Human- and computer-accessible 2D correlation data for a more reliable structure determination of organic compounds. Future roles of researchers, software developers, spectrometer managers, journal editors, reviewers, publisher and database managers toward artificial-intelligence analysis of NMR spectra

The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research

Covering: up to 2018With contributions from the global natural product (NP) research community, and continuing the Raw Data Initiative, this review collects a comprehensive demonstration of the immense scientific value of disseminating raw nuclear magnetic resonance (NMR) data, independently of, and in parallel with, classical publishing outlets. A comprehensive compilation of historic to present-day cases as well as contemporary and future applications show that addressing the urgent need for a repository of publicly accessible raw NMR data has the potential to transform natural products (NPs) and associated fields of chemical and biomedical research. The call for advancing open sharing mechanisms for raw data is intended to enhance the transparency of experimental protocols, augment the reproducibility of reported outcomes, including biological studies, become a regular component of responsible research, and thereby enrich the integrity of NP research and related fields.

Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals

Various chemical shift predictive methodologies have been studied and developed, but there remains the problem of prediction accuracy. Assigning the NMR signals of metabolic mixtures requires high predictive performance owing to the complexity of the signals. Here we propose a new predictive tool that combines quantum chemistry and machine learning. A scaling factor as the objective variable to correct the errors of 2355 theoretical chemical shifts was optimized by exploring 91 machine learning algorithms and using the partial structure of 150 compounds as explanatory variables. The optimal predictive model gave RMSDs between experimental and predicted chemical shifts of 0.2177 ppm for δ 1H and 3.3261 ppm for δ 13C in the test data; thus, better accuracy was achieved compared with existing empirical and quantum chemical methods. The utility of the predictive model was demonstrated by applying it to assignments of experimental NMR signals of a complex metabolic mixture.

NMReDATA, a standard to report the NMR assignment and parameters of organic compounds

Even though NMR has found countless applications in the field of small molecule characterization, there is no standard file format available for the NMR data relevant to structure characterization of small molecules. A new format is therefore introduced to associate the NMR parameters extracted from 1D and 2D spectra of organic compounds to the proposed chemical structure. These NMR parameters, which we shall call NMReDATA (for nuclear magnetic resonance extracted data), include chemical shift values, signal integrals, intensities, multiplicities, scalar coupling constants, lists of 2D correlations, relaxation times, and diffusion rates. The file format is an extension of the existing Structure Data Format, which is compatible with the commonly used MOL format. The association of an NMReDATA file with the raw and spectral data from which it originates constitutes an NMR record. This format is easily readable by humans and computers and provides a simple and efficient way for disseminating results of structural chemistry investigations, allowing automatic verification of published results, and for assisting the constitution of highly needed open-source structural databases.

The C6H6 NMR repository: An integral solution to control the flow of your data from the magnet to the public

NMR is a mature technique that is well established and adopted in a wide range of research facilities from laboratories to hospitals. This accounts for large amounts of valuable experimental data that may be readily exported into a standard and open format. Yet the publication of these data faces an important issue: Raw data are not made available; instead, the information is slimed down into a string of characters (the list of peaks). Although historical limitations of technology explain this practice, it is not acceptable in the era of Internet. The idea of modernizing the strategy for sharing NMR data is not new, and some repositories exist, but sharing raw data is still not an established practice. Here, we present a powerful toolbox built on recent technologies that runs inside the browser and provides a means to store, share, analyse, and interact with original NMR data. Stored spectra can be streamlined into the publication pipeline, to improve the revision process for instance. The set of tools is still basic but is intended to be extended. The project is open source under the Massachusetts Institute of Technology (MIT) licence.

Advanced NMR-Based Structural Investigation of Glucosinolates and Desulfoglucosinolates

Glucosinolates (GLs) constitute a class of plant secondary metabolites that are characteristic of the order Brassicales. They each contain a common hydrophilic moiety connected to a mostly hydrophobic side chain whose constitution is the most frequent structural variant. Their transformations by myrosinases lead to intensively studied and highly reactive compounds of biological relevancy. In other respects, the enzymatic desulfation of GLs produces derivatives (DS-GLs) that are useful for GL analysis. A collection of 31 compounds, GLs and DS-GLs, representing 17 different side chains was established in order to report accurate descriptions of the molecules' ¹H-, ¹³C-, and ¹⁵N-NMR parameters. The descriptions of the ¹H-NMR spectra were achieved using the PERCH software, which accurately analyzed the complex coupling patterns that arose from strongly coupled nuclei. The chemical shift assignments were supported by 2D COSY, HSQC, and HMBC spectra. The impact of desulfation and the influence of the nature of the side chains on the chemical shift values are discussed. The results of the spectroscopic analysis and the 3D chemical-structure models of the studied molecules were grouped in structure-and-data-format (SDF) files. The NMR parameters were also collected in a simple text file, a spreadsheet file, and a relational database.

High Resolution NMR Spectroscopy as a Structural and Analytical Tool for Unsaturated Lipids in Solution

Mono- and polyunsaturated lipids are widely distributed in Nature, and are structurally and functionally a diverse class of molecules with a variety of physicochemical, biological, medicinal and nutritional properties. High resolution NMR spectroscopic techniques including 1H-, 13C- and 31P-NMR have been successfully employed as a structural and analytical tool for unsaturated lipids. The objective of this review article is to provide: (i) an overview of the critical 1H-, 13C- and 31P-NMR parameters for structural and analytical investigations; (ii) an overview of various 1D and 2D NMR techniques that have been used for resonance assignments; (iii) selected analytical and structural studies with emphasis in the identification of major and minor unsaturated fatty acids in complex lipid extracts without the need for the isolation of the individual components; (iv) selected investigations of oxidation products of lipids; (v) applications in the emerging field of lipidomics; (vi) studies of protein-lipid interactions at a molecular level; (vii) practical considerations and (viii) an overview of future developments in the field.

Computer-aided Dereplication and Structure Elucidation of Natural Products at the University of Reims

Natural product chemistry began in Reims, France, in a pharmacognosy research laboratory whose main emphasis was the isolation and identification of bioactive molecules, following the guidelines of chemotaxonomy. The structure elucidation of new compounds of steadily increasing complexity favored the emergence of methodological work in nuclear magnetic resonance. As a result, our group was the first to report the use of proton-detected heteronuclear chemical shift correlation spectra for the computer-assisted structure elucidation of small organic molecules driven by atom proximity relationships and without relying on databases. The early detection of known compounds appeared as a necessity in order to deal more efficiently with complex plant extracts. This goal was reached by an original combination of mixture fractionation by centrifugal partition chromatography, analysis by ¹³ C NMR, digital data reduction and alignment, hierarchical data clustering, and computer database search.