2D Nuclear Magnetic Resonance Spectra Research Articles

Relaxation Editing in NMR Using a New Two-Dimensional Long-Lived Coherence Method for Mixture Analysis.

The exploration of metabolomics and targeted segments of proteins stands as a pivotal facet of Nuclear Magnetic Resonance (NMR) analysis, furnishing valuable insights into molecular architectures and potential therapeutic applications. The issue of spectral congestion frequently presents challenges in ascribing distinct peaks within the confines of both one-dimensional (1D) and two-dimensional (2D) NMR spectra. Numerous strategies have been proposed to resolve specific resonances in NMR spectra differentially. Among these approaches, relaxation editing emerges as a viable solution. In the realm of relaxation phenomena within NMR, Long-Lived States (LLS) and Long-Lived Coherences (LLC) manifest as promising phenomena, offering enhanced relaxation lifetimes in comparison to the traditional longitudinal (T1) and transverse (T2) relaxation times for coupled nuclear spins. Notably, LLC presents a pathway to attenuate uncoupled high-intensity peaks, effectively diminishing their impact. The foundation of this technique rests upon the premise that the relaxation lifetime in the rotating frame (T1ρ) remains smaller than TLLC. In pursuit of refining spectral assignments within complex mixtures, we introduce a new pulse sequence tailored for LLC Total Correlation Spectroscopy (LLC-TOCSY). This demonstrates efficacy in extracting LLC signals within configurations involving multiple coupled spins, thereby decluttering the spectrum and enhancing the accuracy of peak assignments. To validate the effectiveness of this method, a collection of samples was subjected to scrutiny, yielding compelling results.

Nuclear magnetic resonance study on wettability of shale oil reservoir

In order to identify and evaluate reservoir wettability, so as to select suitable mining methods to improve oil recovery, wettability evaluation through one-dimensional (1D) and two-dimensional (2D) nuclear magnetic resonance (NMR) technology combined with physical simulation experiments was carried out in this study, and the feasibility of the experimental method was verified by contact Angle method. According to the principle of hydrophilic inorganic content and hydrophobic organic content of shale, the wettability can be evaluated through vacuum self-imbibition oil and self-imbibition water physical simulation experiments combined with 1D NMR technology, that is, total water absorption can be considered as inorganic content, organic content can be obtained by subtracting total water absorption from total oil absorption, and adsorbed oil content can be obtained by subtracting pore volume from total oil absorption. In addition to the wettability measurement by contact Angle method to verify the experimental method, the occurrence ratio of adsorbed oil can also be verified twice by 2D NMR spectrum. The results show that the error between the proportion of adsorbed oil measured by 2D NMR spectrum and that measured by self-imbibition method is within 4%. In the saturated oil-bound water state, the bound water is within the relaxation interval of 1<T1<10 ms and 1<T2<10 ms, and the oil signal is within the relaxation interval of T1>10 ms and T2>10 ms. And the relaxation time of aqueous phase moved to the right compared with that in the saturated water state, the interaction force between water and the pore wall was weakened, showing the characteristics of free fluid, and the rock sample became oil-wet, which was consistent with the wettability results measured by contact Angle. Therefore, the wettability of rock samples can be evaluated by 1D NMR technique combined with vacuum imbibition method, or by analyzing the changes of NMR spectra of aqueous phase in completely saturated water and saturated oil-bound water.

Deconvolution of 1D NMR spectra: A deep learning-based approach

The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and characterize their parameters, often referred to as deconvolution, is a crucial step in the quantification, elucidation, and verification of the structure of molecular systems. However, deconvolution of 1D NMR spectra is a challenge for both experts and machines. We propose a robust, expert-level quality deep learning-based deconvolution algorithm for 1D experimental NMR spectra. The algorithm is based on a neural network trained on synthetic spectra. Our customized pre-processing and labeling of the synthetic spectra enable the estimation of critical peak parameters. Furthermore, the neural network model transfers well to the experimental spectra and demonstrates low fitting errors and sparse peak lists in challenging scenarios such as crowded, high dynamic range, shoulder peak regions as well as broad peaks. We demonstrate in challenging spectra that the proposed algorithm is superior to expert results.

Speciation and Structures in Pt Surface Sites Stabilized by N-Heterocyclic Carbene Ligands Revealed by Dynamic Nuclear Polarization Enhanced Indirectly Detected 195Pt NMR Spectroscopic Signatures and Fingerprint Analysis.

