Nuclear Magnetic Resonance Observables Research Articles

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data.

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

Tracking Li atoms in real-time with ultra-fast NMR simulations.

We present for the first time a multiscale machine learning approach to jointly simulate atomic structure and dynamics with the corresponding solid state Nuclear Magnetic Resonance (ssNMR) observables. We study the use-case of spin-alignment echo (SAE) NMR for exploring Li-ion diffusion within the solid state electrolyte material Li3PS4 (LPS) by calculating quadrupolar frequencies of 7Li. SAE NMR probes long-range dynamics down to microsecond-timescale hopping processes. Therefore only a few machine learning force field schemes are able to capture the time- and length scales required for accurate comparison with experimental results. By using a new class of machine learning interatomic potentials, known as ultra-fast potentials (UFPs), we are able to efficiently access timescales beyond the microsecond regime. In tandem, we have developed a machine learning model for predicting the full 7Li electric field gradient (EFG) tensors in LPS. By combining the long timescale trajectories from the UFP with our model for 7Li EFG tensors, we are able to extract the autocorrelation function (ACF) for 7Li quadrupolar frequencies during Li diffusion. We extract the decay constants from the ACF for both crystalline β-LPS and amorphous LPS, and find that the predicted Li hopping rates are on the same order of magnitude as those predicted from the Li dynamics. This demonstrates the potential for machine learning to finally make predictions on experimentally relevant timescales and temperatures, and opens a new avenue of NMR crystallography: using machine learning dynamical NMR simulations for accessing polycrystalline and glass ceramic materials.

Predicting 51V nuclear magnetic resonance observables in molecular crystals.

Solid-state nuclear magnetic resonance (NMR) spectroscopy and quantum chemical density functional theory (DFT) calculations are widely used to characterize vanadium centers in biological and pharmaceutically relevant compounds. Several techniques have been recently developed to improve the accuracy of predicted NMR parameters obtained from DFT. Fragment-based and planewave-corrected methods employing hybrid density functionals are particularly effective tools for solid-state applications. A recent benchmark study involving molecular crystal compounds found that fragment-based NMR calculations using hybrid density functionals improve the accuracy of predicted 51V chemical shieldings by 20% relative to traditional planewave methods. This work extends the previous study, including a careful analysis of 51V chemical shift anisotropy, electric field gradient calculations, and a more extensive test set. The accuracy of planewave-corrected techniques and recently developed fragment-based methods using electrostatic embedding based on the polarized continuum model (PCM) are found to be highly competitive with previous methods. Planewave-corrected methods achieve a 34% improvement in the errors of predicted 51V chemical shieldings relative to planewave. Additionally, planewave-corrected and fragment-based calculations were performed using PCM embedding, improving the accuracy of predicted 51V chemical shielding (CS) tensor principal values by 30% and values by 15% relative to traditional planewave methods. The performance of these methods is further examined using a redox-active oxovandium complex and a common 51V NMR reference compound.

Probing oxide-based glass structures by solid-state NMR: Opportunities and limitations

This Primer critically reviews the possibilities and limitations of probing the short-range structure of an oxide-based glass by routine magic-angle spinning (MAS) or triple-quantum MAS (3QMAS) nuclear magnetic resonance (NMR) experiments. We briefly outline the structural features of oxide-based glasses and the basics of solid-state NMR. Besides suggesting guidelines for selecting favorable experimental conditions and important considerations for recording high-quality MAS NMR spectra amenable for subsequent analysis, we review options for extracting NMR observables and their distributions from spins-1/2 as well as half-integer spin quadrupolar nuclei. Considering that the isotropic chemical shift remains the primary probe of local structure by MAS NMR, we thoroughly review its dependence on the short-range oxide-based glass parameters from the viewpoint of a very simple and intuitive but qualitative model. The utility of deconvolutions of notably 29Si and 31P MAS NMR spectra are discussed critically. We also suggest alternative yet qualitative analysis options that are available whenever MAS NMR spectral deconvolutions are not warranted without additional information, which incidentally applies to a majority of modern multicomponent glasses.

