Temperature as an Extra Dimension in Multidimensional Protein NMR Spectroscopy

Abstract

NMR spectroscopy is a particularly informative method for studying protein structures and dynamics in solution; however, it is also one of the most time‐consuming. Modern approaches to biomolecular NMR spectroscopy are based on lengthy multidimensional experiments, the duration of which grows exponentially with the number of dimensions. The experimental time may even be several days in the case of 3D and 4D spectra. Moreover, the experiment often has to be repeated under several different conditions, for example, to measure the temperature‐dependent effects in a spectrum (temperature coefficients (TCs)). Herein, a new approach that involves joint sampling of indirect evolution times and temperature is proposed. This allows TCs to be measured through 3D spectra in even less time than that needed to acquire a single spectrum by using the conventional approach. Two signal processing methods that are complementary, in terms of sensitivity and resolution, 1) dividing data into overlapping subsets followed by compressed sensing reconstruction, and 2) treating the complete data set with a variant of the Radon transform, are proposed. The temperature‐swept 3D HNCO spectra of two intrinsically disordered proteins, osteopontin and CD44 cytoplasmic tail, show that this new approach makes it possible to determine TCs and their non‐linearities effectively. Non‐linearities, which indicate the presence of a compact state, are particularly interesting. The complete package of data acquisition and processing software for this new approach are provided.

Supporting the assignment of NMR spectra with variable‐temperature experiments

“…For example, TCs have been used to study protein dynamics, 6,7 where the nonlinearities of TCs indicated the presence of low-populated excited states. 6,8 Some reports mentioned the temperature dependencies of chemical shifts of labile protons of hydroxyl groups in small molecules. 9,10 However, the fact that TCs of small molecules are not easy to predict 11 and are not widely available in databases limited their use.…”

Sensitivity considerations on denoising series of spectra by singular value decomposition

“…In this Letter, we question the possibility of denoising these data through low-rank decomposition methods: the modulation of the signal intensity as a result of the pulse sequence is expected to be captured as the most significant variation across a series of spectra. [3,4] It is worth mentioning that the joint processing of series of signals has been used in other contexts in NMR for sensitivity boosts and better determination of spectral parameters (see for instance previous works [5][6][7][8][9][10][11] ). We also discuss which acquisition protocol can be applied to obtain data of a sufficient quality to be used for quantitative purposes.…”

Time-Frequency Analysis of Two-Dimensional Electron Spin Resonance Signals

“…However, for non-stationary data, where the frequencies are time-dependent, application of FT results in a significant loss of information. In NMR, there are approaches for analyzing non-stationary signals related to studying reaction mechanisms and fast signal acquisitions, among others. − In this paper, we present a time-frequency analysis of non-stationary 2D signals for separating overlapped peaks. In case of 2D ESR frequency-domain spectra, data analysis becomes challenging due to the presence of overlapped and hard-to-distinguish peaks.…”