Projection-reconstruction of three-dimensional NMR spectra

13C Direct Detected NMR for Challenging Systems

Thanks to recent improvements in NMR spectrometer hardware and pulse sequence design, modern 13C NMR has become a useful tool for biomolecular applications. The complete assignment of a protein can be accomplished by using 13C detected multinuclear experiments and it can provide unique information relevant for the study of a variety of different biomolecules including paramagnetic proteins and intrinsically disordered proteins. A wide range of NMR observables can be measured, concurring to the structural and dynamic characterization of a protein in isolation, as part of a larger complex, or even inside a living cell. We present the different properties of 13C with respect to 1H, which provide the rationale for the experiments developed and their application, the technical aspects that need to be faced, and the many experimental variants designed to address different cases. Application areas where these experiments successfully complement proton NMR are also described.

Non-uniform sampling for NOESY? A case study on spiramycin

To date, most nuclear magnetic resonance (NMR)-based 3-D structure determinations of both small molecules and of biopolymers utilize the nuclear Overhauser effect (NOE) via NOESY spectra. The acquisition of high-quality NOESY spectra is a prerequisite for quantitative analysis providing accurate interatomic distances. As the acquisition of NOE build-ups is time-consuming, acceleration of the process by the use of non-uniform sampling (NUS) may seem beneficial; however, the quantitativity of NOESY spectra acquired with NUS has not yet been validated. Herein, NOESY spectra with various extents of NUS have been recorded, artificial NUS spectra with two different sampling schemes created, and by using two different NUS reconstruction algorithms the influence of NUS on the data quality was evaluated. Using statistical analyses, NUS is demonstrated to influence the accuracy of quantitative NOE experiments. The NOE-based distances show an increased error as the sampling density decreases. Weak NOE signals are affected more severely by NUS than more intense ones. The application of NUS with NOESY comes at two major costs: the interatomic distances are determined with lower accuracy and long-range correlations are lost.

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.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

Homonuclear decoupling by projection reconstruction

Similar to J-resolved spectroscopy, also, heteronuclear multiple bond correlation (HMBC), heteronuclear single bond correlation (HSBC), and heteronuclear multiple quantum coherence (HMQC) types of correlation experiments result in homonuclear tilted multiplet patterns. On the example of the high-resolution heteronuclear single bond correlation (HR-HSBC) pulse sequence, it is shown how the tilt angle can be varied within a wide range of positive and negative values. Projection along the tilt angles in all cases results in homonuclear decoupling. Using well-known projection reconstruction techniques, the different tilt angles can be used to reconstruct a homonuclear decoupled two-dimensional correlation spectrum. The concept is proven and further refined by segmental projection reconstruction and the use of a clean in-phase heteronuclear single quantum correlation (CLIP-HSQC) spectrum with an effective zero tilt angle for further filtering. The proof of principle, its application to one-bond coupling measurement, as well as a basic HMBC, and a detailed discussion with comparison to other homodecoupling techniques are given.

Non-Uniform and Absolute Minimal Sampling for High-Throughput Multidimensional NMR Applications

Many biomolecular NMR applications can benefit from the faster acquisition of multidimensional NMR data with high resolution and their automated analysis and interpretation. In recent years, a number of non-uniform sampling (NUS) approaches have been introduced for the reconstruction of multidimensional NMR spectra, such as compressed sensing, thereby bypassing traditional Fourier-transform processing. Such approaches are applicable to both biomacromolecules and small molecules and their complex mixtures and can be combined with homonuclear decoupling (pure shift) and covariance processing. For homonuclear 2D TOCSY experiments, absolute minimal sampling (AMS) permits the drastic shortening of measurement times necessary for high-throughput applications for identification and quantification of components in complex biological mixtures in the field of metabolomics. Such TOCSY spectra can be comprehensively represented by graphic theoretical maximal cliques for the identification of entire spin systems and their subsequent query against NMR databases. Integration of these methods in webservers permits the rapid and reliable identification of mixture components. Recent progress is reviewed in this Minireview.