Do Models beyond Hybrid Density Functionals Increase the Agreement with Experiment for Predicted NMR Chemical Shifts or Electric Field Gradient Tensors in Organic Solids?

Ab initio predictions of chemical shifts and electric field gradient (EFG) tensor components are frequently used to help interpret solid-state nuclear magnetic resonance (NMR) experiments. Typically, these predictions employ density functional theory (DFT) with generalized gradient approximation (GGA) functionals, though hybrid functionals have been shown to improve accuracy relative to experiment. Here, the performance of a dozen models beyond the GGA approximation are examined for the prediction of solid-state NMR observables, including meta-GGA, hybrid, and double-hybrid density functionals and second-order Møller-Plesset perturbation theory (MP2). These models are tested on organic molecular crystal data sets containing 169 experimental 13C and 15N chemical shifts and 114 17O and 14N EFG tensor components. To make these calculations affordable, gauge-including projector augmented wave (GIPAW) Perdew-Burke-Ernzerhof (PBE) calculations with periodic boundary conditions are combined with a local intramolecular correction computed at the higher level of theory. Within the context of typical NMR property calculations performed on a static, DFT-optimized crystal structure, the benchmarking finds that the double-hybrid DFT functionals produce errors versus experiment that are no smaller than those of hybrid functionals in the best cases, and they can be larger. MP2 errors versus experiment are even bigger. Overall, no practical advantages are found for using any of the tested double-hybrid functionals or MP2 to predict experimental solid-state NMR chemical shifts and EFG tensor components for routine organic crystals, especially given the higher computational cost of those methods. This finding likely reflects error cancellation benefiting the hybrid functionals. Improving the accuracy of the predicted chemical shifts and EFG tensors relative to experiment would probably require more robust treatments of the crystal structures, their dynamics, and other factors.

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data.

Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available.

NMR Reveals Functionally Relevant Thermally Induced Structural Changes within the Native Ensemble of G-CSF.

Structure–function relationships in proteins refer to a trade-off between stability and bioactivity, molded by evolution of the molecule. Identifying which protein amino acid residues jeopardize global or local stability for the benefit of bioactivity would reveal residues pivotal to this structure–function trade-off. Here, we use 15N–1H heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy to probe the microenvironment and dynamics of residues in granulocyte colony-stimulating factor (G-CSF) through thermal perturbation. From this analysis, we identified four residues (G4, A6, T133, and Q134) that we classed as significant to global stability, given that they all experienced large environmental and dynamic changes and were closely correlated to each other in their NMR characteristics. Additionally, we observe that roughly four structural clusters are subject to localized conformational changes or partial unfolding prior to global unfolding at higher temperature. Combining NMR observables with structure relaxation methods reveals that these structural clusters concentrate around loop AB (binding site III inclusive). This loop has been previously implicated in conformational changes that result in an aggregation prone state of G-CSF. Residues H43, V48, and S63 appear to be pivotal to an opening motion of loop AB, a change that is possibly also important for function. Hence, we present here an approach to profiling residues in order to highlight their potential roles in the two vital characteristics of proteins: stability and bioactivity.

Efficiently Computing NMR 1H and 13C Chemical Shifts of Saccharides in Aqueous Environment.

