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Symmetric and asymmetric receptor conformation continuum induced by a new insulin

Nature Chemical Biology volume 18, pages 511–519 (2022)Cite this article

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Abstract

Cone snail venoms contain a wide variety of bioactive peptides, including insulin-like molecules with distinct structural features, binding modes and biochemical properties. Here, we report an active humanized cone snail venom insulin with an elongated A chain and a truncated B chain, and use cryo-electron microscopy (cryo-EM) and protein engineering to elucidate its interactions with the human insulin receptor (IR) ectodomain. We reveal how an extended A chain can compensate for deletion of B-chain residues, which are essential for activity of human insulin but also compromise therapeutic utility by delaying dissolution from the site of subcutaneous injection. This finding suggests approaches to developing improved therapeutic insulins. Curiously, the receptor displays a continuum of conformations from the symmetric state to a highly asymmetric low-abundance structure that displays coordination of a single humanized venom insulin using elements from both of the previously characterized site 1 and site 2 interactions.

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Fig. 1: Alignment of insulin sequences.
Fig. 2: Activities of Vh-Ins analogs based on cone snail venoms containing extended A-chain sequences.
Fig. 3: The symmetric Vh-Ins-HSLQ–receptor structure.
Fig. 4: Receptor binding affinity.
Fig. 5: Conformational heterogeneity in Vh-Ins-HSLQ–receptor reconstructions.
Fig. 6: Activity of Vh-Ins-HALQ relative to human insulin.

Data availability

RNA-sequencing data have been deposited into the GenBank Nucleotide Database (accession numbers MW091321, MW091322, MW091323 and MW091324). Coordinates of the refined atomic models have been deposited in the Protein Data Bank (‘head’: 7MQO; ‘whole’: 7MQR; ‘asymmetric’: 7MQS). The associated cryo-EM maps have been deposited in the Electron Microscopy Data Bank (‘head’: EMD-23949; ‘whole’: EMD-23950; ‘asymmetric’: EMD-23951). Raw cryo-EM movies are available on the Electron Microscopy Public Image Archive (accession code: EMPIAR-10736). Other data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

References

  1. Shabanpoor, F., Separovic, F. & Wade, J. D. The human insulin superfamily of polypeptide hormones. Vitam. Horm. 80, 1–31 (2009).

    Article  CAS  PubMed  Google Scholar 

  2. Heinemann, L. Variability of insulin absorption and insulin action. Diabetes Technol. Ther. 4, 673–682 (2004).

  3. Gradel, A. K. J. et al. Factors affecting the absorption of subcutaneously administered insulin: effect on variability. J. Diabetes Res. 2018, 1205121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Menting, J. G. et al. How insulin engages its primary binding site on the insulin receptor. Nature 493, 241–245 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Carpenter, F. H. Relationship of structure to biological activity of insulin as revealed by degradative studies. Am. J. Med. 40, 750–758 (1966).

    Article  CAS  PubMed  Google Scholar 

  6. Cruz, L. J., Gray, W. R., Yoshikami, D. & Olivera, B. M. Conus venoms—a rich source of neuroactive peptides. J. Toxicol. Toxin Rev. 4, 107–132 (1985).

    Article  CAS  Google Scholar 

  7. Olivera, B. M. Conus venom peptide: reflections from the biology of clades and species. Annu. Rev. Ecol. Syst. 33, 25–47 (2002).

    Article  Google Scholar 

  8. Safavi-Hemami, H. et al. Specialized insulin is used for chemical warfare by fish-hunting cone snails. Proc. Natl Acad. Sci. USA 112, 1743–1748 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Xiong, X. et al. A structurally minimized yet fully active insulin based on cone-snail venom insulin principles. Nat. Struct. Mol. Biol. 27, 615–624 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ahorukomeye, P. et al. Fish-hunting cone snail venoms are a rich source of minimized ligands of the vertebrate insulin receptor. eLife 8, e41574 (2019).

  11. Menting, J. G. et al. A minimized human insulin-receptor-binding motif revealed in a Conus geographus venom insulin. Nat. Struct. Mol. Biol. 23, 916–920 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Safavi-Hemami, H. et al. Venom insulins of cone snails diversify rapidly and track prey taxa. Mol. Biol. Evol. 33, 2924–2934 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ul-Hasan, S. et al. Characterization of the peptidylglycine α-amidating monooxygenase (PAM) from the venom ducts of neogastropods, Conus bullatus and Conus geographus. Toxicon 74, 215–224 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gutmann, T. et al. Cryo-EM structure of the complete and ligand-saturated insulin receptor ectodomain. J. Cell Biol. 219, e201907210 (2020).

