Spectroscopy by integration of frequency and time domain information for fast acquisition of high-resolution dark spectra

Adventures in interdisciplinary science: a half century at the nexus between chemistry, physics and biology

A look back over five decades of research.

Software for reconstruction of nonuniformly sampled NMR data

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.

Amino-acid selective isotope labeling enables simultaneous overlapping signal decomposition and information extraction from NMR spectra

Signal overlapping is a major bottleneck for protein NMR analysis. We propose a new method, stable-isotope-assisted parameter extraction (SiPex), to resolve overlapping signals by a combination of amino-acid selective isotope labeling (AASIL) and tensor decomposition. The basic idea of Sipex is that overlapping signals can be decomposed with the help of intensity patterns derived from quantitative fractional AASIL, which also provides amino-acid information. In SiPex, spectra for protein characterization, such as ¹⁵N relaxation measurements, are assembled with those for amino-acid information to form a four-order tensor, where the intensity patterns from AASIL contribute to high decomposition performance even if the signals share similar chemical shift values or characterization profiles, such as relaxation curves. The loading vectors of each decomposed component, corresponding to an amide group, represent both the amino-acid and relaxation information. This information link provides an alternative protein analysis method that does not require "assignments" in a general sense; i.e., chemical shift determinations, since the amino-acid information for some of the residues allows unambiguous assignment according to the dual selective labeling. SiPex can also decompose signals in time-domain raw data without Fourier transform, even in non-uniformly sampled data without spectral reconstruction. These features of SiPex should expand biological NMR applications by overcoming their overlapping and assignment problems.

Fast time-resolved NMR with non-uniform sampling

NMR spectroscopy is a versatile tool for studying time-dependent processes: chemical reactions, phase transitions or macromolecular structure changes. However, time-resolved NMR is usually based on the simplest among available techniques - one-dimensional spectra serving as "snapshots" of the studied process. One of the reasons is that multidimensional experiments are very time-expensive due to costly sampling of evolution time space. In this review we summarize efforts to alleviate the problem of limited applicability of multidimensional NMR in time-resolved studies. We focus on techniques based on sparse or non-uniform sampling (NUS), which lead to experimental time reduction by omitting a significant part of the data during measurement and reconstructing it mathematically, adopting certain assumptions about the spectrum. NUS spectra are faster to acquire than conventional ones and thus better suited to the role of "snapshots", but still suffer from non-stationarity of the signal i.e. amplitude and frequency variations within a dataset. We discuss in detail how these instabilities affect the spectra, and what are the optimal ways of sampling the non-stationary FID signal. Finally, we discuss related areas of NMR where serial experiments are exploited and how they can benefit from the same NUS-based approaches.

Accelerating 2D NMR relaxation dispersion experiments using iterated maps

NMR relaxation dispersion experiments play a central role in exploring molecular motion over an important range of timescales, and are an example of a broader class of multidimensional NMR experiments that probe important biomolecules. However, resolving the spectral features of these experiments using the Fourier transform requires sampling the full Nyquist grid of data, making these experiments very costly in time. Practitioners often reduce the experiment time by omitting 1D experiments in the indirectly observed dimensions, and reconstructing the spectra using one of a variety of post-processing algorithms. In prior work, we described a fast, Fourier-based reconstruction method using iterated maps according to the Difference Map algorithm of Veit Elser (DiffMap). Here we describe coDiffMap, a new reconstruction method that is based on DiffMap, but which exploits the strong correlations between 2D data slices in a pseudo-3D experiment. We apply coDiffMap to reconstruct dispersion curves from an [Formula: see text] relaxation dispersion experiment, and demonstrate that the method provides fast reconstructions and accurate relaxation curves down to very low numbers of sparsely-sampled data points.

Reaching the sparse-sampling limit for reconstructing a single peak in a 2D NMR spectrum using iterated maps

Many of the ubiquitous experiments of biomolecular NMR, including [Formula: see text], [Formula: see text], and CEST, involve acquiring repeated 2D spectra under slightly different conditions. Such experiments are amenable to acceleration using non-uniform sampling spectral reconstruction methods that take advantage of prior information. We previously developed one such technique, an iterated maps method (DiffMap) that we successfully applied to 2D NMR spectra, including [Formula: see text] relaxation dispersion data. In that prior work, we took a top-down approach to reconstructing the 2D spectrum with a minimal number of sparse samples, reaching an undersampling fraction that appeared to leave some room for improvement. In this study, we develop an in-depth understanding of the action of the DiffMap algorithm, identifying the factors that cause reconstruction errors for different undersampling fractions. This improved understanding allows us to formulate a bottom-up approach to finding the lowest number of sparse samples required to accurately reconstruct individual spectral features with DiffMap. We also discuss the difficulty of extending this method to reconstructing many peaks at once, and suggest a way forward.

Nonstationary Two-Dimensional Nuclear Magnetic Resonance: A Method for Studying Reaction Mechanisms in Situ

Nuclear magnetic resonance spectroscopy (NMR) is a versatile tool of chemical analysis allowing one to determine structures of molecules with atomic resolution. Particularly informative are two-dimensional (2D) experiments that directly identify atoms coupled by chemical bonds or a through-space interaction. Thus, NMR could potentially be powerful tool to study reactions in situ and explain their mechanisms. Unfortunately, 2D NMR is very time-consuming and thus often cannot serve as a "snapshot" technique for in situ reaction monitoring. Particularly difficult is the case of spectra, in which resonance frequencies vary in the course of reaction. This leads to resolution and sensitivity loss, often hindering the detection of transient products. In this paper we introduce a novel approach to correct such nonstationary 2D NMR signals and raise the detection limits over 10 times. We demonstrate success of its application for studying the mechanism of the reaction of AgSO₄-induced synthesis of diphenylmethane-type compounds. Several reactions occur in the studied mixture of benzene and toluene, all with rather low yield and leading to compounds with similar chemical shifts. Nevertheless, with the use of a proposed 2D NMR approach we were able to describe complex mechanisms of diphenylmethane formation involving AgSO₄-induced toluene deprotonation and formation of benzyl carbocation, followed by nucleophilic attacks.