N-Heterocyclic carbenes (NHCs) are widely used ligands in transition metal catalysis. Notably, they are increasingly encountered in heterogeneous systems. While a detailed knowledge of the possibly multiple metal environments would be essential to understand the activity of metal-NHC-based heterogeneous catalysts, only a few techniques currently have the ability to describe with atomic-resolution structures dispersed on a solid support. Here, we introduce a new dynamic nuclear polarization (DNP) surface-enhanced solid-state nuclear magnetic resonance (NMR) approach that, in combination with advanced density functional theory (DFT) calculations, allows the structure characterization of isolated silica-supported Pt-NHC sites. Notably, we demonstrate that the signal amplification provided by DNP in combination with fast magic angle spinning enables the implementation of sensitive 13C-195Pt correlation experiments. By exploiting 1J(13C-195Pt) couplings, 2D NMR spectra were acquired, revealing two types of Pt sites. For each of them, 1J(13C-195Pt) value was determined as well as 195Pt chemical shift tensor parameters. To interpret the NMR data, DFT calculations were performed on an extensive library of molecular Pt-NHC complexes. While one surface site was identified as a bis-NHC compound, the second site most likely contains a bidentate 1,5-cyclooctadiene ligand, pointing to various parallel grafting mechanisms. The methodology described here represents a new step forward in the atomic-level description of catalytically relevant surface metal-NHC complexes. In particular, it opens up innovative avenues for exploiting the spectral signature of platinum, one of the most widely used transition metals in catalysis, but whose use for solid-state NMR remains difficult. Our results also highlight the sensitivity of 195Pt NMR parameters to slight structural changes.

Influence of alkali metals on water dynamics inside imidazolium-based ionic liquid nano-domains.

The global need to expand the design of energy-storage devices led to the investigation of alkali metal - Ionic Liquid (IL) mixtures as a possible class of electrolytes. In this study, 1D and 2D Nuclear Magnetic Resonance (NMR) and Electrochemical Impedance Spectroscopy (EIS) as well as Molecular Dynamics (MD) simulations were used to study the intermolecular interactions in imidazolium-based IL - water - alkali halide ternary mixtures. The 1H and 23Na 1D and 1H DOSY NMR spectra revealed that the presence of small quantities of NaCl does not influence the aggregation of water molecules in the IL nano-domains. The order of adding ionic compounds to water, as well as the certain water and NaCl molecular ratios, lead to the formation of isolated water clusters. Two ternary solutions representing different orders of compounds mixing (H2O+ IL + NaCl or H2O+ NaCl + IL) showed a strong dependence of the initial solvation shell of Na+ and the self-clustering of water. Furthermore, the behaviour of water was found to be independent from the conditions applied during the solution preparation, such as temperature and/or duration of stirring and aging. These findings could be confirmed by large differences in the amount of ionic species, observed in the ternary solutions and depending on the order of mixing/solute preparation.

Solid-State NMR Spectra of Protons and Quadrupolar Nuclei at 28.2 T: Resolving Signatures of Surface Sites with Fast Magic Angle Spinning.

Advances in solid-state nuclear magnetic resonance (NMR) methods and hardware offer expanding opportunities for analysis of materials, interfaces, and surfaces. Here, we demonstrate the application of a very high magnetic field strength of 28.2 T and fast magic-angle-spinning rates (MAS, >40 kHz) to surface species relevant to catalysis. Specifically, we present as case studies the 1D and 2D solid-state NMR spectra of important catalyst and support materials, ranging from a well-defined silica-supported organometallic catalyst to dehydroxylated γ-alumina and zeolite solid acids. The high field and fast-MAS measurement conditions substantially improve spectral resolution and narrow NMR signals, which is particularly beneficial for solid-state 1D and 2D NMR analysis of 1H and quadrupolar nuclei such as 27Al at surfaces.

Solution nuclear magnetic resonance spectroscopy of bacterial outer membrane proteins in natively excreted vesicles using engineered Escherichia coli.

