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Alkali ion influence on structure and stability of fibrillar amyloid-β oligomers

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Abstract

Alzheimer’s disease is characterized by the aggregation of Amyloid-β (Aβ) peptide into oligomers, fibrils and plaques. Many factors influencing this process as well as the stability of the various Aβ aggregates are known to date, and include the concentration and type of metal ions. Most experimental and theoretical studies have concentrated on heavy metal ions, like Fe2+, Zn2+, or Cu2+, while the smaller alkali ions Li+, Na+, and K+ have not gained much attention notwithstanding their role and ubiquity in physiological environments. In this work, we applied atomistic molecular dynamics simulations to investigate the potential role of these alkali ions in stabilizing fibrillar Aβ oligomers of different size and topology, i.e., single and double filament systems comprising 3–24 peptide chains per filament. We find a pronounced difference on the molecular level in the interaction behavior with free carboxylate groups of the Aβ oligomer: Li+ forms stable bridged interactions, whereas K+ interacts more transiently and lacks bridging. The behavior of Na+ is in between, so that this ion–protein interaction obeys the renowned Hofmeister series. These differences are also reflected in the ability of the alkali ions to stabilize the oligomer secondary structure. The stabilizing effect is most pronounced for the smaller fibrillar oligomers, suggesting that the type of alkali ion critically affects the initial stages of fibril formation. Our findings thus offer a molecular explanation for the observation that the polymorphisms of Aβ fibril structures are caused by differences in the surrounding ionic environment.

Influence of alkali ions on the structure and stability of fibrillar amyloid-β oligomers

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References

  1. Haass C, Selkoe DJ (2007) Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer’s amyloid beta-peptide. Nat Rev Mol Cell Biol 8(2):101–112. https://doi.org/10.1038/nrm2101

    Article  CAS  PubMed  Google Scholar 

  2. Selkoe DJ, Hardy J (2016) The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med 8(6):595–608. https://doi.org/10.15252/emmm.201606210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Taylor CA, Greenlund SF, McGuire LC, Lu H, Croft JB (2017) Deaths from Alzheimer’s disease—United States, 1999–2014. MMWR Morb Mortal Wkly Rep 66(20):521–526. https://doi.org/10.15585/mmwr.mm6620a1

    Article  PubMed  PubMed Central  Google Scholar 

  4. Stewart KL, Radford SE (2017) Amyloid plaques beyond Aβ: a survey of the diverse modulators of amyloid aggregation. Biophys Rev 9(4):405–419. https://doi.org/10.1007/s12551-017-0271-9

  5. Klement K, Wieligmann K, Meinhardt J, Hortschansky P, Richter W, Fändrich M (2007) Effect of different salt ions on the propensity of aggregation and on the structure of Alzheimer’s abeta(1-40) amyloid fibrils. J Mol Biol 373(5):1321–1333. https://doi.org/10.1016/j.jmb.2007.08.068

    Article  CAS  PubMed  Google Scholar 

  6. Hatami A, Monjazeb S, Milton S, Glabe CG (2017) Familial Alzheimer’s disease mutations within the Amyloid precursor protein Alter the aggregation and conformation of the Amyloid-beta peptide. J Biol Chem 292(8):3172–3185. https://doi.org/10.1074/jbc.M116.755264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wärmländer S, Tiiman A, Abelein A, Luo J, Jarvet J, Söderberg KL, Danielsson J, Gräslund A (2013) Biophysical studies of the amyloid beta-peptide: interactions with metal ions and small molecules. Chembiochem Eur J Chem Biol 14(14):1692–1704. https://doi.org/10.1002/cbic.201300262

    Article  CAS  Google Scholar 

  8. Okur HI, Hladilkova J, Rembert KB, Cho Y, Heyda J, Dzubiella J, Cremer PS, Jungwirth P (2017) Beyond the Hofmeister series: ion-specific effects on proteins and their biological functions. J Phys Chem B 121(9):1997–2014. https://doi.org/10.1021/acs.jpcb.6b10797

