Far from being static structures, it is now well accepted that proteins are highly dynamic entities even in the solid state. Here, we review recent progress in application of magic-angle spinning (MAS) NMR for studying the site-specific dynamics of proteins, as assessed using spin-relaxation and measurement of anisotropic spin interactions. We focus on the types of experimental data that are available, and how best to access the information which is provided by these experimental measurements; in particular, we provide a retrospective on the field in light of recent advances in analytical methodologies, which have led to a slow paradigm shift in how we interpret relaxation rate constant measurements made in the solid state.
Biomolecular condensates formed by liquid–liquid phase separation are inherently biphasic systems in which a condensed phase coexists with a surrounding dilute phase that contains vastly different solute concentrations. While NMR spectroscopy offers unique, quantitative access to molecular structure, interactions and dynamics, its application to phase-separated systems has long been challenged by signal overlap, slow diffusion, and the line broadening in the condensed phase. Recent methodological developments have begun to overcome these limitations by explicitly exploiting the physical properties of biomolecular condensates. In this review, we discuss emerging NMR approaches that enable the characterization of condensates in their native biphasic state. These include the diffusion-based strategy LLPS REDIFINE to disentangle dilute and condensed phase populations and enable quantitative extraction of interphase exchange and droplet properties, the magnetization-transfer-based solvent detection approach CONDENSE-MT, that provides access to NMR-invisible condensed phases, and multiplexed filtering schemes that resolve multiple coexisting components within complex condensates. Together, these methods extend the scope of NMR beyond homogeneous solutions, providing complementary and label-free insights into the structure, dynamics, and phase behavior of multicomponent biomolecular condensates.
Protein self-association and aggregation are common phenomena that occur in various environments, including live cells, research samples, and during bioprocessing or storage of biopharmaceuticals. They may be a part of native biological function, or cause diseases, it can be an artefact in protein research, a concerning phenomenon in biopharmaceutical protein formulation, or a favourable opportunity to spontaneously concentrate proteins. The consequences of protein self-association and aggregation vary across different fields, and therefore may require somewhat different analytical approaches for their assessment and characterization. In this review we focus on types of aggregation and self-association which occur in protein formulations prepared for diverse purposes, where the aggregation itself is not the main functional feature of a protein. We aim to first highlight some major pathways of protein self-interaction and outline the terminology around the process of protein molecules clumping together, and the level of structural changes involved for each major pathway. We will briefly overview various analytical methods for characterising protein aggregation and self-association, and then consider the role of NMR, highlighting NMR parameters that are frequently used to gain insight into these processes. We focus on signal line broadening, chemical shift perturbation, diffusion coefficients, relaxation parameters and spatially selective NMR as the main approaches used to characterise various protein particles and the kinetics of their formation. Lastly, we discuss in more detail a few recent examples of NMR applications to study protein self-association and aggregation mainly in biopharmaceutical context, of how NMR measurables can assist in profiling of higher-order-structure, studies of protein-excipient interactions in formulation development, study of liquid-liquid phase separation and assessment of protein behaviour in complex mixtures. We also discuss low-field NMR methods that are being developed for in-line process monitoring as well as quality control.
Fluorine-19 nuclear magnetic resonance (19F NMR) is an attractive and widely studied heteronuclear platform due to its high gyromagnetic ratio, wide chemical-shift dispersion, and negligible endogenous background. However, if only the intrinsically low thermal nuclear polarization is available, then, as with any NMR nucleus, 19F still suffers from a high limit of detection. Hyperpolarization addresses this bottleneck and delivers orders-of-magnitude signal enhancements by creating non-Boltzmann nuclear spin populations. Here, we review recent progress in hyperpolarized 19F NMR in liquids, with a primary emphasis on parahydrogen-based techniques, including both hydrogenative PHIP and reversible binding approaches such as SABRE. We discuss how polarization is generated and transferred, and how 19F-specific spin properties and relaxation pathways govern attainable polarization levels and lifetimes. Key determinants—including magnetic-field dependence, scalar-coupling topology, chemical exchange and hydrogenation kinetics, and catalyst/ligand design—are summarized as mechanistic and practical design variables for optimizing performance. We also survey complementary hyperpolarization strategies, including dissolution and Overhauser-effect DNP, CIDNP, and photo-CIDNP, highlighting their scope, strengths, and limitations. Finally, we outline emerging opportunities and design principles for robust, quantitative hyperpolarized 19F NMR protocols that enable sensitive spectroscopy and analysis in complex chemical and biological environments.