  15. Scapin, G. et al. Structure of the insulin receptor–insulin complex by single-particle cryo-EM analysis. Nature 556, 122–125 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Uchikawa, E., Choi, E., Shang, G., Yu, H. & Bai, X.-C. Activation mechanism of the insulin receptor revealed by cryo-EM structure of the fully liganded receptor–ligand complex. eLife 8, e48630 (2019).

  17. Weis, F. et al. The signalling conformation of the insulin receptor ectodomain. Nat. Commun. 9, 4420 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Menting, J. G. et al. Protective hinge in insulin opens to enable its receptor engagement. Proc. Natl Acad. Sci. USA 111, E3395–E3404 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pandyarajan, V. et al. Aromatic anchor at an invariant hormone–receptor interface. J. Biol. Chem. 289, 34709–34727 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Žáková, L. et al. Structural integrity of the B24 site in human insulin is important for hormone functionality. J. Biol. Chem. 288, 10230–10240 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Hansen, B. F., Kurtzhals, P., Jensen, A. B., Dejgaard, A. & Russell-Jones, D. Insulin X10 revisited: a super-mitogenic insulin analogue. Diabetologia 54, 2226–2231 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. Schreiber, G. & Fersht, A. R. Energetics of protein–protein interactions: analysis of the Barnase–Barstar interface by single mutations and double mutant cycles. J. Mol. Biol. 248, 478–486 (1995).

    CAS  PubMed  Google Scholar 

  23. Hoyne, P. A. et al. High affinity insulin binding by soluble insulin receptor extracellular domain fused to a leucine zipper. FEBS Lett. 479, 15–18 (2000).

    Article  CAS  PubMed  Google Scholar 

  24. Whittaker, J., Garcia, P., Yu, G. Q. & Mynarcik, D. C. Transmembrane domain interactions are necessary for negative cooperativity of the insulin receptor. Mol. Endocrinol. 8, 1521–1527 (1994).

    CAS  PubMed  Google Scholar 

  25. Lou, M. et al. The first three domains of the insulin receptor differ structurally from the insulin-like growth factor 1 receptor in the regions governing ligand specificity. Proc. Natl Acad. Sci. USA 103, 12429–12434 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Croll, T. I. et al. Higher-resolution structure of the human insulin receptor ectodomain: multi-modal inclusion of the insert domain. Structure 24, 469–476 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bepler, T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat. Methods 16, 1153–1160 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  29. Brems, D. N. et al. Altering the association properties of insulin by amino acid replacement. Protein Eng. 5, 527–533 (1992).

    Article  CAS  PubMed  Google Scholar 

  30. Gammeltoft, S. et al. Insulin aspart: a novel rapid-acting human insulin analogue. Expert Opin. Investig. Drugs 8, 1431–1442 (1999).

    Article  CAS  PubMed  Google Scholar 

  31. Rakatzi, I. et al. A novel insulin analog with unique properties: LysB3, GluB29 insulin induces prominent activation of insulin receptor substrate 2, but marginal phosphorylation of insulin receptor substrate 1. Diabetes 52, 2227–2238 (2003).

    Article  CAS  PubMed  Google Scholar 

  32. Lawrence, M. C. Understanding insulin and its receptor from their three-dimensional structures. Mol. Metab. 52, 101255 (2021).

  33. Jiráček, J. & Žáková, L. Structural perspectives of insulin receptor isoform-selective insulin analogs. Front. Endocrinol. 8, 167 (2017).

  34. Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).

    Article  CAS  PubMed  Google Scholar 

  35. Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. 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 

  37. 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 

  38. Asarnow, D., Palovcak, E. & Cheng, Y. UCSF pyem v0.5. Zenodo https://doi.org/10.5281/zenodo.3576630 (2019).

  39. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. Sect. D Biol. Crystallogr. 66, 486–501 (2010).

    Article  CAS  Google Scholar 

  40. Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. Sect. D Struct. Biol. 75, 861–877 (2019).

    Article  CAS  Google Scholar 

  41. Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. 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 

  43. Goddard, T. D. et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Denley, A. et al. Structural determinants for high-affinity binding of insulin-like growth factor II to insulin receptor (IR)-A, the exon 11 minus isoform of the IR. Mol. Endocrinol. 18, 2502–2512 (2004).