    Article  CAS  PubMed  Google Scholar 

  9. Mantyh PW, Ghilardi JR, Rogers S, DeMaster E, Allen CJ, Stimson ER, Maggio JE (1993) Aluminum, iron, and zinc ions promote aggregation of physiological concentrations of beta-amyloid peptide. J Neurochem 61(3):1171–1174

    Article  CAS  PubMed  Google Scholar 

  10. Mannini B, Habchi J, Chia S, Ruggeri FS, Perni M, Knowles TPJ, Dobson CM, Vendruscolo M (2018) Stabilization and characterization of cytotoxic Aβ40 oligomers isolated from an aggregation reaction in the presence of zinc ions. ACS Chem Neurosci 9(12):2959–2971. https://doi.org/10.1021/acschemneuro.8b00141

  11. Parthasarathy S, Long F, Miller Y, Xiao Y, McElheny D, Thurber K, Ma B, Nussinov R, Ishii Y (2011) Molecular-level examination of Cu2+ binding structure for amyloid fibrils of 40-residue Alzheimer’s beta by solid-state NMR spectroscopy. J Am Chem Soc 133(10):3390–3400. https://doi.org/10.1021/ja1072178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kim AC, Lim S, Kim YK (2018) Metal ion effects on Aβ and tau aggregation. Int J Mol Sci 19(1):128. https://doi.org/10.3390/ijms19010128

    Article  CAS  PubMed Central  Google Scholar 

  13. Atwood CS, Moir RD, Huang X, Scarpa RC, Bacarra NM, Romano DM, Hartshorn MA, Tanzi RE, Bush AI (1998) Dramatic aggregation of Alzheimer abeta by Cu(II) is induced by conditions representing physiological acidosis. J Biol Chem 273(21):12817–12826

    Article  CAS  PubMed  Google Scholar 

  14. Raffa DF, Rauk A (2007) Molecular dynamics study of the beta amyloid peptide of Alzheimer’s disease and its divalent copper complexes. J Phys Chem B 111(14):3789–3799. https://doi.org/10.1021/jp0689621

    Article  CAS  PubMed  Google Scholar 

  15. Miller Y, Ma B, Nussinov R (2010) Zinc ions promote Alzheimer Aβ aggregation via population shift of polymorphic states. Proc Natl Acad Sci USA 107(21):9490–9495. https://doi.org/10.1073/pnas.0913114107

    Article  PubMed  PubMed Central  Google Scholar 

  16. Abelein A, Jarvet J, Barth A, Graslund A, Danielsson J (2016) Ionic strength modulation of the free energy landscape of Aβ40 peptide fibril formation. J Am Chem Soc 138(21):6893–6902. https://doi.org/10.1021/jacs.6b04511

    Article  CAS  PubMed  Google Scholar 

  17. Adamcik J, Mezzenga R (2011) Adjustable twisting periodic pitch of amyloid fibrils. Soft Matter 7(11):5437–5443. https://doi.org/10.1039/c1sm05382e

    Article  CAS  Google Scholar 

  18. Kurouski D, Lauro W, Lednev IK (2010) Amyloid fibrils are “alive”: spontaneous refolding from one polymorph to another. Chem Commun 46(24):4249–4251. https://doi.org/10.1039/b926758a

    Article  CAS  Google Scholar 

  19. Hu D, Zhao W, Zhu Y, Ai H, Kang B (2017) Bead-level characterization of early-stage Amyloid β42 aggregates: nuclei and ionic concentration effects. Chemistry 23(64):16257–16273. https://doi.org/10.1002/chem.201702388

    Article  CAS  PubMed  Google Scholar 

  20. Smith MD, Cruz L (2013) Effect of ionic aqueous environments on the structure and dynamics of the a β21-30 fragment: a molecular-dynamics study. J Phys Chem B 117(22):6614–6624. https://doi.org/10.1021/jp312653h

    Article  CAS  PubMed  Google Scholar 

  21. Smith MD, Cruz L (2013) Structural dynamics of Iowa, Dutch, and Arctic mutations of the 21-30 fragment of Amyloid Beta under aqueous salt environments. Biophys J 104(2):226a–226a. https://doi.org/10.1016/j.bpj.2012.11.1277