Gaining structural information on membrane proteins in their native lipid environment is a long‐standing challenge in molecular biology. Instead, it is common to employ membrane mimetics, which has been shown to affect protein structure, dynamics, and function severely. Here, we describe the incorporation of a bacterial outer membrane protein (OmpW) into natively excreted membrane vesicles for solution nuclear magnetic resonance (NMR) spectroscopy using a mutant Escherichia coli strain with a high outer membrane vesicle (OMV) production rate. We collected NMR spectra from both vesicles containing overexpressed OmpW and vesicles from a control strain to account for the presence of physiologically relevant outer membrane proteins in vesicles and observed distinct resonance signals from OmpW. Due to the increased production of OMVs and the use of non‐uniform sampling techniques we were able to obtain high‐resolution 2D (HSQC) and 3D (HNCO) NMR spectra of our target protein inside its native lipid environment. While this workflow is not yet sufficient to achieve in situ structure determination, our results pave the way for further research on vesicle‐based solution NMR spectroscopy.

1D and 2D NMR spectra of coffee from 27 countries.

Between 2012 and 2014, 715 green coffee samples were gathered by Almacafé S.A. (Bogotá, Colombia) from 27 countries. These were analysed at the nuclear magnetic resonance (NMR) laboratory at Universidad del Valle (Cali, Colombia). Over 1000 methanolic coffee extracts were prepared and 4563 spectra were acquired in a fully automatic manner using a 400MHz NMR spectrometer (Bruker Biospin, Germany). The dataset spans the variance that could be expected for an industrial application of origin monitoring, including samples from different harvest times, collected over several years, and processed by at least two distinct operators. The resulting 1D and 2D spectra can be used to develop and evaluate feature extraction methods, multivariate algorithms, and automation monitoring techniques. They can also be used as datasets for teaching, or as a reference for new studies of similar samples and approaches.

Sensory-guided isolation and identification of new sweet-tasting dammarane-type saponins from Jiaogulan (Gynostemma pentaphyllum) herbal tea

The purpose of this study was to elucidate the chemical basis for the sweet property produced by Gynostemma pentaphyllum and find new natural high-potency (HP) sweeteners. Sixteen new compounds (gypenosides YN 1–16) were obtained by sensory-guided isolation and identification, in which fifteen of them were sweet-tasting constituents with sweetness intensities 10–100 times higher than that of sucrose evaluated by human sensory panel test. Their structures were established by 1D and 2D nuclear magnetic resonance spectra, mass spectroscopy, infrared spectroscopy, UV–visible spectroscopy, and chemical method. Gypenoside YN 4 was the sweetest compound with a concentration of 15.504 ± 1.343 mg/kg, while gypenoside YN 12 has the highest concentration (1397.674 ± 12.948 mg/kg), as shown by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). Structure-activity relationship analysis implied that the compounds’ sweetness intensity was associated with side-chain substitutions at C-20 or the number of glucosyl groups at C-3. These new plant-derived natural products may be potential natural sweeteners.

Time- and site-resolved kinetic NMR for real-time monitoring of off-equilibrium reactions by 2D spectrotemporal correlations

Nuclear magnetic resonance (NMR) spectroscopy provides detailed information about dynamic processes through line-shape changes, which are traditionally limited to equilibrium conditions. However, a wealth of information is available by studying chemical reactions under off-equilibrium conditions—e.g., in states that arise upon mixing reactants that subsequently undergo chemical changes—and in monitoring the reactants and products in real time. Herein, we propose and demonstrate a time-resolved kinetic NMR experiment that combines rapid mixing techniques, continuous flow, and single-scan spectroscopic imaging methods, leading in unison to a 2D spectrotemporal NMR correlation that provides high-quality kinetic information of off-equilibrium chemical reactions. These kinetic 2D NMR spectra possess a high-resolution spectral dimension revealing the individual chemical sites, correlated with a time-independent, steady-state spatial axis that delivers information concerning temporal changes along the reaction coordinate. A comprehensive description of the kinetic, spectroscopic, and experimental features associated with these spectrotemporal NMR analyses is presented. Experimental demonstrations are carried out using an enzymatically catalyzed reaction leading to site- and time-resolved kinetic NMR data, that are in excellent agreement with control experiments and literature values.

SMART-Miner: A convolutional neural network-based metabolite identification from 1 H-13 C HSQC spectra.

The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to 1 H-13 C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR-based metabolomic tools.