    Article  CAS  PubMed  Google Scholar 

  45. Menting, J. G., Ward, C. W., Margetts, M. B. & Lawrence, M. C. A thermodynamic study of ligand binding to the first three domains of the human insulin receptor: relationship between the receptor α-chain C-terminal peptide and the site 1 insulin mimetic peptides. Biochemistry 48, 5492–5500 (2009).

    Article  CAS  PubMed  Google Scholar 

  46. Lata, S., Reichel, A., Brock, R., Tampe, R. & Piehler, J. High-affinity adaptors for switchable recognition of histidine-tagged proteins. J. Am. Chem. Soc. 127, 10205–10215 (2005).

    Article  CAS  PubMed  Google Scholar 

  47. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016).

  48. Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation (R Foundation for Statistical Computing, 2021).

  49. Auguie, B. & Antonov, A. gridExtra: Miscellaneous Functions for “Grid” Graphics (R Foundation for Statistical Computing, 2017).

  50. R Core Team. RStudio: Integrated Development for R (R Foundation for Statistical Computing, 2020).

  51. Rajapaksha, H. & Forbes, B. E. Ligand-binding affinity at the insulin receptor isoform-A and subsequent IR-A tyrosine phosphorylation kinetics are important determinants of mitogenic biological outcomes. Front. Endocrinol. 6, 107 (2015).

    Article  Google Scholar 

  52. Salic, A. & Mitchison, T. J. A chemical method for fast and sensitive detection of DNA synthesis in vivo. Proc. Natl Acad. Sci. USA 105, 2415–2420 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Gauguin, L. et al. Structural basis for the lower affinity of the insulin-like growth factors for the insulin receptor. J. Biol. Chem. 283, 2604–2613 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Disotuar, M. M. et al. Facile synthesis of insulin fusion derivatives through sortase A ligation. Acta Pharm. Sin. B 11, 2719–2725 (2020).

  55. Hayes, D. B., Laue, T. & Philo, J. SEDNTERP http://www.jphilo.mailway.com/sednterp.htm (1995).

  56. Schuck, P., Perugini, M. A., Gonzales, N. R., Howlett, G. J. & Schubert, D. Size-distribution analysis of proteins by analytical ultracentrifugation: strategies and application to model systems. Biophys. J. 82, 1096–1111 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Brautigam, C. A. Calculations and publication-quality illustrations for analytical ultracentrifugation data. Methods Enzymol. 562, 109–133 (2015).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank B.M. Olivera for cone snail collection and identification and insightful discussions, P. Shen for advice with structure determination and editing, D. Timm for EM screening and data collection at the University of Utah Electron Microscopy Core Laboratory and Paula Flórez Salcedo for the illustration of the Conus kinoshitai shell. The support and resources from the Center for High Performance Computing and the High Throughput Genomics Core Facility at the University of Utah are gratefully acknowledged. Financial support was provided by the National Institutes of Health NIDDK (DK120430 to D.H.-C.C., DK127268 to C.P.H. and DK118082 to S.J.F.), NIGMS (GM125001 to D.H.-C.C.), Juvenile Diabetes Research Foundation (5-CDA-2018-572-A-N to D.H.-C.C. and 1-INO-2017-441-A-N to H.S.-H.), German Federal Ministry of Education and Research (BMBF) grant to the German Center for Diabetes Research (DZD e.V. to Ü.C.), Deutsche Forschungsgemeinschaft (DFG 251981924–TRR 83 to Ü.C. and DFG 347368302 to Ü.C. and T.G.) and the Australian National Health and Medical Research Council (APP1143546 to M.C.L. and B.E.F.). Support of M.C.L.’s research is also made possible at WEHI through Victorian State Government Operational Infrastructure Support and the Australian NHMRC Independent Research Institutes Infrastructure Support Scheme. H.S.-H. acknowledges fellowship support from the Villum Foundation (19063) and the Carlsberg Foundation (CF19-0445).