SpinStudioJ: A cross-platform NMR data acquisition and processing workbench based on a plug-in architecture

Flexibility and extensibility are important issues in the design of nuclear magnetic resonance (NMR) software, as these determine the ability to integrate a variety of continuously evolving data acquisition and processing methods. Here, SpinStudioJ is introduced. It is an NMR data acquisition and processing workbench with a plug-in-based architecture. The workbench is based on Eclipse Rich Client Platform, which provides a plug-and-play runtime mechanism and rich graphical user interface functionality. New data acquisition methods and processing algorithms can be easily integrated into the SpinStudioJ workbench by defining extension points, without the need to redistribute existing modules. The software is independent of operating systems, as it leverages the cross-platform feature of the Java virtual machine.

Monitoring Hydrogenation Reactions using Benchtop 2D NMR with Extraordinary Sensitivity and Spectral Resolution

Low-field benchtop nuclear magnetic resonance (BT-NMR) spectrometers with Halbach magnets are being increasingly used in science and industry as cost-efficient tools for the monitoring of chemical reactions, including hydrogenation. However, their use of low-field magnets limits both resolution and sensitivity. In this paper, we show that it is possible to alleviate these two problems through the combination of parahydrogen-induced polarization (PHIP) and fast correlation spectroscopy with time-resolved non-uniform sampling (TR-NUS). PHIP can enhance NMR signals so that substrates are easily detectable on BT-NMR spectrometers. The interleaved acquisition of one- and two-dimensional spectra with TR-NUS provides unique insight into the consecutive moments of hydrogenation reactions, with a spectral resolution unachievable in a standard approach. We illustrate the potential of the technique with two examples: the hydrogenation of ethylphenyl propiolate and the hydrogenation of a mixture of two substrates - ethylphenyl propiolate and ethyl 2-butynoate.

TReNDS-Software for reaction monitoring with time-resolved non-uniform sampling

NMR spectroscopy, used routinely for structure elucidation, has also become a widely applied tool for process and reaction monitoring. However, the most informative of NMR methods-correlation experiments-are often useless in this kind of applications. The traditional sampling of a multidimensional FID is usually time-consuming, and thus, the reaction-monitoring toolbox was practically limited to 1D experiments (with rare exceptions, e.g., single-scan or fast-sampling experiments). Recently, the technique of time-resolved non-uniform sampling (TR-NUS) has been proposed, which allows to use standard multidimensional pulse sequences preserving the temporal resolution close to that achievable in 1D experiments. However, the method existed only as a prototype and did not allow on-the-fly processing during the reaction. In this paper, we introduce TReNDS: free, user-friendly software kit for acquisition and processing of TR-NUS data. The program works on Bruker, Agilent, and Magritek spectrometers, allowing to carry out up to four experiments with interleaved TR-NUS. The performance of the program is demonstrated on the example of enzymatic hydrolysis of sucrose.

Accelerated acquisition in pure-shift spectra based on prior knowledge from 1H NMR

Pure shift NMR with maximum performance by non-uniform sampling with prior knowledge.

Systematic Evaluation of Non-Uniform Sampling Parameters in the Targeted Analysis of Urine Metabolites by 1H,1H 2D NMR Spectroscopy

Non-uniform sampling (NUS) allows the accelerated acquisition of multidimensional NMR spectra. The aim of this contribution was the systematic evaluation of the impact of various quantitative NUS parameters on the accuracy and precision of 2D NMR measurements of urinary metabolites. Urine aliquots spiked with varying concentrations (15.6-500.0 µM) of tryptophan, tyrosine, glutamine, glutamic acid, lactic acid, and threonine, which can only be resolved fully by 2D NMR, were used to assess the influence of the sampling scheme, reconstruction algorithm, amount of omitted data points, and seed value on the quantitative performance of NUS in 1H,1H-TOCSY and 1H,1H-COSY45 NMR spectroscopy. Sinusoidal Poisson-gap sampling and a compressed sensing approach employing the iterative re-weighted least squares method for spectral reconstruction allowed a 50% reduction in measurement time while maintaining sufficient quantitative accuracy and precision for both types of homonuclear 2D NMR spectroscopy. Together with other advances in instrument design, such as state-of-the-art cryogenic probes, use of 2D NMR spectroscopy in large biomedical cohort studies seems feasible.

Solution NMR views of dynamical ordering of biomacromolecules

BACKGROUND

To understand the mechanisms related to the 'dynamical ordering' of macromolecules and biological systems, it is crucial to monitor, in detail, molecular interactions and their dynamics across multiple timescales. Solution nuclear magnetic resonance (NMR) spectroscopy is an ideal tool that can investigate biophysical events at the atomic level, in near-physiological buffer solutions, or even inside cells.

SCOPE OF REVIEW

In the past several decades, progress in solution NMR has significantly contributed to the elucidation of three-dimensional structures, the understanding of conformational motions, and the underlying thermodynamic and kinetic properties of biomacromolecules. This review discusses recent methodological development of NMR, their applications and some of the remaining challenges.