Dissolution-Dynamic Nuclear Polarization (D-DNP) addresses the most pressing issue of nuclear magnetic resonance spectroscopy — low sensitivity. In D-DNP the analyte is mixed with a radical in a glass-forming matrix. This substrate is frozen and kept at low temperature (<100 K) and a magnetic field of several Tesla. By using microwave irradiation, polarization is transferred from electron spins to nuclear spins. The substrate is then liquefied, and the liquid-state signal of the nuclear spins is observed in a high-resolution nuclear magnetic resonance (NMR) magnet or a magnetic resonance imaging scanner. The D-DNP technique has enabled spectacular experiments, such as the in vivo observation of human metabolism. However, unlike other sensitivity enhancement methodologies, such as cryoprobes or magic angle spinning (MAS) DNP, D-DNP is not applied broadly in NMR spectroscopy at present. Here, we describe (i) the gains of an ideal D-DNP experiment for NMR spectroscopy, and contrast them with the real implementations of the D-DNP experiment available today, with a focus on applications in spectroscopy. We review principles of (ii) the dynamic nuclear polarization step and (iii) the sample transfer. We argue (iv) that stringent automation is essential for broader adaptation of the D-DNP experiment.
G-quadruplexes (G4s) have emerged as one of the most exciting nucleic acid secondary structures. G-quadruplexes are non-canonical, four-stranded nucleic acid structures formed in sequences with consecutive runs of guanine bases. Unlike duplex DNA, G-quadruplexes are globularly folded structures and can readily form under physiologically relevant solution conditions. G-quadruplex structures have been found in biologically significant nucleic acid regions, including human telomeres, oncogene-promoter regions, replication initiation sites, and untranslated regions (UTRs) of mRNA. They have been shown to be important regulatory motifs in a number of critical cellular processes including gene transcription, translation, DNA replication, and genomic stability. G-quadruplexes have become a new class of molecular targets for drug development. Nuclear magnetic resonance (NMR) spectroscopy is the major method for studying the structures of G-quadruplexes under physiologically relevant solution conditions. NMR spectroscopy is a powerful tool for studying G-quadruplex interactions with small molecule ligands in solution. To date, most G-quadruplex structures have been determined using NMR techniques. This review provides a comprehensive overview of the NMR methods used to determine DNA G-quadruplex structures and their ligand interactions in solution. It covers essential steps such as resonance assignment, which is foundational for all NMR studies, as well as the determination of G-quadruplex folding topology and structure using NMR spectroscopy. Additionally, it discusses NMR structural studies of small molecule interactions with DNA G-quadruplexes. Through examples of NMR-based structure characterization of G-quadruplexes and G-quadruplex-ligand complexes, this review illustrates the rich information that NMR spectroscopy can provide, demonstrating its applicability to a broad range of biologically relevant DNA G-quadruplexes and their ligand interactions.
Traditionally, Nuclear Magnetic Resonance (NMR) infrastructures have relied on in-person access, requiring researchers to travel to centralized facilities to conduct experiments. However, recent advancements in remote access technologies, accelerated by the constraints imposed by the COVID-19 pandemic, have demonstrated the feasibility and strategic benefits of transitioning NMR operations toward remote accessibility. This review examines the key challenges and opportunities associated with remote access to NMR instrumentation, including standardized protocols for sample handling, secure authentication mechanisms, real-time instrument control, and data management. By establishing a unified framework for remote access, we aim to enhance the sustainability and accessibility of NMR facilities. Our findings highlight the necessity for collaborative efforts to develop best practices that ensure reproducibility, high-quality data acquisition, and equitable access to NMR infrastructure on a global scale.
