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.

  • Article
  • Published:

Cryo-EM structure of islet amyloid polypeptide fibrils reveals similarities with amyloid-β fibrils

Nature Structural & Molecular Biology volume 27, pages 660–667 (2020)Cite this article

Subjects

Abstract

Amyloid deposits consisting of fibrillar islet amyloid polypeptide (IAPP) in pancreatic islets are associated with beta-cell loss and have been implicated in type 2 diabetes (T2D). Here, we applied cryo-EM to reconstruct densities of three dominant IAPP fibril polymorphs, formed in vitro from synthetic human IAPP. An atomic model of the main polymorph, built from a density map of 4.2-Å resolution, reveals two S-shaped, intertwined protofilaments. The segment 21-NNFGAIL-27, essential for IAPP amyloidogenicity, forms the protofilament interface together with Tyr37 and the amidated C terminus. The S-fold resembles polymorphs of Alzheimer’s disease (AD)-associated amyloid-β (Aβ) fibrils, which might account for the epidemiological link between T2D and AD and reports on IAPP–Aβ cross-seeding in vivo. The results structurally link the early-onset T2D IAPP genetic polymorphism (encoding Ser20Gly) with the AD Arctic mutation (Glu22Gly) of Aβ and support the design of inhibitors and imaging probes for IAPP fibrils.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Comparison of reconstructed IAPP polymorphs.
Fig. 2: Architecture of the main polymorph, PM1.
Fig. 3: Secondary structure and hydrogen bonding in PM1.
Fig. 4: Structural comparison of PM1 with fibrillar IAPP peptide and Aβ fibril models.

Similar content being viewed by others

Data availability

The structure of IAPP PM1 has been deposited in the Protein Data Bank under accession code PDB 6Y1A. The cryo-EM density maps have been deposited in the Electron Microscopy Data Bank under accession codes EMD-10669 (PM1), EMD-10670 (PM2) and EMD-10671 (PM3).

References

  1. Opie, E. L. On the relation of chronic interstitial pancreatitis to the islands of Langerhans and to diabetes mellitus. J. Exp. Med. 5, 397–428 (1901).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Jurgens, C. A. et al. β-cell loss and β-cell apoptosis in human type 2 diabetes are related to islet amyloid deposition. Am. J. Pathol. 178, 2632–2640 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Westermark, P., Andersson, A. & Westermark, G. T. Islet amyloid polypeptide, islet amyloid, and diabetes mellitus. Physiol. Rev. 91, 795–826 (2011).

    CAS  PubMed  Google Scholar 

  4. Wimalawansa, S. J. Amylin, calcitonin gene-related peptide, calcitonin, and adrenomedullin: a peptide superfamily. Crit. Rev. Neurobiol. 11, 167–239 (1997).

    CAS  PubMed  Google Scholar 

  5. Akter, R. et al. Islet amyloid polypeptide: structure, function, and pathophysiology. J. Diabetes Res. 2016, 2798269 (2016).

    PubMed  Google Scholar 

  6. Mukherjee, A., Morales-Scheihing, D., Butler, P. C. & Soto, C. Type 2 diabetes as a protein misfolding disease. Trends Mol. Med. 21, 439–449 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Cao, P., Abedini, A. & Raleigh, D. P. Aggregation of islet amyloid polypeptide: from physical chemistry to cell biology. Curr. Opin. Struct. Biol. 23, 82–89 (2013).

    CAS  PubMed  Google Scholar 

  8. Halban, P. A. et al. β-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment. J. Clin. Endocrinol. Metab. 99, 1983–1992 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Zraika, S. et al. Toxic oligomers and islet beta cell death: guilty by association or convicted by circumstantial evidence? Diabetologia 53, 1046–1056 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhang, S. et al. The pathogenic mechanism of diabetes varies with the degree of overexpression and oligomerization of human amylin in the pancreatic islet β cells. FASEB J. 28, 5083–5096 (2014).