Secondary Metabolites with Cytotoxic Activities from Streptomyces sp. BM-8 Isolated from the Feces of Equusquagga

A new aliphatic acid, compound 1, together with six known metabolites, including nonactic acid (2), homononactic acid (3), ethyl homononactate (4), homononactylhomononactate (5), valinomycin (6), and cyclo-(Pro-Leu) (7), was isolated from the culture broth of Streptomyces sp. BM-8, an actinobacterial strain isolated from the feces of Equus quagga. The structures of these compounds were established by analyses of spectroscopic data, including 1D and 2D nuclear magnetic resonance spectra (NMR), as well as by HR-ESI-MS spectrometry and chemical derivative analyses. Additionally, a serial analogue of nonactic acid and homononacticacid (8–21) was synthesized. The cytotoxicity of 1–21 wastested against a panel of cancer cell lines, such as HT-29, MCF-7, A375 and K562, with MTT assay. In addition, the cytotoxicity tests revealed that 1 was less cytotoxic toward a panel of cancerous cells, as compared with valinomycin (6).

Two-dimensional NMR inversion based on fast norm smoothing method

Two-dimensional (2D) nuclear magnetic resonance (NMR) inversion operates with massive echo train data and is an ill-posed problem. It is very important to select a suitable inversion method for the 2D NMR data processing. In this study, we propose a fast, robust, and effective method for 2D NMR inversion that improves the computational efficiency of the inversion process by avoiding estimation of some unneeded regularization parameters. Firstly, a method that combines window averaging (WA) and singular value decomposition (SVD) is used to compress the echo train data and obtain the singular values of the kernel matrix. Subsequently, an optimum regularization parameter in a fast manner using the signal-to-noise ratio (SNR) of the echo train data and the maximum singular value of the kernel matrix are determined. Finally, we use the Butler-Reeds-Dawson (BRD) method and the selected optimum regularization parameter to invert the compressed data to achieve a fast 2D NMR inversion. The numerical simulation results indicate that the proposed method not only achieves satisfactory 2D NMR spectra rapidly from the echo train data of different SNRs but also is insensitive to the number of the final compressed data points.

Nuclear Magnetic Resonance Methods in Structural Characterization of Glycosaminoglycans.

Glycosaminoglycans (GAGs) are sulfated glycans of complex structure and multiple biological actions. They are composed of disaccharide repeating units of alternating uronic acid/galactose and hexosamine. Sulfation patterns are an additional structural variation of these polymers. Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful analytical techniques employed in structural analysis of GAGs. 1D and 2D NMR spectra, both homonuclear 1H and heteronuclear 1H-13C, are the commonest NMR methods used. This chapter describes the overall experimental methods and materials necessary for adequate preparation of GAG samples for NMR investigations aimed to unveil the main structural characteristics of these biomacromolecules. The NMR methods discussed here cover all three isotopes (1H, 13C, and 15N) that can be exploited in structural analysis of GAGs. These NMR methods are described from an overall standpoint, to be applied to any GAG family, extracted from either natural or synthetic sources and destined to either basic research or pharmaceutical applications.

Paramagnetic spin labeling of a bacterial DnaB helicase for solid-state NMR

Labeling of biomolecules with a paramagnetic probe for nuclear magnetic resonance (NMR) spectroscopy enables determining long-range distance restraints, which are otherwise not accessible by classically used dipolar coupling-based NMR approaches. Distance restraints derived from paramagnetic relaxation enhancements (PREs) can facilitate the structure determination of large proteins and protein complexes. We herein present the site-directed labeling of the large oligomeric bacterial DnaB helicase from Helicobacter pylori with cysteine-reactive maleimide tags carrying either a nitroxide radical or a lanthanide ion. The success of the labeling reaction was followed by quantitative continuous-wave electron paramagnetic resonance (EPR) experiments performed on the nitroxide-labeled protein. PREs were extracted site-specifically from 2D and 3D solid-state NMR spectra. A good agreement with predicted PRE values, derived by computational modeling of nitroxide and Gd3+ tags in the low-resolution DnaB crystal structure, was found. Comparison of experimental PREs and model-predicted spin label-nucleus distances indicated that the size of the “blind sphere” around the paramagnetic center, in which NMR resonances are not detected, is slightly larger for Gd3+ (∼14 Å) than for nitroxide (∼11 Å) in 13C-detected 2D spectra of DnaB. We also present Gd3+-Gd3+ dipolar electron–electron resonance EPR experiments on DnaB supporting the conclusion that DnaB was present as a hexameric assembly.

Morphological Changes and Component Characterization of Coffee Silverskin.

