Randomization improves sparse sampling in multidimensional NMR

Abstract

While a number of strategies have been developed to reduce data collection requirements for multidimensional NMR based on non-Fourier methods of spectrum analysis, there is an increasing awareness that the principal differences in the performance of these methods is attributable to the sampling strategies employed, and not the method of spectrum analysis per se. The ability of maximum entropy reconstruction to utilize essentially arbitrary sampling schemes makes it a useful platform for comparative analysis of sampling strategies. Here we use maximum entropy reconstruction to demonstrate that artifacts characteristic of sparse sampling result from regularity in the sampling pattern, and that they can be substantially reduced by introducing a degree of randomness to an otherwise regular sampling scheme, without requiring additional sampling.

Accelerating NMR-Based Structural Studies of Proteins by Combining Amino Acid Selective Unlabeling and Fast NMR Methods

“…The experiments together provide the chemical shifts of 15 N, 1 HN, 13 C α , 13 C β , 13 C nuclei of the C-terminal neighbor ('i + 1') of amino acid residue, 'i', which is selectively unlabeled. The experiments, namely, 2D HN(CA)(i + 1), GFT (3,2)D HNCACB(i + 1) and GFT (3,2)D HNCACO(i + 1) are further accelerated by employing non-uniform sampling (NUS) [20][21][22][23][24][25][26]. The methodology is demonstrated on a selectively unalabeled protein sample of ubiquitin.…”

Combining methods for speeding up multi-dimensional acquisition. Sparse sampling and fast pulsing methods for unfolded proteins

“…Starting from the sampling pattern, one can evaluate the point spread function (PSF) by Fourier transformation (a sampled point is set to 1, a non-sampled to 0). If randomness is introduced into the sampling schedule, then the PSF will resemble noise [26,27] despite its deterministic nature. Each peak in the spectrum is convoluted by the PSF and thus the artifacts are proportional to the main peak.…”

“…S1 (Supplementary information): using a synthetic data set where only 3% of the complete sampling schedule is retained, the artefact amplitude is more than 10% of the main peak. MaxEnt partially deconvolutes the PSF from the spectrum [27] and thus reduces these artifacts, as do several other methods (but not FT). Fig.…”

Noise reduction by dynamic signal preemphasis

“…They include nonuniform data sampling [5][6][7][8], triangular sampling [9], random sampling [10], matched accumulation, i.e., data sampling at a fixed rate but different amounts of signal averaging [11], and so on. The present work is distinct from these previous works, in the sense that the former improves the dynamic range of one-dimensional spectra, while the latter concern sensitivity and/or spectral resolution of multidimensional experiments.…”