Determining the structure of saccharides in their native environment is crucial to understanding their function and more accurately targeting their utilization. Nuclear magnetic resonance observables such as the nuclear Overhauser effect or spin-spin coupling constants are routinely utilized to study saccharides in their native water environment. However, while highly sensitive to the local environment, chemical shifts are mostly overlooked, despite being commonly measured for compounds identification. Although chemical shifts carry considerable structural information, their direct association with structure is notoriously difficult. This is mostly due to the similarity in the chemical nature of most saccharides causing similar physicochemical environments close to sugar C and H atoms, resulting in comparable chemical shifts. The rise of computational power allows one to compute reliable chemical shifts and use them to determine atomistic details of these sugars in solution. However, any prediction is severely limited by the computational protocol used and its accuracy. In this work, we studied a set of 31 saccharides on which we evaluated various computational protocols to calculate the total number of 375 1H and 327 13C chemical shifts of sugars in an aqueous environment. Our study proposes two cost-effective protocols for simulating 1H and 13C chemical shifts that we recommend for further use. These protocols can help with the interpretation of experimental spectra, but we also show that they are also capable of structure prediction independently. This is possible because of the low mean absolute deviations of calculated shifts from the experiment (0.06 ppm for 1H and 1.09 ppm for 13C). We explore different solvation methods, basis sets, and optimization schemes to reach such accuracy. A correct sampling of the conformation phase space of flexible sugar molecules is also key to obtaining accurately converged theoretical chemical shifts. The linear regression method was applied to convert the calculated isotropic nuclear magnetic shielding constants to simulated chemical shifts comparable with the experiment. The achieved level of accuracy can help in utilizing chemical shifts for elucidating the 3D atomistic structure of saccharides in aqueous solutions. All linear regression parameters obtained on our extensive set of sugars for all the tested protocols can be reutilized in future works.

Solid-state nuclear magnetic resonance of spin-9/2 nuclei 115 In and 209 Bi in functional inorganic complex oxides.

Indium and bismuth are technologically important elements, in particular as oxides for optoelectronic applications. 115 In and 209 Bi are both I = 9/2 nuclei with high natural abundances and moderately high frequencies but large nuclear electric quadrupole moments. Leveraging the quadrupolar interaction as a measure of local symmetry and polyhedral distortions for these nuclei could provide powerful insights on a range of applied materials. However, the absence of reported nuclear magnetic resonance (NMR) parameters on these nuclei, particularly in oxides, hinders their use by the broader materials community. In this contribution, solid-state 115 In and 209 Bi NMR of three recently discovered quaternary bismuth or indium oxides are reported, supported by density functional theory calculations, numerical simulations, diffraction and additional multinuclear (27 Al, 69,71 Ga, and 121 Sb) solid-state NMR measurements. The compounds LiIn2 SbO6 , BiAlTeO6 , and BiGaTeO6 are measured without special equipment at 9.4 T, demonstrating that wideline techniques such as the QCPMG pulse sequence and frequency-stepped acquisition can enable straightforward extraction of quadrupolar tensor information in I = 9/2 115 In and 209 Bi even in sites with large quadrupolar coupling constants. Relationships are described between the NMR observables and local site symmetry. These are amongst the first reports of the NMR parameters of 115 In, 121 Sb, and 209 Bi in oxides.

Chemical Analysis of Fluorobenzenes via Multinuclear Detection in the Strong Heteronuclear J-Coupling Regime

Chemical analysis via nuclear magnetic resonance (NMR) spectroscopy using permanent magnets, rather than superconducting magnets, is a rapidly developing field. Performing the NMR measurement in the strong heteronuclear J-coupling regime has shown considerable promise for the chemical analysis of small molecules. Typically, the condition for the strong heteronuclear J-coupling regime is satisfied at µT magnetic field strengths and enables high resolution J-coupled spectra (JCS) to be acquired. However, the JCS response to systematic chemical structural changes has largely not been investigated. In this report, we investigate the JCS of C6H6−xFx (x = 0, 1, 2, …, 6) fluorobenzene compounds via simultaneous excitation and detection of 19F and 1H at 51.5 µT. The results demonstrate that JCS are quantitative, and the common NMR observables, including Larmor frequency, heteronuclear and homonuclear J-couplings, relative signs of the J-coupling, chemical shift, and relaxation, are all measurable and are differentiable between molecules at low magnetic fields. The results, corroborated by ab initio calculations, provide new insights into the impact of chemical structure and their corresponding spin systems on JCS. In several instances, the JCS provided more chemical information than traditional high field NMR, demonstrating that JCS can be used for robust chemical analysis.

Molecular Conformations of Di-, Tri-, and Tetra-α-(2→8)-Linked Sialic Acid from NMR Spectroscopy and MD Simulations.