MAJOR CONCLUSIONS

Although a major drawback of NMR is its difficulty in studying the dynamical ordering of larger biomolecular systems, current technologies have achieved considerable success in the structural analysis of substantially large proteins and biomolecular complexes over 1MDa and have characterised a wide range of timescales across which biomolecular motion exists. While NMR is well suited to obtain local structure information in detail, it contributes valuable and unique information within hybrid approaches that combine complementary methodologies, including solution scattering and microscopic techniques.

GENERAL SIGNIFICANCE

For living systems, the dynamic assembly and disassembly of macromolecular complexes is of utmost importance for cellular homeostasis and, if dysregulated, implied in human disease. It is thus instructive for the advancement of the study of the dynamical ordering to discuss the potential possibilities of solution NMR spectroscopy and its applications. This article is part of a Special Issue entitled "Biophysical Exploration of Dynamical Ordering of Biomolecular Systems" edited by Dr. Koichi Kato.

Probing Transient Release of Membrane-Sequestered Tyrosine-Based Signaling Motif by Solution NMR Spectroscopy

Membrane sequestration of tyrosine-based signaling motifs of antigen receptors effectively restricts the signaling activities in resting lymphocytes. However, low level of basal signaling in resting cells is required for lymphocyte survival and antigen responsiveness, of which the molecular mechanism remains obscure. Here we probe the transient release of the cytoplasmic domain of the membrane-bound IgG heavy chain (mIgG-tail) by hydrogen exchange NMR spectroscopy, illustrating a dynamic molecular basis for its basal signaling activity. To solve the severe resonance overlap problem in the 2D spectra of mIgG-tail, a non-uniformly sampled pseudo-4D hydrogen exchange NMR experiment has been exploited to quantitatively measure site-specific hydrogen exchange rates. Our solution NMR study reveals transient solvent exposure of the ITT signaling motif that can be further enhanced by calcium ion, and provides insight into the mechanism of lymphocyte basal signaling.

Interpolating and extrapolating with hmsIST: seeking a tmax for optimal sensitivity, resolution and frequency accuracy

Non-Uniform Sampling has the potential to exploit the optimal resolution of high-field NMR instruments. This is not possible in 3D and 4D NMR experiments when using traditional uniform sampling due to the long overall measurement time. Nominally, uniformly sampled time domain data acquired to a maximum evolution time t_max can be extended to high resolution via a virtual maximum evolution time t*_max while extrapolating with linear prediction or iterative soft thresholding (IST). At the high resolution obtainable with extrapolation of US data, however, the accuracy of peak positions is compromised as observed when comparing inter- and intra-residue peaks in a 3D HNCA experiment. However, the accuracy of peak positions is largely improved by spreading the same number of acquired time domain data points non-uniformly over a larger evolution time to an optimal t_max followed by extrapolation to a total t*_max and processing the data with an appropriate reconstruction method, such as hmsIST. To explore the optimum value of experimentally measured t_max to be reached non-uniformly with a given number of sampling points we have created test situations of time-equivalent experiments and evaluate sensitivity and accuracy of peak positions. Here we use signal-to-maximum-noise ratio as the decisive measure of sensitivity. We find that both sensitivity and resolution are optimal when PoissonGap sampling to a t_max of about ½*T₂^*. Digital resolution is further enhanced by extrapolating the range of acquired time domain data to 2*T₂^* but without measuring experimental points beyond ½*T₂^*.

Joint non-uniform sampling of all incremented time delays for quicker acquisition in protein relaxation studies

NMR relaxometry plays crucial role in studies of protein dynamics. The measurement of longitudinal and transverse relaxation rates of [Formula: see text]N is the main source of information on backbone motions. However, even the most basic approach exploiting a series of [Formula: see text]N HSQC spectra can require several hours of measurement time. Standard non-uniform sampling (NUS), i.e. random under-sampling of indirect time domain, typically cannot reduce this by more than 2-4[Formula: see text] due to relatively low "compressibility" of these spectra. In this paper we propose an extension of NUS to relaxation delays. The two-dimensional space of [Formula: see text]/[Formula: see text] is sampled in a way similar to NUS of [Formula: see text]/[Formula: see text] domain in 3D spectra. The signal is also processed in a way similar to that known from 3D NUS spectra i.e. using one of the most popular compressed sensing algorithms, iterative soft thresholding. The 2D Fourier transform matrix is replaced with mixed inverse Laplace-Fourier transform matrix. The peak positions in resulting 3D spectrum are characterized by two frequency coordinates and relaxation rate and thus no additional fitting of exponential curves is required. The method is tested on three globular proteins, providing satisfactory results in a time corresponding to acquisition of two conventional [Formula: see text]N HSQC spectra.

Improving resolution in multidimensional NMR using random quadrature detection with compressed sensing reconstruction

NMR spectroscopy is central to atomic resolution studies in biology and chemistry. Key to this approach are multidimensional experiments. Obtaining such experiments with sufficient resolution, however, is a slow process, in part since each time increment in every indirect dimension needs to be recorded twice, in quadrature. We introduce a modified compressed sensing (CS) algorithm enabling reconstruction of data acquired with random acquisition of quadrature components in gradient-selection NMR. We name this approach random quadrature detection (RQD). Gradient-selection experiments are essential to the success of modern NMR and with RQD, a 50 % reduction in the number of data points per indirect dimension is possible, by only acquiring one quadrature component per time point. Using our algorithm (CS_RQD), high quality reconstructions are achieved. RQD is modular and combined with non-uniform sampling we show that this provides increased flexibility in designing sampling schedules leading to improved resolution with increasing benefits as dimensionality of experiments increases, with particular advantages for 4- and higher dimensional experiments.