    CAS  PubMed  Google Scholar 

  11. Masters, S. L. et al. Activation of the NLRP3 inflammasome by islet amyloid polypeptide provides a mechanism for enhanced IL-1β in type 2 diabetes. Nat. Immunol. 11, 897–904 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Rivera, J. F. et al. Human IAPP disrupts the autophagy/lysosomal pathway in pancreatic β-cells: protective role of p62-positive cytoplasmic inclusions. Cell Death Differ. 18, 415–426 (2011).

    CAS  PubMed  Google Scholar 

  13. Gupta, D. & Leahy, J. L. Islet amyloid and type 2 diabetes: overproduction or inadequate clearance and detoxification? J. Clin. Invest. 124, 3292–3294 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Casas, S. et al. Impairment of the ubiquitin–proteasome pathway is a downstream endoplasmic reticulum stress response induced by extracellular human islet amyloid polypeptide and contributes to pancreatic β-cell apoptosis. Diabetes 56, 2284–2294 (2007).

    CAS  PubMed  Google Scholar 

  15. Hull, R. L. et al. Amyloid formation in human IAPP transgenic mouse islets and pancreas, and human pancreas, is not associated with endoplasmic reticulum stress. Diabetologia 52, 1102–1111 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Janson, J., Ashley, R. H., Harrison, D., McIntyre, S. & Butler, P. C. The mechanism of islet amyloid polypeptide toxicity is membrane disruption by intermediate-sized toxic amyloid particles. Diabetes 48, 491–498 (1999).

    CAS  PubMed  Google Scholar 

  17. Paulsson, J. F. et al. High plasma levels of islet amyloid polypeptide in young with new-onset of type 1 diabetes mellitus. PLoS ONE 9, e93053 (2014).

    PubMed  PubMed Central  Google Scholar 

  18. Martinez-Valbuena, I. et al. Interaction of amyloidogenic proteins in pancreatic β cells from subjects with synucleinopathies. Acta Neuropathol. 135, 877–886 (2018).

    CAS  PubMed  Google Scholar 

  19. Oskarsson, M. E. et al. In vivo seeding and cross-seeding of localized amyloidosis: a molecular link between type 2 diabetes and Alzheimer’s disease. Am. J. Pathol. 185, 834–846 (2015).

    CAS  PubMed  Google Scholar 

  20. Moreno-Gonzalez, I. et al. Molecular interaction between type 2 diabetes and Alzheimer’s disease through cross-seeding of protein misfolding. Mol. Psychiatry 9, 1327–1334 (2017).

    Google Scholar 

  21. O’Nuallain, B., Williams, A. D., Westermark, P. & Wetzel, R. Seeding specificity in amyloid growth induced by heterologous fibrils. J. Biol. Chem. 279, 17490–17499 (2004).

    PubMed  Google Scholar 

  22. Kajava, A. V., Aebi, U. & Steven, A. C. The parallel superpleated β-structure as a model for amyloid fibrils of human amylin. J. Mol. Biol. 348, 247–252 (2005).

    CAS  PubMed  Google Scholar 

  23. Luca, S., Yau, W. M., Leapman, R. & Tycko, R. Peptide conformation and supramolecular organization in amylin fibrils: constraints from solid-state NMR. Biochemistry 46, 13505–13522 (2007).

    CAS  PubMed  Google Scholar 

  24. Wiltzius, J. J. W. et al. Atomic structure of the cross-β spine of islet amyloid polypeptide (amylin). Protein Sci. 17, 1467–1474 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Bedrood, S. et al. Fibril structure of human islet amyloid polypeptide. J. Biol. Chem. 287, 5235–5241 (2012).

    CAS  PubMed  Google Scholar 

  26. Alexandrescu, A. T. Amide proton solvent protection in amylin fibrils probed by quenched hydrogen-exchange NMR. PLoS ONE 8, e56467 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Weirich, F. et al. Structural characterization of fibrils from recombinant human islet amyloid polypeptide by solid-state NMR: the central FGAILS segment is part of the β-sheet core. PLoS ONE 11, e0161243 (2016).