    Article  Google Scholar 

  22. Zidar J, Merzel F (2011) Probing Amyloid-Beta fibril stability by increasing ionic strengths. J Phys Chem B 115(9):2075–2081. https://doi.org/10.1021/jp109025b

    Article  CAS  PubMed  Google Scholar 

  23. Rosenlehner K, Schade B, Bottcher C, Jäger CM, Clark T, Heinemann FW, Hirsch A (2010) Sodium effect on self-organization of amphiphilic carboxylates: formation of structured micelles and superlattices. Chemistry 16(31):9544–9554. https://doi.org/10.1002/chem.201001150

    Article  CAS  PubMed  Google Scholar 

  24. Peters JP, Maher LJ (2010) DNA curvature and flexibility in vitro and in vivo. Q Rev Biophys 43(1):23–63. https://doi.org/10.1017/S0033583510000077

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kahler A, Sticht H, Horn AHC (2013) Conformational stability of fibrillar amyloid-beta oligomers via protofilament pair formation - a systematic computational study. PLoS One 8(7):e70521. https://doi.org/10.1371/journal.pone.0070521

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lührs T, Ritter C, Adrian M, Riek-Loher D, Bohrmann B, Döbeli H, Schubert D, Riek R (2005) 3D structure of Alzheimer’s amyloid-β(1-42) fibrils. Proc Natl Acad Sci USA 102(48):17342–17347. https://doi.org/10.1073/pnas.0506723102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tripos (1991–2008) Sybyl7.3. St. Louis, USA

  28. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935. https://doi.org/10.1063/1.445869

    Article  CAS  Google Scholar 

  29. Auffinger P, Cheatham TE, Vaiana AC (2007) Spontaneous formation of KCl aggregates in biomolecular simulations: a force field issue? J Chem Theory Comput 3(5):1851–1859. https://doi.org/10.1021/ct700143s

    Article  CAS  PubMed  Google Scholar 

  30. Åqvist J (1990) Ion water interaction potentials derived from free-energy perturbation simulations. J Phys Chem 94(21):8021–8024. https://doi.org/10.1021/j100384a009

    Article  Google Scholar 

  31. Dang LX (1995) Mechanism and thermodynamics of ion selectivity in aqueous-solutions of 18-Crown-6 ether—a molecular-dynamics study. J Am Chem Soc 117(26):6954–6960. https://doi.org/10.1021/Ja00131a018

    Article  CAS  Google Scholar 

  32. Joung IS, Cheatham 3rd TE (2008) Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J Phys Chem B 112(30):9020–9041. https://doi.org/10.1021/jp8001614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Cheatham TE, Cieplak P, Kollman PA (1999) A modified version of the Cornell et al. force field with improved sugar pucker phases and helical repeat. J Biomol Struct Dyn 16(4):845–862

    Article  CAS  PubMed  Google Scholar 

  34. Cieplak P, Cornell WD, Bayly C, Kollman PA (1995) Application of the multimolecule and multiconformational RESP methodology to biopolymers: charge derivation for DNA, RNA, and proteins. J Comput Chem 16(11):1357–1377. https://doi.org/10.1002/jcc.540161106

    Article  CAS  Google Scholar 

  35. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) A 2nd generation force-field for the simulation of proteins, nucleic-acids, and organic-molecules. J Am Chem Soc 117(19):5179–5197. https://doi.org/10.1021/Ja00124a002

    Article  CAS  Google Scholar 

  36. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins 65(3):712–725. https://doi.org/10.1002/Prot.21123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Nasica-Labouze J, Nguyen PH, Sterpone F, Berthoumieu O, Buchete NV, Cote S, De Simone A, Doig AJ, Faller P, Garcia A, Laio A, Li MS, Melchionna S, Mousseau N, Mu Y, Paravastu A, Pasquali S, Rosenman DJ, Strodel B, Tarus B, Viles JH, Zhang T, Wang C, Derreumaux P (2015) Amyloid beta protein and Alzheimer’s disease: when computer simulations complement experimental studies. Chem Rev 115(9):3518–3563. https://doi.org/10.1021/cr500638n