Pitfalls in compressed sensing reconstruction and how to avoid them

Multidimensional NMR can provide unmatched spectral resolution, which is crucial when dealing with samples of biological macromolecules. The resolution, however, comes at the high price of long experimental time. Non-uniform sampling (NUS) of the evolution time domain allows to suppress this limitation by sampling only a small fraction of the data, but requires sophisticated algorithms to reconstruct omitted data points. A significant group of such algorithms known as compressed sensing (CS) is based on the assumption of sparsity of a reconstructed spectrum. Several papers on the application of CS in multidimensional NMR have been published in the last years, and the developed methods have been implemented in most spectral processing software. However, the publications rarely show the cases when NUS reconstruction does not work perfectly or explain how to solve the problem. On the other hand, every-day users of NUS develop their rules-of-thumb, which help to set up the processing in an optimal way, but often without a deeper insight. In this paper, we discuss several sources of problems faced in CS reconstructions: low sampling level, missassumption of spectral sparsity, wrong stopping criterion and attempts to extrapolate the signal too much. As an appendix, we provide MATLAB codes of several CS algorithms used in NMR. We hope that this work will explain the mechanism of NUS reconstructions and help readers to set up acquisition and processing parameters. Also, we believe that it might be helpful for algorithm developers.

F 1 F 2-selective NMR spectroscopy

Fourier transform NMR spectroscopy has provided unprecedented insight into the structure, interaction and dynamic motion of proteins and nucleic acids. Conventional biomolecular NMR relies on the acquisition of three-dimensional and four-dimensional (4D) data matrices to establish correlations between chemical shifts in the frequency domains F ₁, F ₂, F ₃ and F ₁, F ₂, F ₃, F ₄ respectively. While rich in information, these datasets require a substantial amount of acquisition time, are visually highly unintuitive, require expert knowledge to process, and sample dark and bright regions of the frequency domains equally. Here, we present an alternative approach to obtain multidimensional chemical shift correlations for biomolecules. This strategy focuses on one narrow frequency range, F ₁ F ₂, at a time and records the resulting F ₃ F ₄ correlation spectrum by two-dimensional NMR. As a result, only regions of the frequency domain that contain signals in F ₁ F ₂ ("bright regions") are sampled. F ₁ F ₂ selection is achieved by Hartmann-Hahn cross-polarization using weak radio frequency fields. This approach reveals information equivalent to that of a conventional 4D experiment, while the dimensional reduction may shorten the total acquisition time and simplifies spectral processing, interpretation and comparative analysis. Potential applicability of the F ₁ F ₂-selective approach is illustrated by de novo assignment, structural and dynamics studies of ubiquitin and fatty-acid binding protein 4 (FABP4). Further extension of this concept may spawn new selective NMR experiments to aid studies of site-specific structural dynamics, protein-protein interactions and allosteric modulation of protein structure.

Probing conformational dynamics in biomolecules via chemical exchange saturation transfer: a primer

Although Chemical Exchange Saturation Transfer (CEST) type NMR experiments have been used to study chemical exchange processes in molecules since the early 1960s, there has been renewed interest in the past several years in using this approach to study biomolecular conformational dynamics. The methodology is particularly powerful for the study of sparsely populated, transiently formed conformers that are recalcitrant to investigation using traditional biophysical tools, and it is complementary to relaxation dispersion and magnetization transfer experiments that have traditionally been used to study chemical exchange processes. Here we discuss the concepts behind the CEST experiment, focusing on practical aspects as well, we review some of the pulse sequences that have been developed to characterize protein and RNA conformational dynamics, and we discuss a number of examples where the CEST methodology has provided important insights into the role of dynamics in biomolecular function.

Accurate determination of rates from non-uniformly sampled relaxation data

The application of non-uniform sampling (NUS) to relaxation experiments traditionally used to characterize the fast internal motion of proteins is quantitatively examined. Experimentally acquired Poisson-gap sampled data reconstructed with iterative soft thresholding are compared to regular sequentially sampled (RSS) data. Using ubiquitin as a model system, it is shown that 25 % sampling is sufficient for the determination of quantitatively accurate relaxation rates. When the sampling density is fixed at 25 %, the accuracy of rates is shown to increase sharply with the total number of sampled points until eventually converging near the inherent reproducibility of the experiment. Perhaps contrary to some expectations, it is found that accurate peak height reconstruction is not required for the determination of accurate rates. Instead, inaccuracies in rates arise from inconsistencies in reconstruction across the relaxation series that primarily manifest as a non-linearity in the recovered peak height. This indicates that the performance of an NUS relaxation experiment cannot be predicted from comparison of peak heights using a single RSS reference spectrum. The generality of these findings was assessed using three alternative reconstruction algorithms, eight different relaxation measurements, and three additional proteins that exhibit varying degrees of spectral complexity. From these data, it is revealed that non-linearity in peak height reconstruction across the relaxation series is strongly correlated with errors in NUS-derived relaxation rates. Importantly, it is shown that this correlation can be exploited to reliably predict the performance of an NUS-relaxation experiment by using three or more RSS reference planes from the relaxation series. The RSS reference time points can also serve to provide estimates of the uncertainty of the sampled intensity, which for a typical relaxation times series incurs no penalty in total acquisition time.