    PubMed  PubMed Central  Google Scholar 

  28. Goldsbury, C. S. et al. Polymorphic fibrillar assembly of human amylin. J. Struct. Biol. 119, 17–27 (1997).

    CAS  PubMed  Google Scholar 

  29. Hutton, J. C. The internal pH and membrane potential of the insulin-secretory granule. Biochem. J. 204, 171–178 (1982).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, Z. & Schröder, G. F. Real-space refinement with DireX: from global fitting to side-chain improvements. Biopolymers 97, 687–697 (2012).

    CAS  PubMed  Google Scholar 

  31. Falkner, B. & Schröder, G. F. Cross-validation in cryo-EM-based structural modeling. Proc. Natl Acad. Sci. USA 110, 8930–8935 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Westermark, P., Engstrom, U., Johnson, K. H., Westermark, G. T. & Betsholtz, C. Islet amyloid polypeptide: pinpointing amino acid residues linked to amyloid fibril formation. Proc. Natl Acad. Sci. USA 87, 5036–5040 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Betsholtz, C. et al. Sequence divergence in a specific region of islet amyloid polypeptide (IAPP) explains differences in islet amyloid formation between species. FEBS Lett. 251, 261–264 (1989).

    CAS  PubMed  Google Scholar 

  34. Tenidis, K. et al. Identification of a penta- and hexapeptide of islet amyloid polypeptide (IAPP) with amyloidogenic and cytotoxic properties. J. Mol. Biol. 295, 1055–1071 (2000).

    CAS  PubMed  Google Scholar 

  35. Gremer, L. et al. Fibril structure of amyloid-β(1–42) by cryo–electron microscopy. Science 358, 116–119 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Röder, C. et al. Atomic structure of PI3-kinase SH3 amyloid fibrils by cryo-electron microscopy. Nat. Commun. 10, 3754 (2019).

    PubMed  PubMed Central  Google Scholar 

  37. Janson, J. et al. Increased risk of type 2 diabetes in Alzheimer’s disease. Diabetes 53, 474–481 (2004).

    CAS  PubMed  Google Scholar 

  38. Yang, Y. & Song, W. Molecular links between Alzheimer’s disease and diabetes mellitus. Neuroscience 250, 140–150 (2013).

    CAS  PubMed  Google Scholar 

  39. Colvin, M. T. et al. Atomic resolution structure of monomorphic Aβ42 amyloid fibrils. J. Am. Chem. Soc. 138, 9663–9674 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Wälti, M. A. et al. Atomic-resolution structure of a disease-relevant Aβ1–42 amyloid fibril. Proc. Natl Acad. Sci. USA 113, 4976–4984 (2016).

    Google Scholar 

  41. Tycko, R. Molecular structure of aggregated amyloid-β: insights from solid state nuclear magnetic resonance. Cold Spring Harb. Perspect. Med. 6, a024083 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. Xiao, Y. et al. Aβ1–42 fibril structure illuminates self-recognition and replication of amyloid in Alzheimer’s disease. Nat. Struct. Mol. Biol. 22, 499–505 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kollmer, M. et al. Cryo-EM structure and polymorphism of Aβ amyloid fibrils purified from Alzheimer’s brain tissue. Nat. Commun. 10, 4760 (2019).

    PubMed  PubMed Central  Google Scholar 

  44. Colvin, M. T. et al. Atomic resolution structure of monomorphic Aβ42 amyloid fibrils. J. Am. Chem. Soc. 138, 9663–9674 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Sakagashira, S. et al. Missense mutation of amylin gene (S20G) in Japanese NIDDM patients. Diabetes 45, 1279–1281 (1996).

    CAS  PubMed  Google Scholar 

  46. Seino, S. S20G mutation of the amylin gene is associated with type II diabetes in Japanese. Study Group of Comprehensive Analysis of Genetic Factors in Diabetes Mellitus. Diabetologia 44, 906–909 (2001).

    CAS  PubMed  Google Scholar 

  47. Meier, D. T. et al. The S20G substitution in hIAPP is more amyloidogenic and cytotoxic than wild-type hIAPP in mouse islets. Diabetologia 59, 2166–2171 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Cao, P. et al. Sensitivity of amyloid formation by human islet amyloid polypeptide to mutations at residue 20. J. Mol. Biol. 421, 282–295 (2012).