Ultrafast Four-Dimensional Coherent Spectroscopy by Projection Reconstruction

Multidimensional coherent spectroscopy provides insights into the vibronic structure and dynamics of complex systems. In general, the higher the dimensionality, the better the spectral discrimination and the more information that may be extracted about the system. A major impediment to widespread implementation of these methods, however, is that the acquisition time generally increases exponentially with dimensionality, prohibiting practical implementation. We demonstrate the use of nonuniform sampling based on the projection-slice theorem and inverse Radon transform within the context of a fifth-order, 4D technique (GAMERS) designed to correlate the vibrational contributions to different electronic states. Projection-reconstruction (PRO GAMERS) greatly reduces the data sampling requirements without sacrificing frequency resolution. The sensitivity of this technique is demonstrated to surpass conventional uniform sampling by orders of magnitude. The incorporation of projection-reconstruction into multidimensional coherent spectroscopy opens up the possibility to study the structure of complex chemical, biological, and physical systems with unprecedented detail.

How to face the low intrinsic sensitivity of 2D heteronuclear NMR with fast repetition techniques: go faster to go higher!

Nuclear magnetic resonance (NMR) is one of the most widely used analytical techniques in numerous domains where molecules are objects of investigation. However, major limitations of multidimensional NMR experiments come from their low sensitivity and from the long times needed for their acquisition. In order to overcome such limitations, fast repetition NMR techniques allowed for the reduction of 2D experimental time and for the conversion of the gained time into a higher number of scans leading to a better sensitivity. Thus, fast repetition 2D heteronuclear NMR techniques have allowed new advances in NMR, especially to access infomation on low abundant nuclei, to enhance the detection of low concentrated compounds and to probe weak interactions like hydrogen bonds at natural abundance. Copyright © 2017 John Wiley & Sons, Ltd.

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.

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.

A fast approach to 3D HSQC-based spectroscopy based on a Fourier phase encoding of pre-targeted resonances

Multidimensional Nuclear Magnetic Resonance (NMR) provides a unique window into structure and dynamics at an atomic level. Traditionally, given the scan-by-scan time modulation involved in these experiments, the duration of nD NMR increases exponentially with spectral dimensionality. In addition, acquisition times increase as the number of spectral elements being sought in each indirect domain - given by the ratio between the spectral bandwidth being targeted and the resolution desired. These long sampling times can be substantially reduced by exploiting information that is often available from lower-dimensionality acquisitions. This work presents a novel approach that exploits previous 2D information to speed up the acquisition of 3D spectra, based on what we denote as a Time-Optimized FouriEr Encoding (TOFEE) of pre-targeted peaks. Such 3D TOFEE experiments, which present points in common with Hadamard-encoded 3D acquisitions, do not necessarily require more scans than their 2D counterparts. This is here demonstrated based on extensions of 2D Heteronuclear Single-quantum Coherence (HSQC) experiments, to 3D HSQC-TOCSY or 3D HSQC-NOESY acquisitions. The theoretical basis of this new approach is given, and experimental demonstrations are presented on small molecule and protein-based model systems.

Absolute Minimal Sampling in High-Dimensional NMR Spectroscopy

Standard three-dimensional Fourier transform (FT) NMR experiments of molecular systems often involve prolonged measurement times due to extensive sampling required along the indirect time domains to obtain adequate spectral resolution. In recent years, a wealth of alternative sampling methods has been proposed to ease this bottleneck. However, due to their algorithmic complexity, for a given sample and experiment it is often hard to determine the minimal sampling requirement, and hence the maximal achievable experimental speed up. Herein we introduce an absolute minimal sampling (AMS) method that can be applied to common 3D NMR experiments. We show for the proteins ubiquitin and arginine kinase that for widely used experiments, such as 3D HNCO, accurate carbon frequencies can be obtained with a single time increment, while for others, such as 3D HN(CA)CO, all relevant information is obtained with as few as 6 increments amounting to a speed up of a factor 7-50.