Non-uniform sampling of NMR relaxation data

The use of non-uniform sampling of NMR spectra may give significant reductions in the data acquisition time. For quantitative experiments such as the measurement of spin relaxation rates, non-uniform sampling is however not widely used as inaccuracies in peak intensities may lead to errors in the extracted dynamic parameters. By systematic reducing the coverage of the Nyquist grid of (15)N Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion datasets for four different proteins and performing a full data analysis of the resulting non-uniform sampled datasets, we have compared the performance of the multi-dimensional decomposition and iterative re-weighted least-squares algorithms in reconstructing spectra with accurate peak intensities. As long as a single fully sampled spectrum is included in a series of otherwise non-uniform sampled two-dimensional spectra, multi-dimensional decomposition reconstructs the non-uniform sampled spectra with high accuracy. For two of the four analyzed datasets, a coverage of only 20% results in essentially the same results as the fully sampled data. As exemplified by other data, such a low coverage is in general not enough to produce reliable results. We find that a coverage level not compromising the final results can be estimated by recording a single full two-dimensional spectrum and reducing the spectrum quality in silico.

Fast acquisition of multidimensional NMR spectra of solids and mesophases using alternative sampling methods

Unique information about the atom-level structure and dynamics of solids and mesophases can be obtained by the use of multidimensional nuclear magnetic resonance (NMR) experiments. Nevertheless, the acquisition of these experiments often requires long acquisition times. We review here alternative sampling methods, which have been proposed to circumvent this issue in the case of solids and mesophases. Compared to the spectra of solutions, those of solids and mesophases present some specificities because they usually display lower signal-to-noise ratios, non-Lorentzian line shapes, lower spectral resolutions and wider spectral widths. We highlight herein the advantages and limitations of these alternative sampling methods. A first route to accelerate the acquisition time of multidimensional NMR spectra consists in the use of sparse sampling schemes, such as truncated, radial or random sampling ones. These sparsely sampled datasets are generally processed by reconstruction methods differing from the Discrete Fourier Transform (DFT). A host of non-DFT methods have been applied for solids and mesophases, including the G-matrix Fourier transform, the linear least-square procedures, the covariance transform, the maximum entropy and the compressed sensing. A second class of alternative sampling consists in departing from the Jeener paradigm for multidimensional NMR experiments. These non-Jeener methods include Hadamard spectroscopy as well as spatial or orientational encoding of the evolution frequencies. The increasing number of high field NMR magnets and the development of techniques to enhance NMR sensitivity will contribute to widen the use of these alternative sampling methods for the study of solids and mesophases in the coming years.

Non-uniform sampling: post-Fourier era of NMR data collection and processing

The invention of multidimensional techniques in the 1970s revolutionized NMR, making it the general tool of structural analysis of molecules and materials. In the most straightforward approach, the signal sampling in the indirect dimensions of a multidimensional experiment is performed in the same manner as in the direct dimension, i.e. with a grid of equally spaced points. This results in lengthy experiments with a resolution often far from optimum. To circumvent this problem, numerous sparse-sampling techniques have been developed in the last three decades, including two traditionally distinct approaches: the radial sampling and non-uniform sampling. This mini review discusses the sparse signal sampling and reconstruction techniques from the point of view of an underdetermined linear algebra problem that arises when a full, equally spaced set of sampled points is replaced with sparse sampling. Additional assumptions that are introduced to solve the problem, as well as the shape of the undersampled Fourier transform operator (visualized as so-called point spread function), are shown to be the main differences between various sparse-sampling methods.

Fast multi-dimensional NMR acquisition and processing using the sparse FFT

Increasing the dimensionality of NMR experiments strongly enhances the spectral resolution and provides invaluable direct information about atomic interactions. However, the price tag is high: long measurement times and heavy requirements on the computation power and data storage. We introduce sparse fast Fourier transform as a new method of NMR signal collection and processing, which is capable of reconstructing high quality spectra of large size and dimensionality with short measurement times, faster computations than the fast Fourier transform, and minimal storage for processing and handling of sparse spectra. The new algorithm is described and demonstrated for a 4D BEST-HNCOCA spectrum.

Probing Invisible, Excited Protein States by Non-Uniformly Sampled Pseudo-4D CEST Spectroscopy

Chemical exchange saturation transfer (CEST) NMR spectroscopy is a powerful tool for studies of slow timescale protein dynamics. Typical experiments are based on recording a large number of 2D data sets and quantifying peak intensities in each of the resulting planes. A weakness of the method is that peaks must be resolved in 2D spectra, limiting applications to relatively small proteins. Resolution is significantly improved in 3D spectra but recording uniformly sampled data is time-prohibitive. Here we describe non-uniformly sampled HNCO-based pseudo-4D CEST that provides excellent resolution in reasonable measurement times. Data analysis is done through fitting in the time domain, without the need of reconstructing the frequency dimensions, exploiting previously measured accurate peak positions in reference spectra. The methodology is demonstrated on several protein systems, including a nascent form of superoxide dismutase that is implicated in neurodegenerative disease.

Magic angle spinning NMR spectroscopy: a versatile technique for structural and dynamic analysis of solid-phase systems

Magic Angle Spinning (MAS) NMR spectroscopy is a powerful method for analysis of a broad range of systems, including inorganic materials, pharmaceuticals, and biomacromolecules. The recent developments in MAS NMR instrumentation and methodologies opened new vistas to atomic-level characterization of a plethora of chemical environments previously inaccessible to analysis, with unprecedented sensitivity and resolution.

