Journal of Biomolecular NMR

, Volume 52, Issue 4, pp 315–327 | Cite as

Application of iterative soft thresholding for fast reconstruction of NMR data non-uniformly sampled with multidimensional Poisson Gap scheduling

  • Sven G. Hyberts
  • Alexander G. Milbradt
  • Andreas B. Wagner
  • Haribabu Arthanari
  • Gerhard Wagner
Article

Abstract

The fast Fourier transformation has been the gold standard for transforming data from time to frequency domain in many spectroscopic methods, including NMR. While reliable, it has as a drawback that it requires a grid of uniformly sampled data points. This needs very long measuring times for sampling in multidimensional experiments in all indirect dimensions uniformly and even does not allow reaching optimal evolution times that would match the resolution power of modern high-field instruments. Thus, many alternative sampling and transformation schemes have been proposed. Their common challenges are the suppression of the artifacts due to the non-uniformity of the sampling schedules, the preservation of the relative signal amplitudes, and the computing time needed for spectra reconstruction. Here we present a fast implementation of the Iterative Soft Thresholding approach (istHMS) that can reconstruct high-resolution non-uniformly sampled NMR data up to four dimensions within a few hours and make routine reconstruction of high-resolution NUS 3D and 4D spectra convenient. We include a graphical user interface for generating sampling schedules with the Poisson-Gap method and an estimation of optimal evolution times based on molecular properties. The performance of the approach is demonstrated with the reconstruction of non-uniformly sampled medium and high-resolution 3D and 4D protein spectra acquired with sampling densities as low as 0.8%. The method presented here facilitates acquisition, reconstruction and use of multidimensional NMR spectra at otherwise unreachable spectral resolution in indirect dimensions.

Keywords

Nuclear magnetic resonance Sparse sampling Spectra reconstruction Iterative soft thresholding Compressed sensing Maximum entropy reconstruction FM reconstruction 

Abbreviations

NMR

Nuclear magnetic resonance

IST

Iterative soft thresholding

istHMS

Implementation of IST at Harvard Medical School

FM reconstruction

Forward maximum entropy reconstruction

MDD

Multi-dimensional decomposition

FDM

Filter diagonalization method

FFT

Fast Fourier transformation

DFT

Discrete Fourier transformation

NOE

Nuclear Overhauser enhancement

NOESY

NOE spectroscopy

GUI

Graphical user interface

Notes

Acknowledgment

This research was supported by the National Institutes of Health (Grants GM047467, CA127990, GM094608 and EB002026). We thank Dr. Koh Takeuchi for providing the data used in Fig. 7B.

Supplementary material

10858_2012_9611_MOESM1_ESM.doc (2.8 mb)
Supplementary material 1 (DOC 2867 kb)

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Sven G. Hyberts
    • 1
  • Alexander G. Milbradt
    • 1
  • Andreas B. Wagner
    • 1
    • 2
  • Haribabu Arthanari
    • 1
  • Gerhard Wagner
    • 1
  1. 1.Department of Biological Chemistry and Molecular PharmacologyHarvard Medical SchoolBostonUSA
  2. 2.Wentworth Institute of TechnologyBostonUSA

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