Applications of high dimensionality experiments to biomolecular NMR

High dimensionality NMR experiments facilitate resonance assignment and precise determination of spectral parameters such as coupling constants. Sparse non-uniform sampling enables acquisition of experiments of high dimensionality with high resolution in acceptable time. In this review we present and compare some significant applications of NMR experiments of dimensionality higher than three in the field of biomolecular studies in solution.

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.

Six- and seven-dimensional experiments by combination of sparse random sampling and projection spectroscopy dedicated for backbone resonance assignment of intrinsically disordered proteins

Two novel six- and seven-dimensional NMR experiments are proposed. The new experiments employ non-uniform sampling that enables achieving high resolution in four indirectly detected dimensions and synchronous sampling in the additional dimensions using projection spectroscopy principle. The resulted data sets could be processed as five-dimensional data using existing software. The experiments facilitate resonance assignment of intrinsically disordered proteins. The novel experiments were successfully tested using 1 mM sample of α-synuclein on 600 and 800 MHz NMR spectrometers equipped with standard room temperature probes. The experiments allowed backbone assignment from a 1-day acquisition.

NMRFAM-SDF: a protein structure determination framework

The computationally demanding nature of automated NMR structure determination necessitates a delicate balancing of factors that include the time complexity of data collection, the computational complexity of chemical shift assignments, and selection of proper optimization steps. During the past two decades the computational and algorithmic aspects of several discrete steps of the process have been addressed. Although no single comprehensive solution has emerged, the incorporation of a validation protocol has gained recognition as a necessary step for a robust automated approach. The need for validation becomes even more pronounced in cases of proteins with higher structural complexity, where potentially larger errors generated at each step can propagate and accumulate in the process of structure calculation, thereby significantly degrading the efficacy of any software framework. This paper introduces a complete framework for protein structure determination with NMR--from data acquisition to the structure determination. The aim is twofold: to simplify the structure determination process for non-NMR experts whenever feasible, while maintaining flexibility by providing a set of modules that validate each step, and to enable the assessment of error propagations. This framework, called NMRFAM-SDF (NMRFAM-Structure Determination Framework), and its various components are available for download from the NMRFAM website (http://nmrfam.wisc.edu/software.htm).

ADAPT-NMR 3.0: utilization of BEST-type triple-resonance NMR experiments to accelerate the process of data collection and assignment

ADAPT-NMR (Assignment-directed Data collection Algorithm utilizing a Probabilistic Toolkit in NMR) is a software package whose Bayesian core uses on-the-fly chemical shift assignments to guide data acquisition by non-uniform sampling from a panel of through-bond NMR experiments. The new version of ADAPT-NMR (ADAPT-NMR v3.0) has the option of utilizing 2D tilted-plane versions of 3D fast spectral acquisition with BEST-type pulse sequences, while also retaining the capability of acquiring and processing data from tilted-plane versions of conventional sensitivity-enhanced experiments. The use of BEST experiments significantly reduces data collection times and leads to enhanced performance by ADAPT-NMR.

Nonuniform sampling of hypercomplex multidimensional NMR experiments: Dimensionality, quadrature phase and randomization

Nonuniform sampling (NUS) in multidimensional NMR permits the exploration of higher dimensional experiments and longer evolution times than the Nyquist Theorem practically allows for uniformly sampled experiments. However, the spectra of NUS data include sampling-induced artifacts and may be subject to distortions imposed by sparse data reconstruction techniques, issues not encountered with the discrete Fourier transform (DFT) applied to uniformly sampled data. The characterization of these NUS-induced artifacts allows for more informed sample schedule design and improved spectral quality. The DFT-Convolution Theorem, via the point-spread function (PSF) for a given sampling scheme, provides a useful framework for exploring the nature of NUS sampling artifacts. In this work, we analyze the PSFs for a set of specially constructed NUS schemes to quantify the interplay between randomization and dimensionality for reducing artifacts relative to uniformly undersampled controls. In particular, we find a synergistic relationship between the indirect time dimensions and the "quadrature phase dimension" (i.e. the hypercomplex components collected for quadrature detection). The quadrature phase dimension provides additional degrees of freedom that enable partial-component NUS (collecting a subset of quadrature components) to further reduce sampling-induced aliases relative to traditional full-component NUS (collecting all quadrature components). The efficacy of artifact reduction is exponentially related to the dimensionality of the sample space. Our results quantify the utility of partial-component NUS as an additional means for introducing decoherence into sampling schemes and reducing sampling artifacts in high dimensional experiments.

