeric Gi3 and either the endogenous ligand SST14 or the FDA-approved drug octreotide. Complemented by biochemical assays and molecular dynamics simulations, these structures reveal key details of ligand recognition and receptor activation at SSTRs. We find that SSTR ligand recognition is highly diverse, as demonstrated by ligand-induced conformational changes in ECL2 and substantial sequence divergence across subtypes in extracellular regions. Despite this complexity, we rationalize several known sources of SSTR subtype selectivity and identify an additional interaction for specific binding. These results provide valuable insights for structure-based drug discovery at SSTRs.

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Fig. 1: Cryo-EM structures of SST14 or octreotide activated SSTR2–Gi protein complex.
Fig. 2: Differential SSTR2 ECL2 interactions with octreotide and SST14.
Fig. 3: Role of ECL2 and ECL3 in SSTR subtype selectivity.
Fig. 4: Subtype-selective point mutations in SSTR1 and SSTR2.

Data availability

Source data for all assays has been provided in spreadsheet format and raw micrographs have been uploaded to EMPIAR under the accession codes EMPIAR-10931 and EMPIAR-10932. All data generated or analyzed in this study are included in this article and the Supplementary Information. The cryo-EM density maps and corresponding coordinates have been deposited in the Electron Microscopy Data Bank (EMDB) and the PDB, respectively, under the following accession codes: PDB 7T10 EMDB-25586 (SST14) and PDB 7T11 EMDB-25587 (Octreotide). Source data are provided with this paper.

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References

  1. Günther, T. et al. International Union of Basic and Clinical Pharmacology. CV. Somatostatin receptors: structure, function, ligands, and new nomenclature. Pharmacol. Rev. 70, 762–835 (2018).

    Article  Google Scholar 

  2. Gu, Y. Z. & Schonbrunn, A. Coupling specificity between somatostatin receptor sst2A and G proteins: Isolation of the receptor-G protein complex with a receptor antibody. Mol. Endocrinol. 11, 527–537 (1997).

    Article  CAS  PubMed  Google Scholar 

  3. Hofman, M. S., Eddie Lau, W. F. & Hicks, R. J. Somatostatin receptor imaging with 68Ga DOTATATE PET/CT: clinical utility, normal patterns, pearls, and pitfalls in interpretation. Radiographics 35, 500–516 (2015).

  4. Kaltsas, G., Androulakis, I. I., De Herder, W. W. & Grossman, A. B. Paraneoplastic syndromes secondary to neuroendocrine tumours. Endocr. Relat. Cancer 17, 173–193 (2010).

    Article  Google Scholar 

  5. Liapakis, G. et al. Identification of ligand binding determinants in the somatostatin receptor subtypes 1 and 2. J. Biol. Chem. 271, 20331–20339 (1996).

    Article  CAS  PubMed  Google Scholar 

  6. Bruns, C., Lewis, I., Briner, U., Meno-Tetang, G. & Weckbecker, G. SOM230: a novel somatostatin peptidomimetic with broad somatotropin release inhibiting factor (SRIF) receptor binding and a unique antisecretory profile. Eur. J. Endocrinol. 146, 707–716 (2002).

    Article  CAS  PubMed  Google Scholar 

  7. Casarini, A. P. M. et al. Acromegaly: correlation between expression of somatostatin receptor subtypes and response to octreotide-lar treatment. Pituitary 12, 297–303 (2009).

    Article  CAS  PubMed  Google Scholar 

  8. Plöckinger, U., Dienemann, D. & Quabbe, H.-J. Gastrointestinal side-effects of octreotide during long term treatment of acromegaly. J. Clin. Endocrinol. Metab. 71, 1658–1662 (1990).

    Article  PubMed  Google Scholar 

  9. Parry, J. J., Chen, R., Andrews, R., Lears, K. A. & Rogers, B. E. Identification of critical residues involved in ligand binding and G protein signaling in human somatostatin receptor subtype 2. Endocrinol. 153, 2747–2755 (2012).

    Article  CAS  Google Scholar 

  10. Strnad, J. & Hadcock, J. R. Identification of a critical aspartate residue in transmembrane domain three necessary for the binding of somatostatin to the somatostatin receptor SSTR2. Biochem. Biophys. Res. Commun. 22, 913–921 (1995).

    Article  Google Scholar 

  11. Greenwood, M. T. et al. Ligand binding pocket of the human somatostatin receptor 5: Mutational analysis of the extracellular domains. Mol. Pharmacol. 52, 807–814 (1997).

    Article  CAS  PubMed  Google Scholar 

  12. Nehrung, R. B., Meyerhof, W. & Richter, D. Aspartic acid residue 124 in the third transmembrane domain of the somatostatin receptor subtype 3 is essential for somatostatin-14 binding. DNA Cell Biol. 14, 939–944 (1995).