Author information

Author notes

  1. Theresia Gutmann

    Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

  2. Simon J. Fisher

    Present address: Department of Internal Medicine, University of Kentucky, Lexington, KY, USA

  3. These authors contributed equally: Xiaochun Xiong, Alan Blakely.

Authors and Affiliations

  1. Department of Pediatrics, Division of Endocrinology and Diabetes, Stanford University, Stanford, CA, USA

    Xiaochun Xiong, Yi Wolf Zhang & Danny Hung-Chieh Chou

  2. Department of Biochemistry, University of Utah, Salt Lake City, UT, USA

    Xiaochun Xiong, Alan Blakely, Jin Hwan Kim, Heidi L. Schubert, Yi Wolf Zhang, Debbie Eckert, Helena Safavi-Hemami, Christopher P. Hill & Danny Hung-Chieh Chou

  3. WEHI, Parkville, Victoria, Australia

    John G. Menting & Michael C. Lawrence

  4. Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, Australia

    John G. Menting & Michael C. Lawrence

  5. Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Munich, Germany

    Ingmar B. Schäfer

  6. Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA

    Rahul Agrawal & Simon J. Fisher

  7. Paul Langerhans Institute Dresden, Helmholtz Zentrum München, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

    Theresia Gutmann, Gizem Olay Artik & Ünal Coskun

  8. German Center for Diabetes Research (DZD), Neuherberg, Germany

    Theresia Gutmann, Gizem Olay Artik & Ünal Coskun

  9. Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

    Theresia Gutmann, Gizem Olay Artik & Ünal Coskun

  10. Discipline of Medical Biochemistry and Cell Biology, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, South Australia, Australia

    Carlie Delaine, Allanah Merriman & Briony E. Forbes

  11. Department of Membrane Biochemistry and Lipid Research, University Clinic Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

    Gizem Olay Artik & Ünal Coskun

  12. Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark

    Helena Safavi-Hemami

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  1. Xiaochun Xiong
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Contributions

X.X. designed, synthesized and purified insulin analogs. A.B. prepared cryo-EM samples, processed cryo-EM data and modeled atomic coordinates with input from I.B.S. A.B. performed analytical ultracentrifugation with D.E. J.H.K., Y.W.Z. and X.X. performed pAkt-based activity assays. J.G.M. and M.C.L. performed the ITC experiments. H.L.S. screened cryo-EM samples and collected data sets. T.G., G.O.A. and Ü.C. expressed and purified the IR ectodomain and performed nanoDSF experiments and western blots to assess signal transduction in Hep-G2 cells. C.D., A.M. and B.E.F. performed signal transduction western blots and DNA synthesis assays in L6 myoblasts. R.A. and S.J.F. performed in vivo glucose response assays. X.X., A.B., H.S.-H., C.P.H. and D.H.-C.C. interpreted data, generated figures and wrote the manuscript with significant input from M.C.L., Ü.C., I.B.S. and T.G. All authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Helena Safavi-Hemami, Christopher P. Hill or Danny Hung-Chieh Chou.

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Extended data

Extended Data Fig. 1 Precursor sequence alignment of venom insulins identified in this study.

Canonical arrangement of preproinsulins with N-terminal signal sequences (purple) followed by the B chain (blue), C-peptide region (black) and A chain (green). The signal sequence, C peptide(s), and additional black-colored residues are predicted to be cleaved during post-translational processing.

Extended Data Fig. 2 AKT phosphorylation activity of Vh-Ins-HTLQ and related analogs.

NIH 3T3 cells overexpressing IR-B were stimulated with insulin analogs and pAkt was quantified using a homogeneous time-resolved fluorescence assay. Error bars (s.e.m. of 4 biological replicates) are shown when larger than the symbols. Two substitutions on the B chain, GluB10 and LeuB20, were found to increase the relative activity of Vh-Ins-HTLQ. These substitutions were subsequently included in later stages of design of Vh-Ins molecules.

Source data

Extended Data Fig. 3 Vh-Ins-HSLQ at site 2.

Density is shown around Vh-Ins-HSLQ. Vh-Ins-HSLQ green, with Vh-Ins mutated residues relative to native human insulin shown in orange. Receptor FnIII-1 domain, purple. The only Vh-specific residue that approaches receptor at site 2 is GluB10, which has poor density. Nearby receptor side chains lack density but are shown explicitly for illustrative purposes.

Extended Data Fig. 4 Activity of Vh-Ins analogs with single-residue substitutions in the extended A-chain residues (A21-A24).

NIH 3T3 cells overexpressing IR-B were stimulated with insulin analogs and pAkt was quantified using a homogeneous time-resolved fluorescence assay. Error bars (s.e.m. of 4 biological replicates) are shown when larger than the symbols.