Development of a method for reconstruction of crowded NMR spectra from undersampled time-domain data

NMR is a unique methodology for obtaining information about the conformational dynamics of proteins in heterogeneous biomolecular systems. In various NMR methods, such as transferred cross-saturation, relaxation dispersion, and paramagnetic relaxation enhancement experiments, fast determination of the signal intensity ratios in the NMR spectra with high accuracy is required for analyses of targets with low yields and stabilities. However, conventional methods for the reconstruction of spectra from undersampled time-domain data, such as linear prediction, spectroscopy with integration of frequency and time domain, and analysis of Fourier, and compressed sensing were not effective for the accurate determination of the signal intensity ratios of the crowded two-dimensional spectra of proteins. Here, we developed an NMR spectra reconstruction method, "conservation of experimental data in analysis of Fourier" (Co-ANAFOR), to reconstruct the crowded spectra from the undersampled time-domain data. The number of sampling points required for the transferred cross-saturation experiments between membrane proteins, photosystem I and cytochrome b 6 f, and their ligand, plastocyanin, with Co-ANAFOR was half of that needed for linear prediction, and the peak height reduction ratios of the spectra reconstructed from truncated time-domain data by Co-ANAFOR were more accurate than those reconstructed from non-uniformly sampled data by compressed sensing.

Magic angle spinning NMR of viruses

Viruses, relatively simple pathogens, are able to replicate in many living organisms and to adapt to various environments. Conventional atomic-resolution structural biology techniques, X-ray crystallography and solution NMR spectroscopy provided abundant information on the structures of individual proteins and nucleic acids comprising viruses; however, viral assemblies are not amenable to analysis by these techniques because of their large size, insolubility, and inherent lack of long-range order. In this article, we review the recent advances in magic angle spinning NMR spectroscopy that enabled atomic-resolution analysis of structure and dynamics of large viral systems and give examples of several exciting case studies.

Magic angle spinning NMR of proteins: high-frequency dynamic nuclear polarization and (1)H detection

Magic angle spinning (MAS) NMR studies of amyloid and membrane proteins and large macromolecular complexes are an important new approach to structural biology. However, the applicability of these experiments, which are based on (13)C- and (15)N-detected spectra, would be enhanced if the sensitivity were improved. Here we discuss two advances that address this problem: high-frequency dynamic nuclear polarization (DNP) and (1)H-detected MAS techniques. DNP is a sensitivity enhancement technique that transfers the high polarization of exogenous unpaired electrons to nuclear spins via microwave irradiation of electron-nuclear transitions. DNP boosts NMR signal intensities by factors of 10(2) to 10(3), thereby overcoming NMR's inherent low sensitivity. Alternatively, it permits structural investigations at the nanomolar scale. In addition, (1)H detection is feasible primarily because of the development of MAS rotors that spin at frequencies of 40 to 60 kHz or higher and the preparation of extensively (2)H-labeled proteins.

NMR Methods for the Study of Instrinsically Disordered Proteins Structure, Dynamics, and Interactions: General Overview and Practical Guidelines

Thanks to recent improvements in NMR instrumentation, pulse sequence design, and sample preparation, a panoply of new NMR tools has become available for atomic resolution characterization of intrinsically disordered proteins (IDPs) that are optimized for the particular chemical and spectroscopic properties of these molecules. A wide range of NMR observables can now be measured on increasingly complex IDPs that report on their structural and dynamic properties in isolation, as part of a larger complex, or even inside an entire living cell. Herein we present basic NMR concepts, as well as optimised tools available for the study of IDPs in solution. In particular, the following sections are discussed hereafter: a short introduction to NMR spectroscopy and instrumentation (Sect. 3.1), the effect of order and disorder on NMR observables (Sect. 3.2), particular challenges and bottlenecks for NMR studies of IDPs (Sect. 3.3), 2D HN and CON NMR experiments: the fingerprint of an IDP (Sect. 3.4), tools for overcoming major bottlenecks of IDP NMR studies (Sect. 3.5), 13C detected experiments (Sect. 3.6), from 2D to 3D: from simple snapshots to site-resolved characterization of IDPs (Sect. 3.7), sequential NMR assignment: 3D experiments (Sect. 3.8), high-dimensional NMR experiments (nD, with n>3) (Sect. 3.9) and conclusions and perspectives (Sect. 3.10).

Nonuniform sampling and non-Fourier signal processing methods in multidimensional NMR

Beginning with the introduction of Fourier Transform NMR by Ernst and Anderson in 1966, time domain measurement of the impulse response (the free induction decay, FID) consisted of sampling the signal at a series of discrete intervals. For compatibility with the discrete Fourier transform (DFT), the intervals are kept uniform, and the Nyquist theorem dictates the largest value of the interval sufficient to avoid aliasing. With the proposal by Jeener of parametric sampling along an indirect time dimension, extension to multidimensional experiments employed the same sampling techniques used in one dimension, similarly subject to the Nyquist condition and suitable for processing via the discrete Fourier transform. The challenges of obtaining high-resolution spectral estimates from short data records using the DFT were already well understood, however. Despite techniques such as linear prediction extrapolation, the achievable resolution in the indirect dimensions is limited by practical constraints on measuring time. The advent of non-Fourier methods of spectrum analysis capable of processing nonuniformly sampled data has led to an explosion in the development of novel sampling strategies that avoid the limits on resolution and measurement time imposed by uniform sampling. The first part of this review discusses the many approaches to data sampling in multidimensional NMR, the second part highlights commonly used methods for signal processing of such data, and the review concludes with a discussion of other approaches to speeding up data acquisition in NMR.

