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Importance of time-ordered non-uniform sampling of multi-dimensional NMR spectra of Aβ1–42 peptide under aggregating conditions

  • Jinfa Ying
  • C. Ashley Barnes
  • John M. Louis
  • Ad BaxEmail author
Article

Abstract

Although the order of the time steps in which the non-uniform sampling (NUS) schedule is implemented when acquiring multi-dimensional NMR spectra is of limited importance when sample conditions remain unchanged over the course of the experiment, it is shown to have major impact when samples are unstable. In the latter case, time-ordering of the NUS data points by the normalized radial length yields a reduction of sampling artifacts, regardless of the spectral reconstruction algorithm. The disadvantage of time-ordered NUS sampling is that halting the experiment prior to its completion will result in lower spectral resolution, rather than a sparser data matrix. Alternatively, digitally correcting for sample decay prior to reconstruction of randomly ordered NUS data points can mitigate reconstruction artifacts, at the cost of somewhat lower sensitivity. Application of these sampling schemes to the Alzheimer’s amyloid beta (Aβ1–42) peptide at an elevated concentration, low temperature, and 3 kbar of pressure, where approximately 75% of the peptide reverts to an NMR-invisible state during the collection of a 3D 15N-separated NOESY spectrum, highlights the improvement in artifact suppression and reveals weak medium-range NOE contacts in several regions, including the C-terminal region of the peptide.

Keywords

Aggregation High pressure Sampling scheme SMILE Sparse sampling Time ordering 

Notes

Acknowledgements

This work was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases. We acknowledge use of the NIDDK Mass Spectrometry facility and thank Annie Geronimo and Dr. Marielle Waelti for technical support, Dr. Dennis A. Torchia, Dr. Frank Delaglio, Dr. Andy Byrd, and Dr. Hsiau-Wei Lee for valuable discussions, and Professor Vladislav Orekhov for his kind help with optimizing the MDDNMR processing scripts used to reconstruct some of the data presented in SI Figs. S3 and S6. These reconstructions were performed by making use of NMRbox: National Center for Biomolecular NMR Data Processing and Analysis, a Biomedical Technology Research Resource (BTRR), which is supported by NIH Grant P41GM111135 (NIGMS).

Funding

Funding was provided by National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. DK075141-02).

Supplementary material

10858_2019_235_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (PDF 1159 KB)

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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthBethesdaUSA

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