    Article  Google Scholar 

  13. Kaupmann, K. et al. Two amino acids, located in transmembrane domains VI and VII, determine the selectivity of the peptide agonist SMS 201-995 for the SSTR2 somatostatin receptor. EMBO J. 14, 727–735 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 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 

  15. Che, T. et al. Nanobody-enabled monitoring of kappa opioid receptor states. Nat. Commun. 11, 1145 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Robertson, M. J. et al. Structure determination of inactive-state GPCRs with a universal nanobody. Preprint at bioRxiv https://doi.org/10.1101/2021.11.02.466983 (2021).

  17. Maeda, S. et al. Development of an antibody fragment that stabilizes GPCR/G-protein complexes. Nat. Commun. 9, 3712 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Maeda, S., Qu, Q., Robertson, M. J., Skiniotis, G. & Kobilka, B. K. Structures of the M1 and M2 muscarinic acetylcholine receptor/G-protein complexes. Science 364, 552–557 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Koehl, A. et al. Structure of the μ-opioid receptor-Gi protein complex. Nature 558, 547–552 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ballesteros, 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. Methods Neurosci. 25, 366–428 (1995).

    Article  CAS  Google Scholar 

  21. Michel, J., Tirado-Rives, J. & Jorgensen, W. L. Prediction of the water content in protein binding sites. J. Phys. Chem. B. 113, 13337–13346 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wacker, D. et al. Structural features for functional selectivity at serotonin receptors. Science 340, 615–619 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Deupi, X., Standfuss, J. & Schertler, G. Conserved activation pathways in G-protein-coupled receptors. Biochem. Soc. Trans. 40, 383–388 (2012).

    Article  CAS  PubMed  Google Scholar 

  24. McAllister, S. D. et al. Structural mimicry in class A G protein-coupled receptor rotamer toggle switches. J. Biol. Chem. 46, 48024–48037 (2004).

    Article  Google Scholar 

  25. Xing, C. et al. Cryo-EM structure of the human cannabinoid receptor CB2-Gi signaling complex. Cell 180, 645–654 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Xu, X. et al. Binding pathway determines norepinephrine selectivity for the human β1AR over β2AR. Cell Res. 31, 569–579 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Zivanov, J. et al. RELION-3: new tools for automated high-resolution cryo-EM structure determination. eLife. 7, e42166 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Rohou, A. & Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. CryoSPARC: agorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  32. Liebshner, 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).

    Article  Google Scholar 

  33. Robertson, M. J., van Zundert, G. C. P., Borrelli, K. & Skiniotis, G. GemSpot: a pipeline for robust modeling of ligands into Cryo-EM Maps. Structure 28, 707–716 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sindhikara, D. et al. Improving accuracy, diversity, and speed with prime macrocycle conformational sampling. J. Chem. Inf. Model. 57, 1881–1894 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. van Zundert, G. C. P., Moriarty, N. W., Sobolev, O. V., Adams, P. D. & Borrelli, K. W. Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality. Structure 29, 913–921 (2021).

    Article  PubMed  Google Scholar 

  36. Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I. & Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. https://doi.org/10.1093/nar/gkr703 (2012).

  37. Lee, J. et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12, 405–413 (2016).

    Article  CAS  PubMed  Google Scholar 

  38. Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Phillips, J. C. et al. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Miyamoto, S. & Kollman, P. A. Settle: an analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 13, 952–962 (1992).

    Article  CAS  Google Scholar 

  41. Ryckaert, J. P., Ciccotti, G. & Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327–341 (1977).

    Article  CAS  Google Scholar 

  42. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).

    Article  CAS  PubMed  Google Scholar 

  43. Robertson, M. J., Tirado-Rives, J. & Jorgensen, W. L. Improved peptide and protein torsional energetics with the OPLS-AA force field. J. Chem. Theory Comput. 11, 3499–3509 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).

    Article  CAS  Google Scholar 

  45. Jorgensen, W. L. & Tirado-Rives, J. Molecular modeling of organic and biomolecular systems using BOSS and MCPRO. J. Comput. Chem. 26, 1689–1700 (2005).

    Article  CAS  PubMed  Google Scholar 

  46. Towns, J. et al. XSEDE: accelerating scientific discovery. Comput. Sci. Eng. 16, 62–74 (2014).

    Article  Google Scholar 

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Acknowledgements

Cryo-EM data were collected at the Stanford cryo-EM center (cEMc) with support from E. Montabana. This work was supported, in part, by the Mathers Foundation (G.S.), training grant no. T32GM089626 (J.G.M.) and used the Extreme Science and Engineering Discovery Environment (XSEDE)46 resource comet-gpu through sdsc-comet allocation grant no. TG-MCB190153 (G.S.), which is supported by National Science Foundation grant number ACI-1548562.