Reduced dimensionality (3,2)D NMR experiments and their automated analysis: implications to high-throughput structural studies on proteins

Protein NMR spectroscopy has expanded dramatically over the last decade into a powerful tool for the study of their structure, dynamics, and interactions. The primary requirement for all such investigations is sequence-specific resonance assignment. The demand now is to obtain this information as rapidly as possible and in all types of protein systems, stable/unstable, soluble/insoluble, small/big, structured/unstructured, and so on. In this context, we introduce here two reduced dimensionality experiments – (3,2)D-hNCOcanH and (3,2)D-hNcoCAnH – which enhance the previously described 2D NMR-based assignment methods quite significantly. Both the experiments can be recorded in just about 2-3 h each and hence would be of immense value for high-throughput structural proteomics and drug discovery research. The applicability of the method has been demonstrated using alpha-helical bovine apo calbindin-D9k P43M mutant (75 aa) protein. Automated assignment of this data using AUTOBA has been presented, which enhances the utility of these experiments. The backbone resonance assignments so derived are utilized to estimate secondary structures and the backbone fold using Web-based algorithms. Taken together, we believe that the method and the protocol proposed here can be used for routine high-throughput structural studies of 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).

A six-dimensional alpha proton detection-based APSY experiment for backbone assignment of intrinsically disordered proteins

Sequence specific resonance assignment is the prerequisite for the NMR-based analysis of the conformational ensembles and their underlying dynamics of intrinsically disordered proteins. However, rapid solvent exchange in intrinsically disordered proteins often complicates assignment strategies based on HN-detection. Here we present a six-dimensional alpha proton detection-based automated projection spectroscopy (APSY) experiment for backbone assignment of intrinsically disordered proteins. The 6D HCACONCAH APSY correlates the six different chemical shifts, H(α)(i - 1), C(α)(i - 1), C'(i - 1), N(i), Cα(i) and Hα(i). Application to two intrinsically disordered proteins, 140-residue α-synuclein and a 352-residue isoform of Tau, demonstrates that the chemical shift information provided by the 6D HCACONCAH APSY allows efficient backbone resonance assignment of intrinsically disordered proteins.

"CON-CON" assignment strategy for highly flexible intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are a class of highly flexible proteins whose characterization by NMR spectroscopy is complicated by severe spectral overlaps. The development of experiments designed to facilitate the sequence-specific assignment procedure is thus very important to improve the tools for the characterization of IDPs and thus to be able to focus on IDPs of increasing size and complexity. Here, we present and describe the implementation of a set of novel ¹H-detected 5D experiments, (HACA)CON(CACO)NCO(CA)HA, BT-(H)NCO(CAN)CONNH and BT-HN(COCAN)CONNH, optimized for the study of highly flexible IDPs that exploit the best resolved correlations, those involving the carbonyl and nitrogen nuclei of neighboring amino acids, to achieve sequence-specific resonance assignment. Together with the analogous recently proposed pulse schemes based on ¹³C detection, they form a complete set of experiments for sequence-specific assignment of highly flexible IDPs. Depending on the particular sample conditions (concentration, lifetime, pH, temperature, etc.), these experiments present certain advantages and disadvantages that will be discussed. Needless to say, that the availability of a variety of complementary experiments will be important for accurate determination of resonance frequencies in complex IDPs.

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.

Bayesian reconstruction of projection reconstruction NMR (PR-NMR)

Projection reconstruction nuclear magnetic resonance (PR-NMR) is a technique for generating multidimensional NMR spectra. A small number of projections from lower-dimensional NMR spectra are used to reconstruct the multidimensional NMR spectra. In our previous work, it was shown that multidimensional NMR spectra are efficiently reconstructed using peak-by-peak based reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. We propose an extended and generalized RJMCMC algorithm replacing a simple linear model with a linear mixed model to reconstruct close NMR spectra into true spectra. This statistical method generates samples in a Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of a protein HasA.