    CAS  PubMed  Google Scholar 

  49. Sakagashira, S. et al. S20G mutant amylin exhibits increased in vitro amyloidogenicity and increased intracellular cytotoxicity compared to wild-type amylin. Am. J. Pathol. 157, 2101–2109 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Ma, Z. et al. Enhanced in vitro production of amyloid-like fibrils from mutant (S20G) islet amyloid polypeptide. Amyloid 8, 242–249 (2001).

    CAS  PubMed  Google Scholar 

  51. Xu, W., Jiang, P. & Mu, Y. Conformation preorganization: effects of S20G mutation on the structure of human islet amyloid polypeptide segment. J. Phys. Chem. B 113, 7308–7314 (2009).

    CAS  PubMed  Google Scholar 

  52. Mirecka, E. A. et al. β-hairpin of islet amyloid polypeptide bound to an aggregation inhibitor. Sci. Rep. 6, 33474 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Nilsberth, C. et al. The ‘Arctic’ APP mutation (E693G) causes Alzheimer’s disease by enhanced Aβ protofibril formation. Nat. Neurosci. 4, 887–893 (2001).

    CAS  PubMed  Google Scholar 

  54. Padrick, S. B. & Miranker, A. D. Islet amyloid polypeptide: identification of long-range contacts and local order on the fibrillogenesis pathway. J. Mol. Biol. 308, 783–794 (2001).

    CAS  PubMed  Google Scholar 

  55. Chen, M. S. et al. Characterizing the assembly behaviors of human amylin: a perspective derived from C-terminal variants. Chem. Commun. 49, 1799–1801 (2013).

    CAS  Google Scholar 

  56. Yonemoto, I. T., Kroon, G. J. A., Dyson, H. J., Balch, W. E. & Kelly, J. W. Amylin proprotein processing generates progressively more amyloidogenic peptides that initially sample the helical state. Biochemistry 47, 9900–9910 (2008).

    CAS  PubMed  Google Scholar 

  57. Kruger, D. F. & Gloster, M. A. Pramlintide for the treatment of insulin-requiring diabetes mellitus: rationale and review of clinical data. Drugs 64, 1419–1432 (2004).

    CAS  PubMed  Google Scholar 

  58. Roth, J. D. et al. Leptin responsiveness restored by amylin agonism in diet-induced obesity: evidence from nonclinical and clinical studies. Proc. Natl Acad. Sci. USA 105, 7257–7262 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Wang, H., Abedini, A., Ruzsicska, B. & Raleigh, D. P. Rationally designed, nontoxic, nonamyloidogenic analogues of human islet amyloid polypeptide with improved solubility. Biochemistry 53, 5876–5884 (2014).

    CAS  PubMed  Google Scholar 

  60. Zheng, S. Q., Palovcak, E., Armache, J.-P., Cheng, Y. & Agard, D. A. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  62. He, S. & Scheres, S. H. W. Helical reconstruction in RELION. J. Struct. Biol. 198, 163–176 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Scheres, S. H. W. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Trabuco, L. G., Villa, E., Schreiner, E., Harrison, C. B. & Schulten, K. Molecular dynamics flexible fitting: a practical guide to combine cryo-electron microscopy and X-ray crystallography. Methods 49, 174–180 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Trabuco, L. G., Villa, E., Mitra, K., Frank, J. & Schulten, K. Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure 16, 673–683 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Brunger, A. T. et al. Crystallography & NMR system: a new software suite for macromolecular structure determination. Acta Crystallogr. D Biol. Crystallogr. 54, 905–921 (1998).

    CAS  PubMed  Google Scholar 

  70. Brunger, A. T. Version 1.2 of the Crystallography and NMR system. Nat. Protoc. 2, 2728–2733 (2007).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  74. Krivov, G. G., Shapovalov, M. V. & Dunbrack, R. L. Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77, 778–795 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 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  PubMed  Google Scholar 

  76. 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).