Source data

Extended Data Fig. 5 Vh-Ins-HALQ binding to IR and IGF-1R ectodomains.

a, NanoDSF monitoring of intrinsic protein fluorescence to determine the thermal conformational stability of IR-ECD (top) or IGF1R-ECD (bottom) in the presence of respective ligands in four-times molar excess. Apo-IR-ECD displays two detectable unfolding transitions Tmlow and Tmhigh at 59.2 °C and 63.2 °C, respectively (Supplementary Table 5). The presence of Vh-Ins-HALQ leads to a decrease in Tmlow to 56.3 °C indicating conformational changes induced by ligand binding similarly to insulin (Tmlow = 54.3 °C). Apo-IGF1R-ECD displays a single transition temperature Tmhigh, while binding to hIGF-I leads to an additional melting transition at 57.4 °C. No significant changes in unfolding transitions were observed for IR-ECD in the presence of hIGF-I or for IGF1R-ECD in the presence of Vh-Ins-HALQ or hIns as compared to the respective ligand-free ectodomains. b, MST with IR-ECD (left) and IGF1R-ECD (right) to determine dissociation constants of binding to respective ligands (Supplementary Table 6; n = 3, error bars show standard deviations).

Source data

Extended Data Fig. 6 Comparison of the two receptor protomers in the asymmetric conformation against previously reported structures.

Left, Vh-Ins:IR asymmetric state apolike protomer (blue) vs apo IR (PDBs 4ZXB). Right, the second Vh-Ins:IR protomer (pink) vs insulin-bound receptor (6PXV) following alignment on L1, CR, L2, and FnIII-1 domains.

Extended Data Fig. 7 Vh-Ins-HALQ signal transduction in Hep-G2 cells.

Signal transduction in Hep-G2 hepatoblastoma cells induced by Vh-Ins-HALQ and hIns at 10 or 50 nM was assessed by Western blot and densitometry (4-6 biological replicates for each condition). Phosphorylation-specific antibodies were used to detect phosphorylated IR (Y1150/115), IGF1R (Y980), AKT1 (S473), and MEK-1/2 (S217/221). Relative intensities of specific protein bands were normalized to the GAPDH loading control and then to the respective signal after 5 min of insulin treatment.

Source data

Extended Data Fig. 8 SV-AUC c(s) analysis of insulin analogs.

a, DOI (des octapeptide insulin) and hIns (human insulin) controls at a concentration of 100 µg/ml and 775 µg/ml respectively in phosphate buffer (137 mM NaCl, 2.7 mM KCl, 5.3 mM Na2HPO4, 1.8 mM KH2PO4, pH 7.4). b, Vh-Ins-HSLQ and Lispro both at 500 µg/ml in sterile insulin diluent (16 mg/ml glycerol, 1.6 mg/ml m-cresol, 0.65 mg/ml phenol, 3.8 mg/ml Na2HPO4, pH 7.4). c-d, Data fit and residuals for DOI, hIns, Vh-Ins-HSLQ and lispro, respectively. For clarity, some scans are omitted from the figures shown but all scans were used for the c(s) analysis. The interval between the scans shown in each panel is ~9 minutes.

Source data

Extended Data Fig. 9 SV-AUC of DOI and hIns in sterile diluent.

a, c(s) analysis of DOI and hIns in sterile diluent. For reference the Vh-Ins-HSLQ trace from Fig. S11 is shown. b-c, data fit and residuals for DOI and hIns in sterile diluent, respectively. hIns shows increasing concentrations at higher radii in early scans, indicative of aggregation during the experiment.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Note and Tables 1–6.

Reporting Summary

Supplementary Video 1

A series of volumes derived from 3D variability analysis show a range of flexible motion in one of the two receptor protomers. Models were built into the conformational extremes and used to create a series of molecular motions that were fit into the experimental density. In the asymmetric extreme, three Vh-Ins molecules are apparent in the complex, including one in a composite site 1/site 2 position. As the receptor moves toward the symmetric state, L1 and CR move away from their position near the composite site, and the separate density for both site 1- and site 2-bound Vh-Ins becomes apparent.

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Unprocessed western blots.

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Xiong, X., Blakely, A., Kim, J.H. et al. Symmetric and asymmetric receptor conformation continuum induced by a new insulin. Nat Chem Biol 18, 511–519 (2022). https://doi.org/10.1038/s41589-022-00981-0

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