Nonuniform sampling and non-Fourier signal processing methods in multidimensional NMR

Beginning with the introduction of Fourier Transform NMR by Ernst and Anderson in 1966, time domain measurement of the impulse response (the free induction decay, FID) consisted of sampling the signal at a series of discrete intervals. For compatibility with the discrete Fourier transform (DFT), the intervals are kept uniform, and the Nyquist theorem dictates the largest value of the interval sufficient to avoid aliasing. With the proposal by Jeener of parametric sampling along an indirect time dimension, extension to multidimensional experiments employed the same sampling techniques used in one dimension, similarly subject to the Nyquist condition and suitable for processing via the discrete Fourier transform. The challenges of obtaining high-resolution spectral estimates from short data records using the DFT were already well understood, however. Despite techniques such as linear prediction extrapolation, the achievable resolution in the indirect dimensions is limited by practical constraints on measuring time. The advent of non-Fourier methods of spectrum analysis capable of processing nonuniformly sampled data has led to an explosion in the development of novel sampling strategies that avoid the limits on resolution and measurement time imposed by uniform sampling. The first part of this review discusses the many approaches to data sampling in multidimensional NMR, the second part highlights commonly used methods for signal processing of such data, and the review concludes with a discussion of other approaches to speeding up data acquisition in NMR.

Accurate scoring of non-uniform sampling schemes for quantitative NMR

Non-uniform sampling (NUS) in NMR spectroscopy is a recognized and powerful tool to minimize acquisition time. Recent advances in reconstruction methodologies are paving the way for the use of NUS in quantitative applications, where accurate measurement of peak intensities is crucial. The presence or absence of NUS artifacts in reconstructed spectra ultimately determines the success of NUS in quantitative NMR. The quality of reconstructed spectra from NUS acquired data is dependent upon the quality of the sampling scheme. Here we demonstrate that the best performing sampling schemes make up a very small percentage of the total randomly generated schemes. A scoring method is found to accurately predict the quantitative similarity between reconstructed NUS spectra and those of fully sampled spectra. We present an easy-to-use protocol to batch generate and rank optimal Poisson-gap NUS schedules for use with 2D NMR with minimized noise and accurate signal reproduction, without the need for the creation of synthetic spectra.

Sensitivity gains, linearity, and spectral reproducibility in nonuniformly sampled multidimensional MAS NMR spectra of high dynamic range

Recently, we have demonstrated that considerable inherent sensitivity gains are attained in MAS NMR spectra acquired by nonuniform sampling (NUS) and introduced maximum entropy interpolation (MINT) processing that assures the linearity of transformation between the time and frequency domains. In this report, we examine the utility of the NUS/MINT approach in multidimensional datasets possessing high dynamic range, such as homonuclear (13)C-(13)C correlation spectra. We demonstrate on model compounds and on 1-73-(U-(13)C,(15)N)/74-108-(U-(15)N) E. coli thioredoxin reassembly, that with appropriately constructed 50% NUS schedules inherent sensitivity gains of 1.7-2.1-fold are readily reached in such datasets. We show that both linearity and line width are retained under these experimental conditions throughout the entire dynamic range of the signals. Furthermore, we demonstrate that the reproducibility of the peak intensities is excellent in the NUS/MINT approach when experiments are repeated multiple times and identical experimental and processing conditions are employed. Finally, we discuss the principles for design and implementation of random exponentially biased NUS sampling schedules for homonuclear (13)C-(13)C MAS correlation experiments that yield high-quality artifact-free datasets.

Perspectives in magnetic resonance: NMR in the post-FFT era

Multi-dimensional NMR spectra have traditionally been processed with the fast Fourier transformation (FFT). The availability of high field instruments, the complexity of spectra of large proteins, the narrow signal dispersion of some unstructured proteins, and the time needed to record the necessary increments in the indirect dimensions to exploit the resolution of the highfield instruments make this traditional approach unsatisfactory. New procedures need to be developed beyond uniform sampling of the indirect dimensions and reconstruction methods other than the straight FFT are necessary. Here we discuss approaches of non-uniform sampling (NUS) and suitable reconstruction methods. We expect that such methods will become standard for multi-dimensional NMR data acquisition with complex biological macromolecules and will dramatically enhance the power of modern biological NMR.

Covariance spectroscopy in high-resolution multi-dimensional solid-state NMR

Covariance spectroscopy (COV), a statistical method that provides increased sensitivity, can be applied to two-dimensional high-resolution solid-state NMR experiments, such as homonuclear spin-exchange spectroscopy. We the alternative States sampling scheme to the experimental time by 50%. By combining COV with other processing methods for non-uniform sampling (NUS), many different three-dimensional experiments can be performed with substantial increases in overall sensitivity. As an example, we show a three-dimensional homonuclear spin-exchange/separated-local-field (SLF) spectrum that enables the assignment of resonances and the measurement of structural restraints from a single experiment performed in a limited amount of time.

The causality principle in the reconstruction of sparse NMR spectra

Improved quality of NMR spectra reconstruction from non-uniformly sampled data by harnessing the causality principle and prior knowledge about the signal phase.

Accelerating multidimensional NMR and MRI experiments using iterated maps

Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of magnetic resonance experiments. Here we discuss an approach for reconstructing multidimensional nuclear magnetic resonance (NMR) spectra and MR images from sparsely-sampled time domain data, by way of iterated maps. This method exploits the computational speed of the FFT algorithm and is done in a deterministic way, by reformulating any a priori knowledge or constraints into projections, and then iterating. In this paper we explain the motivation behind this approach, the formulation of the specific projections, the benefits of using a 'QUasi-Even Sampling, plus jiTter' (QUEST) sampling schedule, and various methods for handling noise. Applying the iterated maps method to real 2D NMR and 3D MRI of solids data, we show that it is flexible and robust enough to handle large data sets with significant noise and artifacts.