Strategy for automated NMR resonance assignment of RNA: application to 48-nucleotide K10

A procedure is presented for automated sequence-specific assignment of NMR resonances of uniformly [(13)C, (15)N]-labeled RNA. The method is based on a suite of four through-bond and two through-space high-dimensional automated projection spectroscopy (APSY) experiments. The approach is exemplified with a 0.3 mM sample of an RNA stem-loop with 48 nucleotides, K10, which is responsible for dynein-mediated localization of Drosophila fs(1)K10 mRNA transcripts. The automated analysis of the APSY data led to highly accurate and precise 3- to 4-dimensional peak lists. They provided a reliable basis for the subsequent sequence-specific resonance assignment with the algorithm FLYA and resulted in the fully automated resonance assignment of more than 80 % of the resonances of the (13)C-(1)H moieties at the 1', 2', 5, 6, and 8 positions in the nucleotides. The procedure was robust with respect to numerous impurity peaks, low concentration of this for NMR comparably large RNA, and structural features such as a loop, single-nucleotide bulges and a non-Watson-Crick wobble base pairs. Currently, there is no precise chemical shift statistics (as used by FLYA) for RNA regions which deviate from the regular A-form helical structure. Reliable and precise peak lists are thus required for automated sequence-specific assignment, as provided by APSY.

Automated NMR resonance assignment strategy for RNA via the phosphodiester backbone based on high-dimensional through-bond APSY experiments

A fast, robust and reliable strategy for automated sequential resonance assignment for uniformly [(13)C, (15)N]-labeled RNA via its phosphodiester backbone is presented. It is based on a series of high-dimensional through-bond APSY experiments: a 5D HCP-CCH COSY, a 4D H1'C1'CH TOCSY for ribose resonances, a 5D HCNCH for ribose-to-base connection, a 4D H6C6C5H5 TOCSY for pyrimidine resonances, and a 4D H8C8(C)C2H2 TOCSY for adenine resonances. The utilized pulse sequences are partially novel, and optimized to enable long evolution times in all dimensions. The highly precise APSY peak lists derived with these experiments could be used directly for reliable automated resonance assignment with the FLYA algorithm. This approach resulted in 98 % assignment completeness for all (13)C-(1)H, (15)N1/9 and (31)P resonances of a stem-loop with 14 nucleotides.

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.

Practical aspects of NMR signal assignment in larger and challenging proteins

NMR has matured into a technique routinely employed for studying proteins in near physiological conditions. However, applications to larger proteins are impeded by the complexity of the various correlation maps necessary to assign NMR signals. This article reviews the data analysis techniques traditionally employed for resonance assignment and describes alternative protocols necessary for overcoming challenges in large protein spectra. In particular, simultaneous analysis of multiple spectra may help overcome ambiguities or may reveal correlations in an indirect manner. Similarly, visualization of orthogonal planes in a multidimensional spectrum can provide alternative assignment procedures. We describe examples of such strategies for assignment of backbone, methyl, and nOe resonances. We describe experimental aspects of data acquisition for the related experiments and provide guidelines for preliminary studies. Focus is placed on large folded monomeric proteins and examples are provided for 37, 48, 53, and 81 kDa proteins.

The nuclear Overhauser effect from a quantitative perspective

The nuclear Overhauser enhancement or effect (NOE) is the most important measure in liquid-state NMR with macromolecules. Thus, the NOE is the subject of numerous reviews and books. Here, the NOE is revisited in light of our recently introduced measurements of exact nuclear Overhauser enhancements (eNOEs), which enabled the determination of multiple-state 3D protein structures. This review encompasses all relevant facets from the theoretical considerations to the use of eNOEs in multiple-state structure calculation. Important aspects include a detailed presentation of the relaxation theory relevant for the nuclear Overhauser effect, the estimation of the correction for spin diffusion, the experimental determination of the eNOEs, the conversion of eNOE rates into distances and validation of their quality, the distance-restraint classification and the protocols for calculation of structures and ensembles.