Nuclear magnetic resonance (NMR) spectroscopy was used for the qualitative and quantitative analysis of aqueous extracts of unroasted and roasted coffee silverskin (CS). Twenty compounds were identified from 1D and 2D NMR spectra, including caffeine, chlorogenic acid (CGA), trigonelline, fructose, glucose, sucrose, etc. For the first time, the presence of trigonelline was detected in CS. Results of the quantitative analysis showed that the total amount of the main components after roasting was reduced by 45.6% compared with values before roasting. Sugars in the water extracts were the main components in CS, and fructose was the most abundant sugar, its relative content accounting for 38.7% and 38.4% in unroasted and roasted CS, respectively. Moreover, 1D NMR combined with 2D NMR technology shows application prospects in the rapid, non-destructive detection of CS. In addition, it was observed by optical microscopy and scanning electron microscopy (SEM) that the morphology of CS changed obviously before and after roasting.

Application of multiplet structure deconvolution to extract scalar coupling constants from 1D nuclear magnetic resonance spectra.

Multiplet structure deconvolution provides a robust method to determine the values of the coupling constants in first-order 1D nuclear magnetic resonance (NMR) spectra. Functions simplifying the coupling structure for partners with spin larger than and for doublets with unequal amplitudes were introduced. The chemical shifts of the coupling partners causing mild second-order effects can, in favourable cases, be calculated from the slopes measured in doublet structures. Illustrations demonstrate that deconvolution can straightforwardly analyse multiplet posing difficulties to humans and, in some cases, extract coupling constants from unresolved multiplets.

A review of NMR analysis in polysaccharide structure and conformation: Progress, challenge and perspective

Nuclear magnetic resonance (NMR) has been widely used as an analytical chemistry technique to investigate the molecular structure and conformation of polysaccharides. Combined with 1D spectra, chemical shifts and coupling constants in both homo- and heteronuclear 2D NMR spectra are able to infer the linkage and sequence of sugar residues. Besides, NMR has also been applied in conformation, quantitative analysis, cell wall in situ, degradation, polysaccharide mixture interaction analysis, as well as carbohydrates impurities profiling. This review summarizes the principle and development of NMR in polysaccharides analysis, and provides NMR spectra data collections of some common polysaccharides. It will help to promote the application of NMR in complex polysaccharides of biochemical interest, and provide valuable information on commercial polysaccharide products.

A New Brominated Norsesquiterpene Glycoside From the Rhizomes of Acorus tatarinowii Schott

A new brominated norsesquiterpene glycoside, acoruside (1), has been isolated from the rhizomes of Acorus tatarinowii Schott, together with 8 known compounds (2-9). Their structures were elucidated mainly based on 1-dimensional (1D) and 2D nuclear magnetic resonance spectra. The absolute configuration of compound 1 was determined by comparing its experimental and calculated electronic circular dichroism spectra. The in vitro tests indicated that at 10 µM, compounds 2, 3, and 4 aggravated serum deprivation injuries of PC12 cells, compound 2 aggravated rotenone-induced injuries of PC12 cells, and compounds 3 and 4 aggravated the oxygen-glucose deprivation-induced injuries of PC12 cells.

Improvement in Signal-to-Noise Ratio of Liquid-State NMR Spectroscopy via a Deep Neural Network DN-Unet.

Nuclear magnetic resonance (NMR) is one of the most powerful analytical tools and is extensively applied in many fields. However, compared to other spectroscopic techniques, NMR has lower sensitivity, impeding its wider applications. Using data postprocessing techniques to increase the NMR spectral signal-to-noise ratio (SNR) is a relatively simple and cost-effective method. In this work, a deep neural network, termed as DN-Unet, is devised to suppress noise in liquid-state NMR spectra to enhance SNR. It combines structures of encoder-decoder and convolutional neural network. Different from traditional deep learning training strategy, M-to-S strategy is developed to enhance DN-Unet capability that multiple noisy spectra (inputs) correspond to a same single noiseless spectrum (label) in the training stage. The trained 1D model can be used for denoising not only 1D but also high dimension spectra, further improving DN-Unet's performance. 1D, 2D, and 3D NMR spectra were utilized to evaluate DN-Unet performance. The results suggest that DN-Unet provides larger than 200-fold increase in SNR with weak peaks hidden in noise perfectly recovered and spurious peaks suppressed well. Since DN-Unet developed here to increase SNR is based on data postprocessing, it is universal for a variety of samples and NMR platforms. The great SNR enhancement and extreme excellence in differentiating signal and noise would greatly promote various liquid-state NMR applications.