Tailoring the nuclear Overhauser effect for the study of small and medium-sized molecules by solvent viscosity manipulation

Conformations of Macrocyclic Peptides Sampled by Nuclear Magnetic Resonance: Models for Cell-Permeability

The biological activities and pharmacological properties of peptides and peptide mimetics are determined by their conformational states. Therefore, a detailed understanding of the conformational landscape is crucial for rational drug design. Nuclear magnetic resonance (NMR) is the only method for structure determination in solution. However, it remains challenging to determine the structures of peptides using NMR because of very weak nuclear Overhauser effects (NOEs), the semiquantitative nature of the rotating frame Overhauser effect (ROE), and the low number of NOEs/ROEs in N-methylated peptides. In this study, we introduce a new approach to investigating the structures of modified macrocyclic peptides. We utilize exact NOEs (eNOEs) in viscous solvent mixtures to replicate various cellular environments. eNOEs provide detailed structural information for highly dynamic modified peptides. Structures of high precision were obtained for cyclosporin A, with a backbone atom rmsd of 0.10 Å. Distinct conformational states in different environments were identified for omphalotin A (OmphA), a fungal nematotoxic and multiple backbone N-methylated macrocyclic peptides. A model for cell-permeation is presented for OmphA, based on its structures in polar, apolar, and mixed polarity solvents. During the transition from a polar to an apolar environment, OmphA undergoes a rearrangement of its H-bonding network, accompanied by a cis to trans isomerization of the ω torsion angle within a type VIa β-turn. We hypothesize that the kinetics of these conformational transitions play a crucial role in determining the membrane-permeation capabilities of OmphA.

NMR methods for the analysis of mixtures

NMR spectroscopy is a powerful approach for the analysis of mixtures. Its usefulness arises in large part from the vast landscape of methods, and corresponding pulse sequences, that have been and are being designed to tackle the specific properties of mixtures of small molecules. This feature article describes a selection of methods that aim to address the complexity, the low concentrations, and the changing nature that mixtures can display. These notably include pure-shift and diffusion NMR methods, hyperpolarisation methods, and fast 2D NMR methods such as ultrafast 2D NMR and non-uniform sampling. Examples or applications are also described, in fields such as reaction monitoring and metabolomics, to illustrate the relevance and limitations of different methods.

Use of Diethanolamine as a Viscous Solvent for Mixture Analysis by Multidimensional Heteronuclear ViscY NMR Experiments

Diethanolamine/DMSO-d₆ as a viscous binary solvent is first reported for the individualization of low-polarity mixture components by multidimensional heteronuclear ViscY NMR experiments under spin diffusion conditions. Solvent viscosity induces the slowing down of molecular tumbling, hence promoting magnetization transfer by dipolar longitudinal cross-relaxation. As a result, all ¹H nuclei resonances within the same molecule may correlate in a 2D nuclear Overhauser effect spectroscopy (NOESY) spectrum, giving access to mixture analysis. We offer a new way to analyze mixtures by considering 3D heteronuclear heteronuclear single-quantum coherence-NOESY (HSQC-NOESY) experiments under viscous conditions. We state the individualization of four low-polarity chemical compounds dissolved in the diethanolamine/DMSO-d₆ solvent blend using homonuclear selective 1D, 2D ¹H-¹H NOESY experiments and heteronuclear 1D, 2D ¹H-¹⁹F heteronuclear Overhauser effect spectroscopy, 2D ¹H-¹⁹F, ¹H-³¹P HSQC-NOESY, and 3D ¹H-¹⁹F-¹H, ¹H-³¹P-¹H HSQC-NOESY experiments by taking profit from spin diffusion.

Taxonomy-Focused Natural Product Databases for Carbon-13 NMR-Based Dereplication

The recent revival of the study of organic natural products as renewable sources of medicinal drugs, cosmetics, dyes, and materials motivated the creation of general purpose structural databases. Dereplication, the efficient identification of already reported compounds, relies on the grouping of structural, taxonomic and spectroscopic databases that focus on a particular taxon (species, genus, family, order, etc.). A set of freely available python scripts, CNMR_Predict, is proposed for the quick supplementation of taxon oriented search results from the naturaL prOducTs occUrrences database (LOTUS, lotus.naturalproducts.net) with predicted carbon-13 nuclear magnetic resonance data from the ACD/Labs CNMR predictor and DB software (acdlabs.com) to provide easily searchable databases. The database construction process is illustrated using Brassica rapa as a taxon example.