Toward optimal-resolution NMR of intrinsically disordered proteins

Proteins, which, in their native conditions, sample a multitude of distinct conformational states characterized by high spatiotemporal heterogeneity, most often termed as intrinsically disordered proteins (IDPs), have become a target of broad interest over the past 15years. With the growing evidence of their important roles in fundamental cellular processes, there is an urgent need to characterize the conformational behavior of IDPs at the highest possible level. The unique feature of NMR spectroscopy in the context of IDPs is its ability to supply details of their structural and temporal alterations at atomic-level resolution. Here, we briefly review recently proposed NMR-based strategies to characterize transient states populated by IDPs and summarize the latest achievements and future prospects in methodological development. Because low chemical shift dispersion represents the major obstacle encountered when studying IDPs by nuclear magnetic resonance, particular attention is paid to techniques allowing one to approach the physical limits of attainable resolution.

NMR determines transient structure and dynamics in the disordered C-terminal domain of WASp interacting protein

WASp-interacting protein (WIP) is a 503-residue proline-rich polypeptide expressed in human T cells. The WIP C-terminal domain binds to Wiskott-Aldrich syndrome protein (WASp) and regulates its activation and degradation, and the WIP-WASp interaction has been shown to be critical for actin polymerization and implicated in the onset of WAS and X-linked thrombocytopenia. WIP is predicted to be an intrinsically disordered protein, a class of polypeptides that are of great interest because they violate the traditional structure-function paradigm. In this first (to our knowledge) study of WIP in its unbound state, we used NMR to investigate the biophysical behavior of WIP(C), a C-terminal domain fragment of WIP that includes residues 407-503 and contains the WASp-binding site. In light of the poor spectral dispersion exhibited by WIP(C) and the high occurrence (25%) of proline residues, we employed 5D-NMR(13)C-detected NMR experiments with nonuniform sampling to accomplish full resonance assignment. Secondary chemical-shift analysis, (15)N relaxation rates, and protection from solvent exchange all concurred in detecting transient structure located in motifs that span the WASp-binding site. Residues 446-456 exhibited a propensity for helical conformation, and an extended conformation followed by a short, capped helix was observed for residues 468-478. The (13)C-detected approach allows chemical-shift assignment in the WIP(C) polyproline stretches and thus sheds light on their conformation and dynamics. The effects of temperature on chemical shifts referenced to a denatured sample of the polypeptide demonstrate that heating reduces the structural character of WIP(C). Thus, we conclude that the disordered WIP(C) fragment is comprised of regions with latent structure connected by flexible loops, an architecture with implications for binding affinity and function.

Reduced dimensionality tailored HN(C)N experiments for facile backbone resonance assignment of proteins through unambiguous identification of sequential HSQC peaks

Two novel reduced dimensionality (RD) tailored HN(C)N [S.C. Panchal, N.S. Bhavesh, R.V. Hosur, Improved 3D triple resonance experiments, HNN and HN(C)N, for HN and 15N sequential correlations in (13C, 15N) labeled proteins: application to unfolded proteins, J. Biomol. NMR 20 (2001) 135-147] experiments are proposed to facilitate the backbone resonance assignment of proteins both in terms of its accuracy and speed. These experiments - referred here as (4,3)D-hNCOcaNH and (4,3)D-hNcoCANH - exploit the linear combination of backbone (15)N and (13)C'/(13)C(α) chemical shifts simultaneously to achieve higher peak dispersion and randomness along their respective F1 dimensions. Simply, this has been achieved by modulating the backbone (15)N(i) chemical shifts with that of (13)C' (i-1)/(13)C(α) (i-1) spins following the established reduced dimensionality NMR approach [T. Szyperski, D.C. Yeh, D.K. Sukumaran, H.N. Moseley, G.T. Montelione, Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment, Proc. Natl. Acad. Sci. USA 99 (2002) 8009-8014]. Though the modification is simple it has resulted an ingenious improvement of HN(C)N both in terms of peak dispersion and easiness of establishing the sequential connectivities. The increased dispersion along F1 dimension solves two purposes here: (i) resolves the ambiguities arising because of degenerate (15)N chemical shifts and (ii) reduces the signal overlap in F2((15)N)-F3((1)H) planes (an important requisite in HN(C)N based assignment protocol for facile and unambiguous identification of sequentially connected HSQC peaks). The performance of both these experiments and the assignment protocol has been demonstrated using bovine apo Calbindin-d9k (75 aa) and urea denatured UNC60B (a 152 amino acid ADF/cofilin family protein of Caenorhabditis elegans), as representatives of folded and unfolded protein systems, respectively.

