Abstract
Protein misfolding and self-assembling into amyloid structures are associated with a number of diseases. Characterization of protein amyloid formation reactions is a challenging task as transient populations of multiple species are involved. Here we outline a method for identification and characterization of the individual soluble states during protein amyloid formation. The method combines NMR translational diffusion measurements with multilinear data analysis.
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References
Chatani E, Yamamoto N (2018) Recent progress on understanding the mechanisms of amyloid nucleation. Biophys Rev 10(2):527–534. https://doi.org/10.1007/s12551-017-0353-8
Michaels TCT, Šarić A, Curk S et al (2020) Dynamics of oligomer populations formed during the aggregation of Alzheimer’s Aβ42 peptide. Nat Chem 12(5):445–451. https://doi.org/10.1038/s41557-020-0452-1
Jensen KS, Linse S, Nilsson M et al (2019) Revealing well-defined soluble states during amyloid fibril formation by multilinear analysis of NMR diffusion data. J Am Chem Soc 141(47):18649–18652. https://doi.org/10.1021/jacs.9b07952
Bro R (1997) PARAFAC. Tutorial and applications. Chemom Intell Lab Syst 38(2):149–172
Dal Poggetto G, Castañar L, Adams RW et al (2017) Relaxation-encoded NMR experiments for mixture analysis: REST and beer. Chem Commun 53(54):7461–7464. https://doi.org/10.1039/C7CC03150E
Colbourne AA, Meier S, Morris GA et al (2013) Unmixing the NMR spectra of similar species–vive la différence. Chem Commun 49(89):10510–10512. https://doi.org/10.1039/C3CC46228E
Colbourne AA, Morris GA, Nilsson M (2011) Local covariance order diffusion-ordered spectroscopy: a powerful tool for mixture analysis. J Am Chem Soc 133(20):7640–7643. https://doi.org/10.1021/ja2004895
Khajeh M, Botana A, Bernstein MA et al (2010) Reaction kinetics studied using diffusion-ordered spectroscopy and multiway chemometrics. Anal Chem 82(5):2102–2108. https://doi.org/10.1021/ac100110m
Nilsson M, Khajeh M, Botana A et al (2009) Diffusion NMR and trilinear analysis in the study of reaction kinetics. Chem Commun 10:1252–1254. https://doi.org/10.1039/B820813A
Björnerås J, Botana A, Morris GA et al (2014) Resolving complex mixtures: trilinear diffusion data. J Biomol NMR 58(4):251–257. https://doi.org/10.1007/s10858-013-9752-8
Claus A, Bro R (2000) The N-way toolbox for MATLAB. Chemom Intell Lab Syst 52:1–4
Khan MAI, Respondek M, Kjellstrom S et al (2017) Cu/Zn superoxide dismutase forms amyloid fibrils under near-physiological quiescent conditions: the roles of disulfide bonds and effects of denaturant. ACS Chem Neurosci 8(9):2019–2026. https://doi.org/10.1021/acschemneuro.7b00162
Bille A, Jensen KS, Mohanty S et al (2019) Stability and local unfolding of SOD1 in the presence of protein crowders. J Phys Chem B 123(9):1920–1930. https://doi.org/10.1021/acs.jpcb.8b10774
Ferrage F, Zoonens M, Warschawski DE et al (2003) Slow diffusion of macromolecular assemblies by a new pulsed field gradient NMR method. J Am Chem Soc 125(9):2541–2545. https://doi.org/10.1021/ja0211407
Ferrage F, Zoonens M, Warschawski DE et al (2004) Correction to Ref. [14]. J Am Chem Soc 126(17):5654. https://doi.org/10.1021/ja033464g
Connell MA, Bowyer PJ, Bone PA et al (2009) Improving the accuracy of pulsed field gradient NMR diffusion experiments: correction for gradient non-uniformity. J Magn Reson 198(1):121–131. https://doi.org/10.1016/j.jmr.2009.01.025
Barbosa TM, Rittner R, Tormena CF et al (2016) Convection in liquid-state NMR: expect the unexpected. RSC Adv 6(97):95173–95176. https://doi.org/10.1039/C6RA23427E
Nilsson M, Gil AM, Delgadillo I et al (2005) Improving pulse sequences for 3D DOSY: COSY-IDOSY. Chem Commun 13:1737–1739. https://doi.org/10.1039/B415099F
Swan I, Reid M, Howe P et al (2015) Sample convection in liquid-state NMR: why it is always with us, and what we can do about it. J Magn Reson 252:120–129. https://doi.org/10.1016/j.jmr.2014.12.006
Engelsen SB, Bro R (2003) PowerSlicing. J Magn Reson 163(1):192–197. https://doi.org/10.1016/S1090-7807(03)00125-3
Nicoud L, Lattuada M, Yates A et al (2015) Impact of aggregate formation on the viscosity of protein solutions. Soft Matter 11(27):5513–5522. https://doi.org/10.1039/C5SM00513B
Sinnaeve D (2012) The Stejskal–Tanner equation generalized for any gradient shape—an overview of most pulse sequences measuring free diffusion. Concepts Magn Reson A 40(2):39–65. https://doi.org/10.1002/cmr.a.21223
Acknowledgment
We thank Ulrich Weininger for his help with the HSQC-based DOSY experiments.
The work founding this protocol is supported by the Swedish Research Council (2014-5815 to M.A) and (2020-04888 to KSJ), the Danish Council for Independent Research, Sapere Aude: DFF Research Talent (DFF − 4002-00258 to KSJ), and the Engineering and Physical Sciences Research Council (EP/E05899X/1 to MN).
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Jensen, K.S., Nilsson, M., Akke, M., Malmendal, A. (2023). Identification of Distinct Soluble States During Fibril Formation Using Multilinear Analysis of NMR Diffusion Data. In: Cieplak, A.S. (eds) Protein Aggregation. Methods in Molecular Biology, vol 2551. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2597-2_29
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DOI: https://doi.org/10.1007/978-1-0716-2597-2_29
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