Molecular translational diffusion measurements not only provide insights into the physical and functional properties of molecules, but they can also yield an understanding of the morphology of the media through which the molecules diffuse. Pulsed Gradient Spin Echo (PGSE) NMR has established itself as an indispensable methodology for the experimental quantification of molecular translational diffusion both in vitro and in vivo. An extensive family of PGSE pulse sequences have been developed over the years for an ever-expanding series of applications. In this review, a group of PGSE sequences, in which band-selective 1H Radio Frequency (RF) pulses are applied throughout the entire time course of a pulse sequence, is evaluated. The features and advantages of this group of 1H PGSE sequences, including (1) sensitivity enhancement, (2) circumvention of dynamic range problems, and (3) suppression of exchange effects, are discussed.
Gas adsorption plays a critical role in understanding fluid storage and transport within porous media, particularly in unconventional reservoirs, where it directly influences reservoir characterization and resource recovery. Moreover, it serves as one of the promising storage mechanisms for gases such as CO2, methane, and hydrogen, making its accurate estimation essential for both energy production and CO2 sequestration. Nuclear Magnetic Resonance (NMR) has emerged as a powerful tool in petrophysics, offering valuable insights into pore saturation, fluid typing, and pore size distribution. In addition, NMR can be used in situ to assess adsorption, a significant advantage over other laboratory techniques that cannot be applied directly in the field. Unlike conventional adsorption measurement tools that only provide total gas uptake, NMR offers the unique capability to quantify compositional gas adsorption, enabling differentiation between adsorption in different pore systems. This review provides a comprehensive analysis of the various approaches used to estimate gas adsorption in porous media and examines the role of NMR in petrophysical evaluations, highlighting key formulations and detailed methodologies. Advanced NMR techniques, including low-field spectroscopy, Magic Angle Spinning (MAS), and Magnetic Resonance Imaging (MRI), are discussed, including underlying theory, laboratory-scale applications, and inherent limitations. Although there have been no reported field applications of these methods for the quantification of gas adsorption, further research into this area will eventually make it possible to fully harness the potential of these methods in the future.
Quadrupolar nuclei with half-integer spin, which represent 66 % of the NMR-active isotopes, are present in a wide range of materials with applications in various fields, including heterogeneous catalysis, optoelectronics and energy. The solid-state NMR spectra of these isotopes are affected by quadrupolar interactions, which provide unique information on the local environment of these nuclei, in addition to their chemical shifts. These anisotropic interactions, which are generally larger than other internal spin interactions, split and broaden the NMR transitions, which reduce the sensitivity for the detection of these isotopes. In addition, the large dimensions of their density matrices and the numerous NMR transitions complicate the spin dynamics and can reduce the efficiency of coherence transfers, such as cross-polarization under magic-angle spinning (CPMAS), which is widely employed to boost the sensitivity for the detection of spin-1/2 isotopes. In the last decade, sensitivity gains provided by dynamic nuclear polarization (DNP) have been exploited to detect half-integer quadrupolar nuclei in solids. This review discusses the advantages and limitations of the different DNP-NMR techniques that have been proposed for the detection of these isotopes, including direct excitation and CPMAS, and two more recently introduced methods called PRESTO (Phase-shifted Recoupling Effects by Smooth Transfer of Order) and D-RINEPT (Dipolar-mediated Refocusing Insensitive Nuclei Enhanced by Polarization Transfer). We also show how these techniques can be applied to obtain new insights on the structure of materials, notably of their surfaces, and hence, contribute to extend the range of applications of the surface-enhanced NMR spectroscopy (DNP-SENS).
