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

Detecting Submicromolar Analytes in Mixtures with a 5 min Acquisition on 600 MHz NMR Spectrometers

Amino compounds are widely present in complex mixtures in chemistry, biology, medicine, food, and environmental sciences involving drug impurities and metabolisms of proteins, biogenic amines, neurotransmitters, and pyrimidine in biological systems. Nuclear magnetic resonance (NMR) spectroscopy is an excellent tool for simultaneously identifying and quantifying these in-mixture compounds but has a limit-of-detection (LOD) over several micromolarities (>5 μM). To break such a sensitivity barrier, we developed a sensitive and rapid method by combining the probe-induced sensitivity enhancement and nonuniform-sampling-based 1H-13C HSQC 2D-NMR (PRISE-NUS-HSQC). We introduced two 13CH3 tags for each analyte to respectively increase the 1H and 13C abundances for up to 6 and 200 fold. This enabled high-resolution detection of 0.4-0.8 μM analytes in mixtures in 5 mm tubes with a 5 min acquisition on 600 MHz spectrometers. The method is much more sensitive and faster than traditional 1H-13C HSQC methods (∼50 μM, >10 h). Using sulfanilic acid as a single reference, furthermore, we established a database covering chemical shifts and relative-response factors for >100 compounds, enabling reliable identification and quantification. The method showed good quantitation linearity, accuracy, precision, and applicability in multiple biological matrices, offering a rapid and sensitive approach for quantitative analysis of large cohorts of chemical, medicinal, metabolomic, food, and other mixtures.

Kövér K. Ultrahigh-Resolution Homo- and Heterodecoupled 1H and TOCSY NMR Experiments

The original homonuclear decoupled (pure shift) experiments provide ultrahigh-resolution ¹H spectra of compounds containing NMR-active heteronuclei of low natural isotopic abundance (e.g., ¹³C or ¹⁵N). In contrast, molecules containing highly abundant heteronuclei (like ³¹P or ¹⁹F) give doublets or a multiple of doublets in their homonuclear decoupled spectra, depending on the number of heteronuclear coupling partners and the magnitude of the respective coupling constants. In these cases, the complex and frequently overlapping signals may hamper the unambiguous assignment of resonances. Here, we present new heteronuclear decoupled (HD) PSYCHE ¹H and TOCSY experiments, which result in simplified spectra with significantly increased resolution, allowing the reliable assessment of individual resonances. The utility of the experiments has been demonstrated on a challenging stereoisomeric mixture of a platinum-phosphine complex, where ultrahigh resolution of the obtained HD PSYCHE spectra made the structure elucidation of the chiral products feasible. HD PSYCHE methods can be potentially applied to other important ³¹P- or ¹⁹F-containing compounds in medicinal chemistry and metabolomics.

Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra

Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.

Elusive Toxin in Cleistanthus collinus Causing Vasoconstriction and Myocardial Depression: Detailed NMR Analyses and Biological Studies of Cleistanthoside A

Cleistanthus collinus leaf extracts are consumed for suicidal purposes in southern India. The boiled decoction is known to be more toxic than the fresh leaf juice. Although several compounds have been isolated and their toxicity tested, controversy remains as to which compounds are responsible for the high level of toxicity of C. collinus. We report herein that cleistanthoside A is the major toxin in the boiled aqueous extract of fresh leaves and causes death in rats in small doses. The toxicity of the boiled extract prepared in the manner described can be attributed entirely to cleistanthoside A. Cleistanthin A could also be isolated from the boiled extract, albeit in trace amounts. As hypotension not responding to vasoconstrictors is the cause of death in patients who have consumed the boiled extract, effects of cleistanthoside A on the determinants of blood pressure, namely, force of cardiac contraction and vascular resistance, were tested in isolated organ experiments. Cleistanthoside A has a direct vasoconstrictor effect; however, it inhibits ventricular contractility. Therefore, the notion that the shock in C. collinus poisoning is of vascular origin must be considered carefully, and the possibility of cardiogenic shock must be studied. We present the crystal structure of cleistanthin A and show the potency of fast NMR methods (NOAH4-BSCN-NUS) in the full spectral assignment of cleistanthoside A as a real-world sample of a natural product. We also compare the results of the NOAH4-BSCN-NUS NMR experiments with conventional NMR methods.

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

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

Expedited Nuclear Magnetic Resonance Assignment of Small- to Medium-Sized Molecules with Improved HSQC-CLIP-COSY Experiments

Resonance assignment is a pivotal step for any nuclear magnetic resonance (NMR) analysis, such as structure elucidation or the investigation of protein-ligand interactions. Both ¹H-¹³C heteronuclear single quantum correlation (HSQC) and ¹H-¹H correlation spectroscopy (COSY) two-dimensional (2D) experiments are invaluable for ¹H NMR assignment, by extending the high signal dispersion of ¹³C chemical shifts onto ¹H resonances and by providing a high amount of through-bond ¹H-¹H connectivity information, respectively. The recently introduced HSQC-CLIP(Clean In-Phase)-COSY method combines these two experiments, providing COSY correlations along the high-resolution ¹³C dimension with clean in-phase multiplets. However, two experiments need to be recorded to unambiguously identify COSY cross-peaks. Here, we propose novel variants of the HSQC-CLIP-COSY pulse sequence that edit cross-peak signs so that direct HSQC responses can be distinguished from COSY relay peaks, and/or the multiplicities of the ¹³C nuclei are reflected, allowing the assignment of all the peaks in a single experiment. The advanced HSQC-CLIP-COSY variants have the potential to accelerate and simplify the NMR structure-elucidation process of both synthetic and natural products and to become valuable tools for high-throughput computer-assisted structure determination.

