Review on NMR as a tool to analyse natural products extract directly: Molecular structure elucidation and biological activity analysis

Chemical structure characterization of polysaccharides using diffusion ordered NMR spectroscopy (DOSY)

The potential of DOSY NMR spectroscopy to distinguish the linkage pattern of chemically related polysaccharides was evaluated using β-glucans isolated from yeast (Saccharomyces cerevisiae) and mushroom (Pleurotus eryngii). Laminarin from Laminaria digitata was included for chemical shift comparison. Characterization through methylation and 1D/2D NMR analysis showed that all the samples were constituted by →3)-Glcp-(1→; →3,6)-Glcp-(1→; Glcp-(1→ and →6)-Glcp-(1→ linkages. The results obtained allowed the identification of the well-known chemical structure of laminarin. Moreover, DOSY demonstrated that the units →3)-Glcp-(1→ and →6)-Glcp-(1→ from yeast β-glucans, presented the same diffusion time. For the mushrooms β-glucans, the diffusion time of these units were different, confirming that they belong to distinct polysaccharides. The yeast and the mushroom polysaccharide samples presented the same NMR correlations, but after DOSY analysis, different structures could be proposed. Therefore, DOSY NMR spectroscopy could be a tool for the identification of different linkage patterns of polysaccharides belonging to the same group and may be a useful contribution to the chemical structure and biological activity correlation studies of such structures.

Target Discovery Driven by Chemical Biology and Computational Biology

Target identification is crucial for drug screening and development because it can reveal the mechanism of drug action and ensure the reliability and accuracy of the results. Chemical biology, an interdisciplinary field combining chemistry and biology, can assist in this process by studying the interactions between active molecular compounds and proteins and their physiological effects. It can also help predict potential drug targets or candidates, develop new biomarker assays and diagnostic reagents, and evaluate the selectivity and range of active compounds to reduce the risk of off-target effects. Chemical biology can achieve these goals using techniques such as changing protein thermal stability, enzyme sensitivity, and molecular structure and applying probes, isotope labeling and mass spectrometry. Concurrently, computational biology employs a diverse array of computational models to predict drug targets. This approach also offers innovative avenues for repurposing existing drugs. In this paper, we review the reported chemical biology and computational biology techniques for identifying different types of targets that can provide valuable insights for drug target discovery.

Quelling the Geometry Factor Effect in Quantum Chemical Calculations of 13C NMR Chemical Shifts with the Aid of the pecG-n (n = 1, 2) Basis Sets

A root factor for the accuracy of all quantum chemical calculations of nuclear magnetic resonance (NMR) chemical shifts is the quality of the molecular equilibrium geometry used. In turn, this quality depends largely on the basis set employed at the geometry optimization stage. This parameter represents the main subject of the present study, which is a continuation of our recent work, where new pecG-n (n = 1, 2) basis sets for the geometry optimization were introduced. A goal of this study was to compare the performance of our geometry-oriented pecG-n (n = 1, 2) basis sets against the other basis sets in massive calculations of 13C NMR shielding constants/chemical shifts in terms of their efficacy in reducing geometry factor errors. The testing was carried out with both large-sized biologically active natural products and medium-sized compounds with complicated electronic structures. The former were treated using the computation protocol based on the density functional theory (DFT) and considered in the theoretical benchmarking, while the latter were treated using the computational scheme based on the upper-hierarchy coupled cluster (CC) methods and were used in the practical benchmarking involving the comparison with experimental NMR data. Both the theoretical and practical analyses showed that the pecG-1 and pecG-2 basis sets resulted in substantially reduced geometry factor errors in the calculated 13C NMR chemical shifts/shielding constants compared to their commensurate analogs, with the pecG-2 basis set being the best of all the considered basis sets.

Exploring the Significance, Extraction, and Characterization of Plant-Derived Secondary Metabolites in Complex Mixtures

Secondary metabolites are essential components for the survival of plants. Secondary metabolites in complex mixtures from plants have been adopted and documented by different traditional medicinal systems worldwide for the treatment of various human diseases. The extraction strategies are the key components for therapeutic development from natural sources. Polarity-dependent solvent-selective extraction, acidic and basic solution-based extraction, and microwave- and ultrasound-assisted extraction are some of the most important strategies for the extraction of natural products from plants. The method needs to be optimized to isolate a specific class of compounds. Therefore, to establish the mechanism of action, the characterization of the secondary metabolites, in a mixture or in their pure forms, is equally important. LC-MS, GC-MS, and extensive NMR spectroscopic strategies are established techniques for the profiling of metabolites in crude extracts. Various protocols for the extraction and characterization of a wide range of classes of compounds have been developed by various research groups and are described in this review. Additionally, the possible means of characterizing the compounds in the mixture and their uniqueness are also discussed. Hyphenated techniques are crucial for profiling because of their ability to analyze a vast range of compounds. In contrast, inherent chemical shifts make NMR an indispensable tool for structure elucidation in complex mixtures.