By using molecular dynamics simulations with an efficient enhanced sampling technique and in combination with nuclear magnetic resonance (NMR) spectroscopy quantitative structural information on -2,8-linked sialic acids is presented. We used a bottom-up approach to obtain a set of larger ensembles for tetra- and deca-sialic acid from model dimer and trimer systems that are in agreement with the available J-coupling constants and nuclear Overhauser effects. The molecular dynamic (MD) simulations with enhanced sampling are used to validate the force field used in this study for its further use. This empowered us to couple NMR observables in the MD framework via J-coupling and distance restraining simulations to obtain conformations that are supported by experimental data. We used these conformations in thermodynamic integration and one-step perturbation simulations to calculate the free-energy of suggested helical conformations. This study brings most of the available NMR experiments together and supplies information to resolve the conflict on the structures of poly--2,8-linked sialic acid.

Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data

Molecular dynamics (MD) simulations play an important role in characterizing Ribonucleic Acid (RNA) structure, augmenting information from experimental techniques such as Nuclear Magnetic Resonance (NMR). In this work, we examine the accuracy of structural representation resulting from application of a number of explicit and implicit solvent models and refinement protocols against experimental data ranging from high density of residual dipolar coupling (RDC) restraints to completely unrestrained simulations. For a prototype A-form RNA helix, our results indicate that AMBER RNA force field with either implicit or explicit solvent can produce a realistic dynamic representation of RNA helical structure, accurately cross-validating with respect to a diverse array of NMR observables. In refinement against NMR distance restraints, modern MD force fields are found to be equally adequate, with high fidelity cross-validation to the residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs), while slightly over-estimating structural order as monitored via NMR relaxation data. With restraints trimmed to encode only for base pairing information, cross-validation quality significantly deteriorates, now exhibiting a pronounced dependence on the choice of the solvent model. This deterioration is found to be partially reversible by increasing planarity restraints on the nucleobase geometry. For completely unrestrained MD simulations, the choice of water model becomes very important, with the best-performing TIP4P-Ew accurately reproducing both the RDC and RCSA data, while closely matching the NMR-derived order parameters. The information provided here will serve as a foundation for MD-based refinement of solution state NMR structures of RNA.

Importance of the Force Field Choice in Capturing Functionally Relevant Dynamics in the von Willebrand Factor.

Whether recent updates and new releases of atomistic force fields can model the structural and dynamical properties of proteins containing both folded and partially disordered domains is still unclear. To address this fundamental question, we tested eight recently released force fields against our set of nuclear magnetic resonance (NMR) observables for a complex and medically relevant system, the major factor VIII binding region on the von Willebrand factor. This biomedically important region comprises both a folded and a partially structured domain. By using an enhanced sampling technique (temperature replica-exchange molecular dynamics simulations), we find that some force fields indeed rise to the challenge and capture the structural and dynamical features of the NMR ensemble and, therefore, are the appropriate choice for simulations of proteins with partially structured domains. What is more, we show that only such force fields can qualitatively capture the effects of a pathogenic mutation on the structural ensemble.

Molecular Dynamics Simulations Combined with Nuclear Magnetic Resonance and/or Small-Angle X-ray Scattering Data for Characterizing Intrinsically Disordered Protein Conformational Ensembles.