    CAS  PubMed  Google Scholar 

  77. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).

    Google Scholar 

  78. Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 8, 3237–3256 (2012).

    Google Scholar 

  79. MacKerell, A. D. et al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102, 3586–3616 (1998).

    CAS  PubMed  Google Scholar 

  80. Beglov, D. & Roux, B. Finite representation of an infinite bulk system: solvent boundary potential for computer simulations. J. Chem. Phys. 100, 9050–9063 (1994).

    CAS  Google Scholar 

  81. Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).

    CAS  Google Scholar 

  82. Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511–519 (1984).

    Google Scholar 

  83. Hoover, W. G. Canonical dynamics: equilibrium phase-space distributions. Phys. Rev. A Gen. Phys. 31, 1695–1697 (1985).

    CAS  PubMed  Google Scholar 

  84. Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981).

    CAS  Google Scholar 

  85. Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993).

    CAS  Google Scholar 

Download references

Acknowledgements

We thank P.J. Peters and C. López-Iglesias for advice and helpful discussions, H. Duimel for help with sample preparation and the M4I Division of Nanoscopy of Maastricht University for microscope access and support. The authors gratefully acknowledge the computing time granted by the Jülich Aachen Research Alliance High-Performance Computing (JARA-HPC) Vergabegremium and VSR commission on the supercomputer JURECA at Forschungszentrum Jülich. We acknowledge support from a European Research Council (ERC) Consolidator grant (no. 726368; W.H.), the Alzheimer Forschung Initiative e.V. and Alzheimer Nederland (project no. 19082CB; R.B.G.R. and G.F.S.), the Russian Science Foundation (RSF; project no. 20-64-46027; L.G. and D.W.) and the Helmholtz Association Initiative and Networking Fund (project no. ZT-I-0003; K.R.P. and G.F.S.).

Author information

Author notes

  1. These authors contributed equally: Christine Röder, Tatsiana Kupreichyk.

Authors and Affiliations

  1. Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany

    Christine Röder, Tatsiana Kupreichyk, Lothar Gremer, Luisa U. Schäfer, Karunakar R. Pothula, Dieter Willbold, Wolfgang Hoyer & Gunnar F. Schröder

  2. Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany

    Christine Röder, Tatsiana Kupreichyk, Lothar Gremer, Luisa U. Schäfer, Karunakar R. Pothula, Dieter Willbold, Wolfgang Hoyer & Gunnar F. Schröder

  3. Institut für Physikalische Biologie, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Christine Röder, Tatsiana Kupreichyk, Lothar Gremer, Dieter Willbold & Wolfgang Hoyer

  4. The Multimodal Molecular Imaging Institute, Maastricht University, Maastricht, the Netherlands

    Raimond B. G. Ravelli

  5. Physics Department, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Gunnar F. Schröder

Authors
  1. Christine Röder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tatsiana Kupreichyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lothar Gremer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luisa U. Schäfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Karunakar R. Pothula
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Raimond B. G. Ravelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dieter Willbold
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wolfgang Hoyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gunnar F. Schröder
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.G., W.H., T.K. and G.F.S. conceived the study. T.K. and L.G. performed and analyzed fibril preparation and AFM experiments. R.G.B.R. performed cryo-EM experiments and the initial data analysis. C.R., T.K. and G.F.S. performed image processing and initial reconstruction. C.R. and G.F.S. performed reconstruction, model building and refinement. L.U.S., K.R.P. and G.F.S. performed molecular dynamics simulations and structure fitting. C.R., T.K., G.F.S., W.H., L.G., K.R.P. and L.U.S. wrote the manuscript. D.W. and all other authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Wolfgang Hoyer or Gunnar F. Schröder.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Peer reviewer reports are available. Inês Chen was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Comparison of described IAPP polymorphs.

a, Single fibril cut-outs of polymorphs PM1, PM2 and PM3 from AFM images (top row) and cryo-EM micrographs (bottom row); single box size is 100 × 250 nm. b, Height profiles of individual fibrils extracted from AFM images. c, Height distribution histogram, showing the highest number of counts for the plane background surface around 0 nm and a distinct peak around 2.2 nm. The peak around 2.2 nm includes both PM1 and PM2 which are non-distinguishable in sense of height distribution. Moreover, a pronounced shoulder on the right corresponds to the presence of lower amounts of PM3 as well as the overlaps of single PM1/PM2 fibrils. For the height distribution analysis, histograms from six height images of 5 × 5 µm size and a resolution of 1024 × 1024 pixels were obtained, binned and presented in one graph. An example of the image used can be seen in Supplementary Figure 2.