Efficient protocol for backbone and side-chain assignments of large, intrinsically disordered proteins: transient secondary structure analysis of 49.2 kDa microtubule associated protein 2c

Microtubule-associated proteins (MAPs) are abundantly present in axons and dendrites, and have been shown to play crucial role during the neuronal morphogenesis. The period of main dendritic outgrowth and synaptogenesis coincides with high expression levels of one of MAPs, the MAP2c, in rats. The MAP2c is a 49.2 kDa intrinsically disordered protein. To achieve an atomic resolution characterization of such a large protein, we have developed a protocol based on the acquisition of two five-dimensional (13)C-directly detected NMR experiments. Our previously published 5D CACONCACO experiment (Nováček et al. in J Biomol NMR 50(1):1-11, 2011) provides the sequential assignment of the backbone resonances, which is not interrupted by the presence of the proline residues in the amino acid sequence. A novel 5D HC(CC-TOCSY)CACON experiment facilitates the assignment of the aliphatic side chain resonances. To streamline the data analysis, we have developed a semi-automated procedure for signal assignments. The obtained data provides the first atomic resolution insight into the conformational state of MAP2c and constitutes a model for further functional studies of MAPs.

4D experiments measured with APSY for automated backbone resonance assignments of large proteins

Detailed structural and functional characterization of proteins by solution NMR requires sequence-specific resonance assignment. We present a set of transverse relaxation optimization (TROSY) based four-dimensional automated projection spectroscopy (APSY) experiments which are designed for resonance assignments of proteins with a size up to 40 kDa, namely HNCACO, HNCOCA, HNCACB and HN(CO)CACB. These higher-dimensional experiments include several sensitivity-optimizing features such as multiple quantum parallel evolution in a 'just-in-time' manner, aliased off-resonance evolution, evolution-time optimized APSY acquisition, selective water-handling and TROSY. The experiments were acquired within the concept of APSY, but they can also be used within the framework of sparsely sampled experiments. The multidimensional peak lists derived with APSY provided chemical shifts with an approximately 20 times higher precision than conventional methods usually do, and allowed the assignment of 90 % of the backbone resonances of the perdeuterated primase-polymerase ORF904, which contains 331 amino acid residues and has a molecular weight of 38.4 kDa.

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.

Formalism for hypercomplex multidimensional NMR employing partial-component subsampling

Multidimensional NMR spectroscopy typically employs phase-sensitive detection, which results in hypercomplex data (and spectra) when utilized in more than one dimension. Nonuniform sampling approaches have become commonplace in multidimensional NMR, enabling dramatic reductions in experiment time, increases in sensitivity and/or increases in resolution. In order to utilize nonuniform sampling optimally, it is necessary to characterize the relationship between the spectrum of a uniformly sampled data set and the spectrum of a subsampled data set. In this work we construct an algebra of hypercomplex numbers suitable for representing multidimensional NMR data along with partial-component nonuniform sampling (i.e. the hypercomplex components of data points are subsampled). This formalism leads to a modified DFT-Convolution relationship involving a partial-component, hypercomplex point-spread function set. The framework presented here is essential for the continued development and appropriate characterization of partial-component nonuniform sampling.

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.