The concept of intrinsically disordered proteins (IDPs) has emerged relatively slowly, but over the past 20 years, it has become an intense research area in structural biology. Indeed, because of their considerable flexibility and structural heterogeneity, the determination of IDP conformational ensemble is particularly challenging and often requires a combination of experimental measurements and computational approaches. With the improved accuracy of all-atom force fields and the increasing computing performances, molecular dynamics (MD) simulations have become more and more reliable to generate realistic conformational ensembles. And the combination of MD simulations with experimental approaches, such as nuclear magnetic resonance (NMR) and/or small-angle X-ray scattering (SAXS) allows one to converge toward a more accurate and exhaustive description of IDP structures. In this Review, we discuss the state of the art of MD simulations of IDP conformational ensembles, with a special focus on studies that back-calculated and directly compared theoretical and experimental NMR or SAXS observables, such as chemical shifts (CS), 3J-couplings (3Jc), residual dipolar couplings (RDC), or SAXS intensities. We organize the review in three parts. In the first section, we discuss the studies which used NMR and/or SAXS data to test and validate the development of force fields or enhanced sampling techniques. In the second part, we explore different methods for the refinement of MD-derived structural ensembles, such as NMR or SAXS data-restrained MD simulations or ensemble reweighting to better fit experiments. Finally, we survey some recent studies combining MD simulations with NMR and/or SAXS measurements to investigate the relationship between IDP conformational ensemble and biological activity, as well as their implication in human diseases. From this review, we noticed that quite a few studies compared MD-generated conformational ensembles with both NMR and SAXS measurements to validate IDP structures at both local and global levels. Yet, beside the IDP propensity to form local secondary structures, their dynamic extension or compactness also appears important for their activity. Thus, we believe that a close synergy between MD simulations, NMR, and SAXS experiments would be greatly appropriate to address the challenges of characterizing the disordered structures of proteins and their complexes, relative to their biological functions.

Characterization of Internal Protein Dynamics and Conformational Entropy by NMR Relaxation.

Recent studies suggest that the fast timescale motion of methyl-bearing side chains may play an important role in mediating protein activity. These motions have been shown to encapsulate the residual conformational entropy of the folded state that can potentially contribute to the energetics of protein function. Here, we provide an overview of how to characterize these motions using nuclear magnetic resonance (NMR) spin relaxation methods. The strengths and limitations of several techniques are highlighted in order to assist with experimental design. Particular emphasis is placed on the practical aspects of sample preparation, data collection, data fitting, and statistical analysis. Additionally, discussion of the recently refined "entropy meter" is presented and its use in converting NMR observables to conformational entropy is illustrated. Taken together, these methods should yield new insights into the complex interplay between structure and dynamics in protein function.

Control of Transmembrane Helix Dynamics by Interfacial Tryptophan Residues

Transmembrane protein domains often contain interfacial aromatic residues, which may play a role in the insertion and stability of membrane helices. Residues such as Trp or Tyr, therefore, are often found situated at the lipid-water interface. We have examined the extent to which the precise radial locations of interfacial Trp residues may influence peptide helix orientation and dynamics. To address these questions, we have modified the GW5,19ALP23 (acetyl-GGALW5(LA)6LW19LAGA-[ethanol]amide) model peptide framework to relocate the Trp residues. Peptide orientation and dynamics were analyzed by means of solid-state nuclear magnetic resonance (NMR) spectroscopy to monitor specific 2H- and 15N-labeled residues. GW5,19ALP23 adopts a defined, tilted orientation within lipid bilayer membranes with minimal evidence of motional averaging of NMR observables, such as 2H quadrupolar or 15N-1H dipolar splittings. Here, we examine how peptide dynamics are impacted by relocating the interfacial Trp (W) residues on both ends and opposing faces of the helix, for example by a 100° rotation on the helical wheel for positions 4 and 20. In contrast to GW5,19ALP23, the modified GW4,20ALP23 helix experiences more extensive motional averaging of the NMR observables in several lipid bilayers of different thickness. Individual and combined Gaussian analyses of the 2H and 15N NMR signals confirm that the extent of dynamic averaging, particularly rotational “slippage” about the helix axis, is strongly coupled to the radial distribution of the interfacial Trp residues as well as the bilayer thickness. Additional 2H labels on alanines A3 and A21 reveal partial fraying of the helix ends. Even within the context of partial unwinding, the locations of particular Trp residues around the helix axis are prominent factors for determining transmembrane helix orientation and dynamics within the lipid membrane environment.