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Okumura H, Itoh SG (2014) Amyloid fibril disruption by ultrasonic cavitation: nonequilibrium molecular dynamics simulations. J Am Chem Soc 136(30):10549–10552. https://doi.org/10.1021/ja502749f

    Article  CAS  PubMed  Google Scholar 

  39. Socher E, Sticht H, Horn AHC (2014) The conformational stability of nonfibrillar amyloid-β peptide oligomers critically depends on the C-terminal peptide length. ACS Chem Neurosci 5(3):161–167. https://doi.org/10.1021/cn400208r

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Berhanu WM, Alred EJ, Hansmann UH (2015) Stability of Osaka mutant and wild-type fibril models. J Phys Chem B 119(41):13063–13070. https://doi.org/10.1021/acs.jpcb.5b07987

    Article  CAS  PubMed  Google Scholar 

  41. Kassler K, Horn AHC, Sticht H (2010) Effect of pathogenic mutations on the structure and dynamics of Alzheimer’s Aβ 42-amyloid oligomers. J Mol Model 16(5):1011–1020. https://doi.org/10.1007/s00894-009-0611-1

    Article  CAS  PubMed  Google Scholar 

  42. Söldner CA, Sticht H, Horn AHC (2017) Role of the N-terminus for the stability of an amyloid-β fibril with three-fold symmetry. PLoS One 12(10):e0186347. https://doi.org/10.1371/journal.pone.0186347

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Miyamoto S, Kollman PA (1992) SETTLE: an analytical version of the SHAKE and RATTLE algorithm for rigid water models. J Comput Chem 13:952–962. https://doi.org/10.1002/jcc.540130805

    Article  CAS  Google Scholar 

  44. Berendsen HJC, Postma JPM, van Gunsteren WF, Dinola A, Haak JR (1984) Molecular-dynamics with coupling to an external Bath. J Chem Phys 81(8):3684–3690. https://doi.org/10.1063/1.448118

    Article  CAS  Google Scholar 

  45. Case DA, Babin V, Berryman JT, Betz RM, Cai Q, Cerutti DS, Cheatham TE, Darden TA, Duke RE, Gohlke H, Goetz AW, Gusarov S, Homeyer N, Janowski P, Kaus J, Kolossváry I, Kovalenko A, Lee TS, LeGrand S, Luchko T, Luo R, Madej B, Merz KM, Paesani F, Roe DR, Roitberg A, Sagui C, Salomon-Ferrer R, Seabra G, Simmerling CL, Smith W, Swails J, Walker RC, Wang J, Wolf RM, Wu X, Kollman PA (2014) AMBER14. University of California, San Francisco

    Google Scholar 

  46. Götz AW, Williamson MJ, Xu D, Poole D, Le Grand S, Walker RC (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J Chem Theory Comput 8(5):1542–1555. https://doi.org/10.1021/ct200909j

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J Chem Theory Comput 9(9):3878–3888. https://doi.org/10.1021/ct400314y

    Article  CAS  PubMed  Google Scholar 

  48. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095. https://doi.org/10.1021/ct400341p

    Article  CAS  PubMed  Google Scholar 

  49. Kabsch W, Sander C (1983) Dictionary of protein secondary structure - pattern-recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637. https://doi.org/10.1002/bip.360221211

    Article  CAS  PubMed  Google Scholar 

  50. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph Model 14(1):33–38. https://doi.org/10.1016/0263-7855(96)00018-5

    Article  CAS  Google Scholar 

  51. Horn AHC, Sticht H (2010) Amyloid-β42 oligomer structures from fibrils: a systematic molecular dynamics study. J Phys Chem B 114(6):2219–2226. https://doi.org/10.1021/jp100023q

    Article  CAS  PubMed  Google Scholar 

  52. Okumura H, Itoh SG (2016) Structural and fluctuational difference between two ends of Aβ amyloid fibril: MD simulations predict only one end has open conformations. Sci Rep 6:38422. https://doi.org/10.1038/srep38422