Solution structure of mouse hepatitis virus (MHV) nsp3a and determinants of the interaction with MHV nucleocapsid (N) protein

Coronaviruses (CoVs) are positive-sense, single-stranded, enveloped RNA viruses that infect a variety of vertebrate hosts. The CoV nucleocapsid (N) protein contains two structurally independent RNA binding domains, designated the N-terminal domain (NTD) and the dimeric C-terminal domain (CTD), joined by a charged linker region rich in serine and arginine residues (SR-rich linker). An important goal in unraveling N function is to molecularly characterize N-protein interactions. Recent genetic evidence suggests that N interacts with nsp3a, a component of the viral replicase. Here we present the solution nuclear magnetic resonance (NMR) structure of mouse hepatitis virus (MHV) nsp3a and show, using isothermal titration calorimetry, that MHV N219, an N construct that extends into the SR-rich linker (residues 60 to 219), binds cognate nsp3a with high affinity (equilibrium association constant [K(a)], [1.4 ± 0.3] × 10(6) M(-1)). In contrast, neither N197, an N construct containing only the folded NTD (residues 60 to 197), nor the CTD dimer (residues 260 to 380) binds nsp3a with detectable affinity. This indicates that the key nsp3a binding determinants localize to the SR-rich linker, a finding consistent with those of reverse genetics studies. NMR chemical shift perturbation analysis reveals that the N-terminal region of an MHV N SR-rich linker peptide (residues 198 to 230) binds to the acidic face of MHV nsp3a containing the acidic α2 helix with an affinity (expressed as K(a)) of 8.1 × 10(3) M(-1). These studies reveal that the SR-rich linker of MHV N is necessary but not sufficient to maintain this high-affinity binding to N.

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.

Low concentration of a Gd-chelate increases the signal-to-noise ratio in fast pulsing BEST experiments

Despite numerous developments in the past few years that aim to increase the sensitivity of NMR multidimensional experiments, NMR spectroscopy still suffers from intrinsic low sensitivity. In this report, we show that the combination of two developments in the field, the Band-selective Excitation Short-Transient (BEST) experiment [Schanda et al., J. Am. Chem. Soc., 128 (2006) 9042] and the addition of the nonionic paramagnetic gadolinium chelate gadodiamide into NMR samples, enhances the signal-to-noise ratio. This effect is shown here for four different proteins, three globular and one unfolded, of molecular weights ranging from 6.5 kDa to 40 kDa, using 2D BEST HSQC and 3D BEST triple resonance sequences. Moreover, we show that the increase in signal-to-noise ratio provided by the gadodiamide is higher for peak resonances with lower than average intensity in BEST experiments. It is interesting to note that these residues are on average the weakest ones in those experiments. In this case, the gadodiamide-mediated increase can reach a value of 60% for low and 30% for high molecular weight proteins respectively. An investigation into the origin of this "paramagnetic gain" in BEST experiments is presented.

Fast and accurate quantitative metabolic profiling of body fluids by nonlinear sampling of 1H–13C two-dimensional nuclear magnetic resonance spectroscopy

Two-dimensional (2D) nuclear magnetic resonance (NMR) methods have shown to be an excellent analytical tool for the identification and characterization of statistically relevant changes in low-abundance metabolites in body fluid. The advantage of 2D NMR in terms of minimized ambiguities in peak assignment, aided in metabolite identifications and comprehensive metabolic profiling comes with the cost of increased NMR data collection time; making it inconvenient choice for routine metabolic profiling. We present here a method for the reduction in NMR data collection time of 2D (1)H-(13)C NMR spectroscopy for the purpose of quantitative metabolic profiling. Our method combines three techniques; which are nonlinear sampling (NLS), forward maximum (FM) entropy reconstruction, and J-compensated quantitative heteronuclear single quantum (HSQC) (1)H-(13)C NMR spectra. We report here that approximately 22-fold reduction in 2D NMR data collection time for the body fluid samples can be achieved by this method, without any compromise in quantitative information recovery of various low abundance metabolites. The method has been demonstrated in standard mixture solution, native, and lyophilized human urine samples. Our proposed method has potential to make quantitative metabolic profiling by 2D NMR as a routine method for various metabonomic studies.