Extended Data Fig. 2 Overview of IAPP polymorphs.

a, Typical height profile AFM image used for polymorph distribution analysis. b, Cryo-EM micrographs showing 370 × 370 nm areas. c, AFM overview images showing 1 × 1 µm areas. Arrows indicate the presence of PM1 (red), PM2 (green) and PM3 (blue).

Extended Data Fig. 3 DireX analysis of polymorph 2 (PM2).

The table contains the Cwork and Cfree values from DireX fitting of 21-residue-long sequence snippets (black box) of IAPP in both possible Cα-chain directions into a density layer of PM2 together with the respective amino acid sequence. The results are ranked according to their Cfree values. Highlighted (green box) is the most favorable sequence fit. Atomic models of the four most favorable sequence snippets are shown at the bottom. Note that some models, for example model 2, can be excluded since they are incompatible with the disulfide bond between residues Cys2 and Cys7.

Extended Data Fig. 4 Hydrophobicity plot of the fibril displayed as top view.

Hydrophobicity levels of the IAPP polymorph 1 (PM1) fibril are colored according to Kyte-Doolittle in the hydrophobicity score range −4.5 (white) to 4.5 (gold). One hydrophobic cluster spans the entire diagonal of the fibril cross-section. This hydrophobic streak is surrounded by highly ordered polar clusters.

Extended Data Fig. 5 Results of molecular dynamics simulations of IAPP polymorph 1 (PM1).

Superimposed snapshots from a 250 ns simulation displaying only the backbone (a) or all atoms (except for solvent and hydrogen) (b). c, Showing the RMSD from the deposited structure of PM1 (PDB ID 6Y1A) for two 250 ns simulations (black and grey lines, respectively). d, Showing the RMSD of a single chain from the deposited structure during the two 250 ns simulations. e, Showing the atomic root mean square fluctuations (RMSF) for each residue calculated over each 250 ns simulation.

Extended Data Fig. 6 FSC Analysis of polymorph 1 (PM1).

FSC curves from the even/odd test (solid black) from the gold-standard refinement yields a resolution of 4.2 Å (using the 0.143 criterion). The even/odd FSC curve is fitted (red) with the model function 1/(1+exp((x-A)/B)) (with A = 0.1947 and B = 0.026) to obtain a more robust resolution estimate.

Extended Data Fig. 7 FSC analysis of polymorph 2 (PM2).

FSC curves from the even/odd test (solid black) from the gold-standard refinement yields a resolution of 4.2 Å (using the 0.143 criterion). The even/odd FSC curve is fitted (green) with the model function 1/(1+exp((x-A)/B))) (with A = 0.194789 and B = 0.02427) to obtain a more robust resolution estimate.

Extended Data Fig. 8 FSC analysis of Polymorph 3 (PM3).

FSC curves from the even/odd test (solid black) from the gold-standard refinement yields a resolution of 8.1 Å (using the 0.143 criterion). The even/odd FSC curve is fitted (light blue) with the model function 1/(1+exp((x-A)/B)) (with A = 0.0772 and B = 0.0256) to obtain a more robust resolution estimate.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Röder, C., Kupreichyk, T., Gremer, L. et al. Cryo-EM structure of islet amyloid polypeptide fibrils reveals similarities with amyloid-β fibrils. Nat Struct Mol Biol 27, 660–667 (2020). https://doi.org/10.1038/s41594-020-0442-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41594-020-0442-4

This article is cited by

Search

Advanced 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