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Beierlein FR, Clark T, Braunschweig B, Engelhardt K, Glas L, Peukert W (2015) Carboxylate ion pairing with alkali-metal ions for beta-Lactoglobulin and its role on aggregation and interfacial adsorption. J Phys Chem B 119(17):5505–5517. https://doi.org/10.1021/acs.jpcb.5b01944

    Article  CAS  PubMed  Google Scholar 

  54. Zheng J, Jang H, Ma B, Tsai CJ, Nussinov R (2007) Modeling the Alzheimer Aβ17-42 fibril architecture: tight intermolecular sheet-sheet association and intramolecular hydrated cavities. Biophys J 93(9):3046–3057. https://doi.org/10.1529/biophysj.107.110700

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Kherb J, Flores SC, Cremer PS (2012) Role of carboxylate side chains in the cation Hofmeister series. J Phys Chem B 116(25):7389–7397. https://doi.org/10.1021/jp212243c

    Article  CAS  PubMed  Google Scholar 

  56. Jakobsson E, Arguello-Miranda O, Chiu SW, Fazal Z, Kruczek J, Nunez-Corrales S, Pandit S, Pritchet L (2017) Towards a unified understanding of Lithium action in basic biology and its significance for applied biology. J Membrane Biol 250(6):587–604. https://doi.org/10.1007/s00232-017-9998-2

    Article  CAS  Google Scholar 

  57. Alda M (2015) Lithium in the treatment of bipolar disorder: pharmacology and pharmacogenetics. Mol Psychiatry 20(6):661–670. https://doi.org/10.1038/mp.2015.4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Esler WP, Stimson ER, Jennings JM, Vinters HV, Ghilardi JR, Lee JP, Mantyh PW, Maggio JE (2000) Alzheimer’s disease amyloid propagation by a template-dependent dock-lock mechanism. Biochemistry 39(21):6288–6295

    Article  CAS  PubMed  Google Scholar 

  59. Nguyen PH, Li MS, Stock G, Straub JE, Thirumalai D (2007) Monomer adds to preformed structured oligomers of Aβ-peptides by a two-stage dock-lock mechanism. Proc Natl Acad Sci USA 104(1):111–116. https://doi.org/10.1073/pnas.0607440104

    Article  CAS  PubMed  Google Scholar 

  60. Kumar S, Nussinov R (2002) Close-range electrostatic interactions in proteins. Chembiochem Eur J Chem Biol 3(7):604–617. https://doi.org/10.1002/1439-7633(20020703)3:7<604::AID-CBIC604>3.0.CO;2-X

    Article  CAS  Google Scholar 

  61. Annamalai K, Gührs KH, Koehler R, Schmidt M, Michel H, Loos C, Gaffney PM, Sigurdson CJ, Hegenbart U, Schonland S, Fändrich M (2016) Polymorphism of Amyloid fibrils in vivo. Angew Chem 55(15):4822–4825. https://doi.org/10.1002/anie.201511524

    Article  CAS  Google Scholar 

  62. Lu JX, Qiang W, Yau WM, Schwieters CD, Meredith SC, Tycko R (2013) Molecular structure of β-amyloid fibrils in Alzheimer’s disease brain tissue. Cell 154(6):1257–1268. https://doi.org/10.1016/j.cell.2013.08.035

    Article  CAS  PubMed  Google Scholar 

  63. Paravastu AK, Leapman RD, Yau WM, Tycko R (2008) Molecular structural basis for polymorphism in Alzheimer’s β-amyloid fibrils. Proc Natl Acad Sci USA 105(47):18349–18354. https://doi.org/10.1073/pnas.0806270105

    Article  PubMed  PubMed Central  Google Scholar 

  64. Petkova AT, Yau WM, Tycko R (2006) Experimental constraints on quaternary structure in Alzheimer’s β-amyloid fibrils. Biochemistry 45(2):498–512. https://doi.org/10.1021/bi051952q