Recent advances in measuring the kinetics of biomolecules by NMR relaxation dispersion spectroscopy

Protein function can be modulated or dictated by the amplitude and timescale of biomolecular motion, therefore it is imperative to study protein dynamics. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technique capable of studying timescales of motion that range from those faster than molecular reorientation on the picosecond timescale to those that occur in real-time. Across this entire regime, NMR observables can report on the amplitude of atomic motion, and the kinetics of atomic motion can be ascertained with a wide variety of experimental techniques from real-time to milliseconds and several nanoseconds to picoseconds. Still a four orders of magnitude window between several nanoseconds and tens of microseconds has remained elusive. Here, we highlight new relaxation dispersion NMR techniques that serve to cover this “hidden-time” window up to hundreds of nanoseconds that achieve atomic resolution while studying the molecule under physiological conditions.

NMR crystallography: structure and properties of materials from solid-state nuclear magnetic resonance observables.

This topical review provides a brief overview of recent developments in NMR crystallography and related NMR approaches to studying the properties of molecular and ionic solids. Areas of complementarity with diffraction-based methods are underscored. These include the study of disordered systems, of dynamic systems, and other selected examples where NMR can provide unique insights. Highlights from the literature as well as recent work from my own group are discussed.

Nuclear magnetic resonance in high magnetic field: Application to condensed matter physics

In this review, we describe the potentialities offered by the nuclear magnetic resonance (NMR) technique to explore at a microscopic level new quantum states of condensed matter induced by high magnetic fields. We focus on experiments realised in resistive (up to 34 T) or hybrid (up to 45 T) magnets, which open a large access to these quantum phase transitions. After an introduction on NMR observables, we consider several topics: quantum spin systems (spin–Peierls transition, spin ladders, spin nematic phases, magnetisation plateaus, and Bose–Einstein condensation of triplet excitations), the field-induced charge density wave (CDW) in high-Tc superconductors, and exotic superconductivity including the Fulde–Ferrel–Larkin–Ovchinnikov superconducting state and the field-induced superconductivity due to the Jaccarino–Peter mechanism. Dans cette revue, nous décrivons les opportunités offertes par la résonance magnétique nucléaire (RMN) pour étudier les propriétés microscopiques des nouveaux états quantiques de la matière induits par les champs magnétiques intenses. Nous mettons l'accent sur les expériences réalisées dans des bobines résistives (jusqu'à 34 T) ou hybrides (jusqu'à 45 T), qui ouvrent un large accès à ce type de transitions quantiques. Après avoir introduit les quantités observables par RMN, nous considérons plusieurs domaines de recherche : les systèmes de spins quantiques (la transition de spin–Peierls, les échelles de spin, les phases nématiques de spin, les plateaux d'aimantation et la condensation de Bose–Einstein des excitations triplets), l'onde de densité de charge induite sous champ dans les supraconducteurs à haute Tc, et la supraconductivité exotique, avec la phase supraconductrice Fulde–Ferrel–Larkin–Ovchinnikov et la supraconductivité induite sous champ de type Jaccarino–Peter.

Advanced Magnetic Resonance Techniques for the Structural Characterization of Aminoxyl Radicals and Their Inorganic-Organic Nanocomposite Systems.

Electron and nuclear spins are extremely sensitive probes of their local structural and dynamic surroundings. Their Zeeman energy levels are modified by different types of local magnetic and electric fields created by their structural environment, which influence their magnetic resonance condition. For this reason, electron spin resonance (ESR) and nuclear magnetic resonance (NMR) spectroscopies have become extremely powerful tools of structural analysis, which are being widely used for the structural characterization of complex solids. Following a brief introduction into the basic theoretical foundations the most commonly used techniques and their application towards the structural characterization of paramagnetic solids based on aminoxyl radicals and their inorganic-organic nanocomposites will be described. Both ESR and NMR observables are useful for monitoring intermolecular interactions between unpaired electron spins, which are particularly important for the design of organically based ferromagnetic systems. ESR and NMR methods based on this effect can be used for monitoring the synthesis of polynitroxides and for evaluating the catalytic function of aminoxyl intercalation compounds. Finally, the sensitivity of ESR signals to motional dynamics can be exploited for characterizing molecule-surface interactions in nanocomposite systems. In the context of the latter work recently developed signal enhancement strategies are described, using polarization transfer from electron spins to nuclear spins for NMR spectroscopic detection.