The successful application of solid-state nuclear magnetic resonance (ssNMR) spectroscopy to structural studies of biological macromolecules requires high spectral resolution. In the presence of abundant 1H spins, the spectral resolution in 13C or 15N chemical-shift encoding experiments depends critically on efficient heteronuclear spin decoupling at a given magnetic field and spinning frequency. Heteronuclear line widths are primarily influenced by heterogeneous broadening, exhibiting minimal dependence on field strength and MAS frequency (νr), provided optimal heteronuclear decoupling is applied. Decoupling schemes aim to minimize the effects of heteronuclear dipole-dipole coupling between 1H and other observed spins. Initial decoupling approaches included, continuous-wave (CW) decoupling schemes proposed by Bloom and Shoolery in 1955, and followed by various methods in the 1990's, including two-pulse phase-modulated (TPPM) and X-inverse-X (XiX) decoupling. Nevertheless, these schemes demonstrate limited tolerance to deviations from optimal parameters and their optimization with biomolecular samples is often time-intensive or even practically unattainable. More recent advancements include non-rotor-synchronized refocused continuous-wave (rCW) decoupling methods, which offer significant improvements over other methods. The robustness of rCW decoupling to variations in radio-frequency (RF) field amplitude (nutation frequency), offset, and MAS frequency is crucial for high-resolution spectra from insensitive samples. A phase-alternated refocused continuous-wave decoupling method (rCWApA) provides even better resolution, simplicity in setup, and robustness. This improvement is largely due to more effective cancellation of residual heteronuclear, 1H-13C, dipole-dipole coupling interactions which are influenced by homonuclear, 1H-1H, dipole-dipole couplings under RF irradiation. This review highlights key decoupling methods, with a focus on rCW and its variants. It presents experimental and numerical results demonstrating the superior efficiency of rCW methods, and provides theoretical insights to guide the design of decoupling strategies for enhanced sensitivity and resolution with minimal optimization and easy implementation across a range of MAS frequencies from 8 kHz to 100 kHz.
NMR spectroscopy is a versatile technique for studies of molecular structures, dynamic processes, and intermolecular interactions across a broad range of systems, including small molecules, macromolecules, biomolecular assemblies, and materials in both solution and solid-state environments. As the complexity of NMR studies continues to pose challenges for practitioners, the integration of machine learning is recognized as a promising research direction for improving data acquisition, processing, and analysis. Here, we summarize recent findings in this area, highlighting common applications such as signal detection, chemical shift assignment, structure determination, chemical shift prediction, non-uniform sampling reconstruction, and denoising. For each of these applications, we discuss machine learning methods, design choices, and key publicly available data repositories. We conclude by identifying major trends and emerging directions at the intersection of machine learning and NMR spectroscopy that could help advance research in the field.
The molecules in nematic liquid crystal phases move rapidly but not randomly producing partial molecular orientation described by sets of order parameters. The molecules of pure liquid crystals are flexible by virtue of bond rotational motion, which has a profound effect on the properties of the liquid crystal phase. NMR spectroscopy can study these phenomena by 1H, 2H and 13C resonances in the nematic and paranematic phases.
This review provides an up-to-date account of the development of two solid-state (SS)NMR methods for enhancing resolution and sensitivity, fast magic angle spinning (MAS) and dynamic nuclear polarization (DNP), and the resulting progress in surface science. We demonstrate the high resolution and efficiency that can be achieved by using two-dimensional homo- and heteronuclear correlation experiments with small rotors capable of MAS at rates exceeding 100 kHz. DNP has offered significant enhancements in signal sensitivity and allowed access to nuclei and experiments that are beyond the limits of conventional SSNMR. The continuing progress in fast MAS and DNP methodologies in recent years generated an unprecedented shift in SSNMR’s capabilities in the studies of surface and interface regions of solids, especially mesoporous supports and catalysts. We give numerous examples of recent applications and discuss the prospects for further improvements of both methods.
