Atomic-level structure of the amorphous drug atuliflapon NMR crystallography.

We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify a number of specific conformations and interactions that stabilize the amorphous structure. These include preferred hydrogen-bonding motifs with water and with other drug molecules, as well as conformations of the cyclohexane and pyrazole rings that stabilize structure by indirectly allowing for optimization of hydrogen bonding.

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids.

Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy.

Structure determination of an amorphous drug through large-scale NMR predictions.

Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state nuclear magnetic resonance (NMR) is among the most popular methods to characterize amorphous materials, and molecular dynamics (MD) simulations can help describe the structure of disordered materials. However, directly relating MD to NMR experiments in molecular solids has been out of reach until now because of the large size of these simulations.

Atomic-Scale Description of Interfaces between Antigen and Aluminum-Based Adjuvants Used in Vaccines by Dynamic Nuclear Polarization (DNP) Enhanced NMR Spectroscopy.

The addition of aluminum-based adjuvants in vaccines enhances the immune response to antigens. The strength of antigen adsorption on adjuvant gels is known to modulate vaccine efficacy. However, a detailed understanding of the mechanisms of interaction between aluminum gels and antigens is still missing.

Homonuclear Decoupling in H NMR of Solids by Remote Correlation.

The typical linewidths of H NMR spectra of powdered organic solids at 111 kHz magic-angle spinning (MAS) are of the order of a few hundred Hz. While this is remarkable in comparison to the tens of kHz observed in spectra of static samples, it is still the key limit to the use of H in solid-state NMR, especially for complex systems. Here, we demonstrate a novel strategy to further improve the spectral resolution.

A Bayesian approach to NMR crystal structure determination.

Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally observed NMR chemical shifts and those of candidate structures. Chemical shifts for the candidate configurations have traditionally been computed by electronic-structure methods, and more recently predicted by machine learning.

High-resolution H NMR of powdered solids by homonuclear dipolar decoupling.

The development of homonuclear dipolar decoupling sequences to obtain high-resolution H NMR spectra from solids has recently celebrated its 50th birthday. Over the years, a series of different decoupling schemes have been developed, starting with the pioneering Lee-Goldburg and WAHUHA sequences up to the most recent generation of experimentally optimized phase-modulated schemes such as eDUMBO-1 and LG4. These schemes can all yield over an order of magnitude reduction in H NMR linewidths in solids.

Measurement of Proton Spin Diffusivity in Hydrated Cementitious Solids.

The study of hydration and crystallization processes involving inorganic oxides is often complicated by poor long-range order and the formation of heterogeneous domains or surface layers. In solid-state NMR, H-H spin diffusion analyses can provide information on spatial composition distributions, domain sizes, or miscibility in both ordered and disordered solids. Such analyses have been implemented in organic solids but crucially rely on separate measurements of the H spin diffusion coefficients in closely related systems.

Line narrowing in H NMR of powdered organic solids with TOP-CT-MAS experiments at ultra-fast MAS.

The residual broadening observed in H spectra of rigid organic solids at natural abundance under 111 kHz magic angle spinning (MAS) is typically a few hundred Hertz. Here we show that refocusable and non-refocusable interactions contribute roughly equally to this residual at high-fields (21.14 T), and suggest that the removal of the non-refocusable part will produce significant increase in spectral resolution.

Rapid Structure Determination of Molecular Solids Using Chemical Shifts Directed by Unambiguous Prior Constraints.

NMR-based crystallography approaches involving the combination of crystal structure prediction methods, calculated chemical shifts and solid-state NMR experiments are powerful methods for crystal structure determination of microcrystalline powders. However, currently structural information obtained from solid-state NMR is usually included only after a set of candidate crystal structures has already been independently generated, starting from a set of single-molecule conformations. Here, we show with the case of ampicillin that this can lead to failure of structure determination.

Chemical shifts in molecular solids by machine learning.

Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the ability to calculate them, which  comes at the cost of high accuracy first-principles calculations. Machine learning has recently emerged as a way to overcome the need for quantum chemical calculations, but for chemical shifts in solids it is hindered by the chemical and combinatorial space spanned by molecular solids, the strong dependency of chemical shifts on their environment, and the lack of an experimental database of shifts.

Refocused linewidths less than 10 Hz in H solid-state NMR.

Coherence lifetimes in homonuclear dipolar decoupled H solid-state NMR experiments are usually on the order of a few ms. We discover an oscillation that limits the lifetime of the coherences by recording spin-echo dephasing curves. We find that this oscillation can be removed by the application of a double spin-echo experiment, leading to coherence lifetimes of more than 45 ms in adamantane and more that 22 ms in β-AspAla, corresponding to refocused linewidths of less than 7 and 14 Hz respectively.

Structure elucidation of a complex CO-based organic framework material by NMR crystallography.

A three-dimensional structural model of a complex CO-based organic framework made from high molecular weight, self-assembled, flexible and multi-functional oligomeric constituents has been determined by solid-state NMR including DNP-enhanced experiments. The complete assignment of the N, C and H resonances was obtained from a series of two-dimensional through space and through bond correlation experiments. MM-QM calculations were used to generate different model structures for the material which were then evaluated by comparing multiple experimental and calculated NMR parameters.