    Article  CAS  PubMed  Google Scholar 

  65. Xiao Y, Ma B, McElheny D, Parthasarathy S, Long F, Hoshi M, Nussinov R, Ishii Y (2015) Aβ(1-42) fibril structure illuminates self-recognition and replication of amyloid in Alzheimer’s disease. Nat Struct Mol Biol 22(6):499–505. https://doi.org/10.1038/nsmb.2991

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Walti MA, Ravotti F, Arai H, Glabe CG, Wall JS, Bockmann A, Guntert P, Meier BH, Riek R (2016) Atomic-resolution structure of a disease-relevant Aβ(1-42) amyloid fibril. Proc Natl Acad Sci USA 113(34):E4976–E4984. https://doi.org/10.1073/pnas.1600749113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Colvin MT, Silvers R, Ni QZ, Can TV, Sergeyev I, Rosay M, Donovan KJ, Michael B, Wall J, Linse S, Griffin RG (2016) Atomic resolution structure of Monomorphic Aβ42 Amyloid fibrils. J Am Chem Soc 138(30):9663–9674. https://doi.org/10.1021/jacs.6b05129

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Gremer L, Scholzel D, Schenk C, Reinartz E, Labahn J, Ravelli RBG, Tusche M, Lopez-Iglesias C, Hoyer W, Heise H, Willbold D, Schroder GF (2017) Fibril structure of amyloid-β(1-42) by cryo-electron microscopy. Science 358(6359):116–119. https://doi.org/10.1126/science.aao2825

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Schmidt M, Rohou A, Lasker K, Yadav JK, Schiene-Fischer C, Fandrich M, Grigorieff N (2015) Peptide dimer structure in an Aβ(1-42) fibril visualized with cryo-EM. Proc Natl Acad Sci USA 112(38):11858–11863. https://doi.org/10.1073/pnas.1503455112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was supported by the Alzheimer Forschung Initiative e.V. (AFI) via a Pilot Grant (#12858). A.H.C.H. thanks the Leibniz-Rechenzentrum (LRZ) München for granting access to its GPU cluster and Heinrich Sticht for many fruitful discussions. Additionally, the authors gratefully acknowledge the compute resources (GPU and CPU cluster) and support provided by the Erlangen Regional Computing Center (RRZE).

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    Danyil Huraskin & Anselm H. C. Horn

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Correspondence to Anselm H. C. Horn.

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Online Resource 1

Document (pdf format) with representations of the final simulation structures of a2x03, a2x06, a2x24, a1x06, and a1x24 (Fig. S1–S5), flexibility difference at the fibril ends (Fig. S6), and plots of the evolution of the ion coordination in a2x12 to E22 (Fig. S7). (PDF 1435 kb)

Online Resource 2

Movie (mpg format) of MD simulation trajectory of 150 mM Na+ with a2x12 (JC parameters, run1, 200 ns); for clarity, only one filament is shown in secondary structure representation with E11, E22, D23, and A42 depicted in red sticks and Na+ ions within 5 Å of the filament as blue spheres. The protein movement is smoothed via VMD. (MPG 5389 kb)

Online Resource 3

Movie (mpg format) of MD simulation trajectory of 150 mM K+ with a2x12 (JC parameters, run1, 200 ns); for clarity, only one filament is shown in secondary structure representation with E11, E22, D23, and A42 depicted in red sticks and K+ ions within 5 Å of the filament as violet spheres. The protein movement is smoothed via VMD. (MPG 5387 kb)

Online Resource 4

Movie (mpg format) of MD simulation trajectory of 150 mM Li+ with a2x12 (JC parameters, run1, 200 ns); for clarity, only one filament is shown in secondary structure representation with E11, E22, D23, and A42 depicted in red sticks and Li+ ions within 5 Å of the filament as iceblue spheres. The protein movement is smoothed via VMD. (MPG 5519 kb)

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Huraskin, D., Horn, A.H.C. Alkali ion influence on structure and stability of fibrillar amyloid-β oligomers. J Mol Model 25, 37 (2019). https://doi.org/10.1007/s00894-018-3920-4

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