Covariance NMR: Theoretical concerns, practical considerations, contemporary applications and related techniques

The family of resolution enhancement and spectral reconstruction techniques collectively known as covariance NMR continues to expand, along with the list of applications for these techniques. Recent advances in covariance NMR include the utilization of covariance to reconstruct pure shift NMR spectra, and the growing use of covariance NMR in processing non-uniformly sampled data, especially in solid state NMR and metabolomics. This review describes theoretical and practical considerations for direct and indirect covariance NMR techniques, and summarizes recent additions to the covariance NMR family. The review also outlines some of the applications of covariance NMR, and places covariance NMR in the larger context of methods that use statistical and algebraic approaches to enhance and combine various kinds of spectroscopic data, including tensor-based approaches for multidimensional NMR and heterocovariance spectroscopy.

COLMAR Lipids Web Server and Ultrahigh-Resolution Methods for Two-Dimensional Nuclear Magnetic Resonance- and Mass Spectrometry-Based Lipidomics

Accurate identification of lipids in biological samples is a key step in lipidomics studies. Multidimensional nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool for this purpose as it provides comprehensive structural information on lipid composition at atomic resolution. However, the interpretation of NMR spectra of complex lipid mixtures is currently hampered by limited spectral resolution and the absence of a customized lipid NMR database along with user-friendly spectral analysis tools. We introduce a new two-dimensional (2D) NMR metabolite database "COLMAR Lipids" that was specifically curated for hydrophobic metabolites presently containing 501 compounds with accurate experimental 2D ¹³C-¹H heteronuclear single quantum coherence (HSQC) chemical shift data measured in CDCl₃. A new module in the public COLMAR suite of NMR web servers was developed for the (semi)automated analysis of complex lipidomics mixtures (http://spin.ccic.osu.edu/index.php/colmarm/index2). To obtain 2D HSQC spectra with the necessary high spectral resolution along both ¹³C and ¹H dimensions, nonuniform sampling in combination with pure shift spectroscopy was applied allowing the extraction of an abundance of unique cross-peaks belonging to hydrophobic compounds in complex lipidomics mixtures. As shown here, this information is critical for the unambiguous identification of underlying lipid molecules by means of the new COLMAR Lipids web server, also in combination with mass spectrometry, as is demonstrated for Caco-2 cell and lung tissue cell extracts.

Emerging solution NMR methods to illuminate the structural and dynamic properties of proteins

The first recognition of protein breathing was more than 50 years ago. Today, we are able to detect the multitude of interaction modes, structural polymorphisms, and binding-induced changes in protein structure that direct function. Solution-state NMR spectroscopy has proved to be a powerful technique, not only to obtain high-resolution structures of proteins, but also to provide unique insights into the functional dynamics of proteins. Here, we summarize recent technical landmarks in solution NMR that have enabled characterization of key biological macromolecular systems. These methods have been fundamental to atomic resolution structure determination and quantitative analysis of dynamics over a wide range of time scales by NMR. The ability of NMR to detect lowly populated protein conformations and transiently formed complexes plays a critical role in its ability to elucidate functionally important structural features of proteins and their dynamics.

Quo Vadis Biomolecular NMR Spectroscopy?

In-cell nuclear magnetic resonance (NMR) spectroscopy offers the possibility to study proteins and other biomolecules at atomic resolution directly in cells. As such, it provides compelling means to complement existing tools in cellular structural biology. Given the dominance of electron microscopy (EM)-based methods in current structure determination routines, I share my personal view about the role of biomolecular NMR spectroscopy in the aftermath of the revolution in resolution. Specifically, I focus on spin-off applications that in-cell NMR has helped to develop and how they may provide broader and more generally applicable routes for future NMR investigations. I discuss the use of ‘static’ and time-resolved solution NMR spectroscopy to detect post-translational protein modifications (PTMs) and to investigate structural consequences that occur in their response. I argue that available examples vindicate the need for collective and systematic efforts to determine post-translationally modified protein structures in the future. Furthermore, I explain my reasoning behind a Quinary Structure Assessment (QSA) initiative to interrogate cellular effects on protein dynamics and transient interactions present in physiological environments.

Real-Time Pure Shift HSQC NMR for Untargeted Metabolomics

Sensitivity and resolution are key considerations for NMR applications in general and for metabolomics in particular, where complex mixtures containing hundreds of metabolites over a large range of concentrations are commonly encountered. There is a strong demand for advanced methods that can provide maximal information in the shortest possible time frame. Here, we present the optimization and application of the recently introduced 2D real-time BIRD 1H-13C HSQC experiment for NMR-based metabolomics of aqueous samples at 13C natural abundance. For mouse urine samples, it is demonstrated how this real-time pure shift sensitivity-improved heteronuclear single quantum correlation method provides broadband homonuclear decoupling along the proton detection dimension and thereby significantly improves spectral resolution in regions that are affected by spectral overlap. Moreover, the collapse of the scalar multiplet structure of cross-peaks leads to a sensitivity gain of about 40-50% over a traditional 2D HSQC-SI experiment. The experiment works well over a range of magnetic field strengths and is particularly useful when resonance overlap in crowded regions of the HSQC spectra hampers accurate metabolite identification and quantitation.