An efficient combination of BEST and NUS methods in multidimensional NMR spectroscopy for high throughput analysis of proteins

Nonuniform sampling (NUS) of multidimensional NMR experiments is a powerful tool to obtain high-resolution spectra with less instrument time. With NUS, only a subset of the points needed for conventional Fourier transformation is recorded, and sophisticated algorithms are needed to reconstruct the missing data points. During the last decade, several software packages implementing the reconstruction algorithms have emerged and been refined and now result in spectra of almost similar quality as spectra from conventionally recorded and processed data. However, from the number of literature references to the reconstruction methods, many more multidimensional NMR spectra could presumably be recorded with NUS. To help researchers considering to start using NUS for their NMR experiments, we here review 13 different reconstruction methods found in five software packages (CambridgeCS, hmsIST, MddNMR, NESTA-NMR, and SMILE). We have compared how the methods run with the provided example scripts for reconstructing a nonuniform sampled three-dimensional 15 N-NOESY-HSQC at sampling densities from 5% to 50%. Overall, the spectra are all of similar quality above 20% sampling density. Thus, without any particular knowledge about the details of the reconstruction algorithms, significant reduction in the experiment time can be achieved. Below 20% sampling density, the intensities of particular weak peaks start being affected. MddNMR's IST with virtual echo and the SMILE algorithms still reproduced the spectra with the highest accuracy of peak intensities.

Section: Why Use Nonuniform Sampling?mentioning

confidence: 99%

Section: Why Use Nonuniform Sampling?mentioning

confidence: 99%

Section: Why Use Nonuniform Sampling?mentioning

confidence: 99%

Section: Software Overviewmentioning

confidence: 99%

“…Calculation time for 10% sparse 15 Increasing the number of points by 50% in each indirect dimension increases the computation time to 9 min.…”

Section: T a B L Ementioning

confidence: 99%

See 3 more Smart Citations

Software for reconstruction of nonuniformly sampled NMR data

Pedersen

Prestel

Teilum

2020

Benefits of time‐resolved nonuniform sampling in reaction monitoring: The case of aza‐Michael addition of benzylamine and acrylamide

Gołowicz

Kaźmierczak

Kazimierczuk

2020

Monitoring of chemical reactions is best carried out using methods that sample the test object at a rate greater than the time scale of the processes taking place. The recently proposed time-resolved nonuniform sampling (TR-NUS) method allows the use of two-dimensional (2D) nuclear magnetic resonance (NMR) spectra for this purpose and provides a time resolution equivalent to that achievable using one-dimensional spectra. Herein, we show that TR-NUS acquired data eliminates 2D spectral line disturbances and enables more accurate signal integration and kinetics conclusions. The considerations are exemplified with a seemingly simple aza-Michael reaction of benzylamine and acrylamide. Surprisingly, the product identification is possible only using 2D spectra, although credible monitoring requires TR-NUS.

Rapid nuclear magnetic resonance data acquisition with improved resolution and sensitivity for high‐throughput metabolomic analysis

Joseph

Sukumaran

Chandra

et al. 2020

Nuclear magnetic resonance (NMR)-based metabolomics has witnessed rapid advancements in recent years with the continuous development of new methods to enhance the sensitivity, resolution, and speed of data acquisition. Some of the approaches were earlier used for peptide and protein resonance assignments and have now been adapted to metabolomics. At the same time, new NMR methods involving novel data acquisition techniques, suited particularly for high-throughput analysis in metabolomics, have been developed. In this review, we focus on the different sampling strategies or data acquisition methods that have been developed in our laboratory and other groups to acquire NMR spectra rapidly with high sensitivity and resolution for metabolomics. In particular, we focus on the use of multiple receivers, phase modulation NMR spectroscopy, and fast-pulsing methods for identification and assignments of metabolites.