Sampling scheme and compressed sensing applied to solid-state NMR spectroscopy

We describe the incorporation of non-uniform sampling (NUS) compressed sensing (CS) into oriented sample (OS) solid-state NMR for stationary aligned samples and magic angle spinning (MAS) Solid-state NMR for unoriented 'powder' samples. Both simulated and experimental results indicate that 25-33% of a full linearly sampled data set is required to reconstruct two- and three-dimensional solid-state NMR spectra with high fidelity. A modest increase in signal-to-noise ratio accompanies the reconstruction.

Efficient resonance assignment of proteins in MAS NMR by simultaneous intra- and inter-residue 3D correlation spectroscopy

Resonance assignment is the first step in NMR structure determination. For magic angle spinning NMR, this is typically achieved with a set of heteronuclear correlation experiments (NCaCX, NCOCX, CONCa) that utilize SPECIFIC-CP (15)N-(13)C transfers. However, the SPECIFIC-CP transfer efficiency is often compromised by molecular dynamics and probe performance. Here we show that one-bond ZF-TEDOR (15)N-(13)C transfers provide simultaneous NCO and NCa correlations with at least as much sensitivity as SPECIFIC-CP for some non-crystalline samples. Furthermore, a 3D ZF-TEDOR-CC experiment provides heteronuclear sidechain correlations and robustness with respect to proton decoupling and radiofrequency power instabilities. We demonstrate transfer efficiencies and connectivities by application of 3D ZF-TEDOR-DARR to a model microcrystalline protein, GB1, and a less ideal system, GvpA in intact gas vesicles.

NASR: an effective approach for simultaneous noise and artifact suppression in NMR spectroscopy

As a powerful tool for biological analysis, especially protein structure and dynamic studies, nuclear magnetic resonance (NMR) spectroscopy suffers from intrinsic low signal to nose ratio (SNR) and long acquisition time required for multidimensional (nD) experiments. Nonuniform sampling (NUS) can effectively speed up the experiment but often introduces artifacts into the spectrum. In addition to the development of highly sensitive hardware and NMR pulse sequences, data postprocessing is a relative simple and cost-effective method to improve the SNR and suppress the artifacts. In this work, we propose an effective approach for simultaneously suppressing noise and artifacts based on the resampling principle. The method is named NASR for short and tested using one-, two-, and three-dimensional (1D, 2D, and 3D) NMR spectra that were acquired using ether conventional or NUS (spiral and random, for 3D) approaches. The results reveal that the NASR is fast and applicable for improving the quality of 1D to nD NMR spectra with all kinds of sampling schemes.

Exploring signal-to-noise ratio and sensitivity in non-uniformly sampled multi-dimensional NMR spectra

It is well established that non-uniform sampling (NUS) allows acquisition of multi-dimensional NMR spectra at a resolution that cannot be obtained with traditional uniform acquisition through the indirect dimensions. However, the impact of NUS on the signal-to-noise ratio (SNR) and sensitivity are less well documented. SNR and sensitivity are essential aspects of NMR experiments as they define the quality and extent of data that can be obtained. This is particularly important for spectroscopy with low concentration samples of biological macromolecules. There are different ways of defining the SNR depending on how to measure the noise, and the distinction between SNR and sensitivity is often not clear. While there are defined procedures for measuring sensitivity with high concentration NMR standards, such as sucrose, there is no clear or generally accepted definition of sensitivity when comparing different acquisition and processing methods for spectra of biological macromolecules with many weak signals close to the level of noise. Here we propose tools for estimating the SNR and sensitivity of NUS spectra with respect to sampling schedule and reconstruction method. We compare uniformly acquired spectra with NUS spectra obtained in the same total measuring time. The time saving obtained when only 1/k of the Nyquist grid points are sampled is used to measure k-fold more scans per increment. We show that judiciously chosen NUS schedules together with suitable reconstruction methods can yield a significant increase of the SNR within the same total measurement time. Furthermore, we propose to define the sensitivity as the probability to detect weak peaks and show that time-equivalent NUS can considerably increase this detection sensitivity. The sensitivity gain increases with the number of NUS indirect dimensions. Thus, well-chosen NUS schedules and reconstruction methods can significantly increase the information content of multidimensional NMR spectra of challenging biological macromolecules.

A time-saving strategy for MAS NMR spectroscopy by combining nonuniform sampling and paramagnetic relaxation assisted condensed data collection

We present a time-saving strategy for acquiring 3D magic angle spinning NMR spectra for chemical shift assignments in proteins and protein assemblies in the solid state. By simultaneous application of nonuniform sampling (NUS) and paramagnetic-relaxation-assisted condensed data collection (PACC), we can attain 16-fold time reduction in the 3D experiments without sacrificing the signal-to-noise ratio or the resolution. We demonstrate that with appropriate concentration of paramagnetic dopant introduced into the sample the overwhelming majority of chemical shifts are not perturbed, with the exception of a limited number of shifts corresponding to residues located at the surface of the protein, which exhibit small perturbations. This approach enables multidimensional MAS spectroscopy in samples of intrinsically low sensitivity and/or high spectral congestion where traditional experiments fail, and is especially beneficial for structural and dynamics studies of large proteins and protein assemblies.