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Author notes

  1. These authors contributed equally: Michael J. Robertson, Justin G. Meyerowitz.

Authors and Affiliations

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

    Michael J. Robertson, Justin G. Meyerowitz, Ouliana Panova & Georgios Skiniotis

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

    Michael J. Robertson, Justin G. Meyerowitz, Ouliana Panova & Georgios Skiniotis

  3. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Justin G. Meyerowitz

  4. Schrödinger, New York, NY, USA

    Kenneth Borrelli

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Contributions

M.J.R. cloned constructs, expressed and purified proteins, processed electron microscopy data, built models and ran then analyzed molecular dynamics simulations. J.G.M. performed BRET assays, expressed and purified proteins. O.P. prepared cryo-EM samples and collected cryo-EM data. K.B., M.J.R. and G.S. wrote the paper with input from J.G.M. and O.P. G.S. supervised the project.

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Correspondence to Georgios Skiniotis.

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Nature Structural and Molecular Biology thanks Daniel Rosenbaum and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Florian Ullrich was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 G protein specificity of SSTR2.

a, Dose-response curves of G protein-specific activation pathways of SSTR2 by SST14 (magenta squares), or octreotide (lavender circles), as compared to activation of neurotensin 1 receptor (NTSR1) by neurotensin (blue triangles) or 𝜷2 adrenergic receptor by isoproterenol (teal diamonds) from simultaneous curve fitting of n = 3 biologically independent replicates. Error bars are S.E.M.. b, Bar plot of Emax from the above dose-response curves. Data represent mean + /− S.E.M. from the simultaneous curve-fitting of n = 3 biological replicates.

Source data

Extended Data Fig. 2 SSTR2/Gi3/scFv16/octreotide complex cryo-EM data collection and processing.

a, Representative micrograph of SSTR2/Gi3/scFv16/octreotide complex, 1 micrograph out of 7,576. b, Example final 2D classes of SSTR2/Gi3/scFv16/octreotide complex. c, Cryo-EM data processing workflow. d, Local resolution of SSTR2/Gi3/scFv16/octreotide global refinement with FSC curve below. e, Local resolution of SSTR2/Gi3/scFv16/octreotide local refinement on SSTR2 with FSC curve below.

Extended Data Fig. 3 SSTR2/Gi3/scFv16/SST14 complex cryo-EM data collection and processing.

a, Representative micrograph of SSTR2/Gi3/scFv16/SST14 complex, 1 micrograph out of 9,740. b, Example final 2D classes of SSTR2/Gi3/scFv16/SST14 complex. c, Cryo-EM data processing workflow. d, Local resolution of SSTR2/Gi3/scFv16/SST14 global refinement with FSC curve below.

Extended Data Fig. 4 Map-Model Agreements.

a, Map-model comparison for SSTR2/SST14. b, Map-model comparison for SSTR2/Octreotide.

Extended Data Fig. 5 Comparison of SSTR2 Gi3 interface.

a, Alignment of SSTR2/Gi3 and MOR Gi1 from two different angles. b, Hydration at the SSTR2 ICL2/Gi3 interface; orange spheres are predicted water positions from JAWS simulations. c, Hydration at the SSTR2 DRY motif/Gi3 interface; orange spheres are predicted water positions from JAWS simulations.

Extended Data Fig. 6 Comparison of ECL2 and ECL3 interactions and mutagenesis with octreotide and SST14.

a, Overlay of the structures of SSTR2 bound to octreotide (lavender) and SST14 (magenta) around ECL3. b, Dose-response curves of SSTR2-dependent Gi3 BRET biosensor activation by SST14. c, Dose-response curves of SSTR2-dependent Gi3 BRET biosensor activation by octreotide. d, Cell surface expression analysis of point mutants and ECL swaps of SSTR2 and e, SSTR1. Bars represent mean value + /− S.E.M. from n = 3 biologically independent replicates. f, Dose-response curves of SSTR3-dependent Gi3 BRET biosensor activation by SST14. All dose-response curve points represent the mean + /− S.E.M. from n = 3 independent biological replicates, and curves are simultaneously fit to n = 3 independent biological replicates.

Source data

Extended Data Fig. 7 Comparison of interactions of additional SSTR2 agonists with SSTR2.

a, Fold change in selectivity in favor of SST14 versus SST28 calculated by EC50 shift of 11-point dose response curves. Bars represent mean EC50 ratio + /− 95% CI from simultaneous curve-fitting of n = 3 biologically independent replicates. b, Overlay of our homology model of lanreotide(green)-bound SSTR2 (teal) with our cryoEM structure of octreotide(lavender)-bound SSTR2. c, Interaction of F7 and the C terminus of SST14 (magenta) with Y2055.35 of SSTR2 (teal).

Source data

Supplementary information

Source data

Source Data Fig. 2

Excel file with source BRET data.

Source Data Fig. 3

Excel file with source BRET data.

Source Data Fig. 4

Excel file with source BRET data.

Source Data Extended Data Fig. 1

Excel file with source BRET data.

Source Data Extended Data Fig. 6

Excel file with source BRET data.

Source Data Extended Data Fig. 7

Excel file with source BRET data.

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Robertson, M.J., Meyerowitz, J.G., Panova, O. et al. Plasticity in ligand recognition at somatostatin receptors. Nat Struct Mol Biol 29, 210–217 (2022). https://doi.org/10.1038/s41594-022-00727-5

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