Application of Iterative Soft Thresholding for Fast Reconstruction of NMR Data Non-uniformly Sampled with Multidimensional Poisson Gap Scheduling

By S. Hyberts, A. Milbradt, Andreas Wagner, H. Arthanari, G. Wagner. 2012

442 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Nuclear and High Energy Physics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Sergey Ovchinnikov
2. Gaetano T. Montelione
3. David Baker
4. Ivan V. Anishchenko

In Radiology, Nuclear Medicine and Imaging

1. David Baker
2. Frances H. Arnold

Citations in the corpus

• De novo protein design by deep network hallucination (2020, methodology): “A 50% Poisson gap sampling schedule 41 was used for NUS within TopSpin 3.2 (Bruker) and subsequently reconstructed using sparse multidimensional iterative lineshape-enhanced 42 (SMILE) reconstruction within NMRPipe 38 .”
• Sparse multidimensional iterative lineshape-enhanced (SMILE) reconstruction of both non-uniformly sampled and conventional NMR data (2016, unknown): “…et al. 1987; Delsuc and Tramesel 2006; Mobli et al. 2007; Balsgart and Vosegaard 2012; Hoch et al. 2014), compressed sensing techniques such as iterative soft thresholding methods (Hyberts et al. 2012, 2013) and others (Holland et al. 2011; Kazimierczuk and Orekhov 2011; Bostock et al. 2012).”
• The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data (2016, methodology): “The approach can flexibly incorporate data from other non-uniform sampling and reconstruction approaches (Dashti et al. 2015), such as ist@HMS (Hyberts et al. 2012) or NESTA - NMR (Sun et al. 2015).”