Studying multidomain proteins, especially those combining well-folded domains with intrinsically disordered regions (IDRs), requires specific Nuclear Magnetic Resonance (NMR) techniques to address their structural complexity. To illustrate this, we focus here on the nucleocapsid protein from SARS-CoV-2, which includes both structured and disordered regions. We applied a suite of NMR methods, combining ARTINA software for automatic assignment and structure modelling with multi-receiver experiments that simultaneously capture signals from different nuclear spins, increasing both data quality and acquisition efficiency. Studies of signal temperature-dependence, heteronuclear relaxation and secondary structure propensity (SSP) analysis, as well as experiments employing either 1H or 13C detection to achieve simultaneous snapshots of globular and disordered regions, were used to analyse both the isolated N-terminal domain (NTD) and a construct (NTR) comprising the NTD and two flanking highly disordered regions (IDR1, IDR2). This comprehensive approach allowed us to characterize the NTD's structure and to evaluate how the IDRs affect the overall conformation and dynamics, as well as the interaction with RNA. The findings underscore the importance of applying such a combination of tailored NMR techniques for effectively studying multidomain proteins with heterogeneous structural and dynamic properties.
Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community, and there is increasing interest in the practical aspects of building a magnetic resonance imaging instrument from scratch. This review is focused on the different steps involved in such an endeavour, the challenges encountered and their solutions; it is based on experience gained at a four-day “hackathon” (named “ezyMRI”) at Singapore University of Technology and Design in spring 2024. One day of this event was devoted to educational lectures and three days to system construction and testing; seventy young researchers from all parts of the world formed six teams focusing respectively on magnet, gradient coil, RF coil, console, system integration, and design, which together produced a working MRI instrument in three days.
Cardiovascular magnetic resonance (CMR) imaging is an established non-invasive tool for the assessment of cardiovascular diseases, which are the leading cause of death globally. CMR provides dynamic and static multi-contrast and multi-parametric images, including cine for functional evaluation, contrast-enhanced imaging and parametric mapping for tissue characterization, and MR angiography for the assessment of the aortic, coronary and pulmonary circulation. However, clinical CMR imaging sequences still have some limitations such as the requirement for multiple breath-holds, incomplete spatial coverage, complex planning and acquisition, low scan efficiency and long scan times. To address these challenges, novel techniques have been developed during the last two decades, focusing on automated planning and acquisition timing, improved respiratory and cardiac motion handling strategies, image acceleration algorithms employing undersampled reconstruction, all-in-one imaging techniques that can acquire multiple contrast/parameters in a single scan, deep learning based methods applied along the entire CMR imaging pipeline, as well as imaging at high- and low-field strengths. In this article, we aim to provide a comprehensive review of CMR imaging, covering both established and emerging techniques, to give an overview of the present and future applications of CMR.
Food metabolomics has emerged as a powerful tool for characterizing complex food systems, offering a non-targeted highly discriminative approach for detecting authenticity, assessing quality, and ensuring safety across an array of food matrices. By capturing the complete spectral signature of a sample and reducing it to manageable variables, this technique provides an extensive metabolite snapshot that encompasses everything from minor compounds to major constituents.A key advantage lies in the reproducibility and robustness of NMR spectroscopy, allowing the comparison of spectra even across different instruments and laboratories. Such comparability fosters collaborative efforts and facilitates the establishment of large, community-built datasets, which are critical for advancing reliable classification models and enabling wide-scale deployment of non-targeted protocols. Rigor in each step, ranging from selecting representative authentic samples to optimizing acquisition parameters, data processing, and classification algorithms, proves essential for achieving consistent, high-quality metabolomics data.As validation and standardization practices become more widely accepted, NMR-based non-targeted approaches will accelerate innovations in food product monitoring and labeling, reduce analytical uncertainties, and address emerging challenges in food fraud detection. Ultimately, by combining best-in-class protocols, collaborative networks, and open-access data repositories, non-targeted NMR metabolomics has the potential to revolutionize traceability and foster global consumer confidence in the authenticity and quality of the food supply chain.
