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The revision of the structure of the sesquiterpene aquatolide from a bicyclo[2.2.0]hexane to a bicyclo[2.1.1]hexane structure using compelling NMR data, X-ray crystallography, and the recent confirmation via full synthesis exemplify that the achievement of “structural correctness” depends on the completeness of the experimental evidence. Archived FIDs and newly acquired aquatolide spectra demonstrate that archiving and rigorous interpretation of 1D 1H NMR data may enhance the reproducibility of (bio)chemical research and curb the growing trend of structural misassignments. Despite being the most accessible NMR experiment, 1D 1H spectra encode a wealth of information about bonds and molecular geometry that may be fully mined by 1H iterative full spin analysis (HiFSA). Fully characterized 1D 1H spectra are unideterminant for a given structure. The corresponding FIDs may be readily submitted with publications and collected in databases. Proton NMR spectra are indispensable for structural characterization even in conjunction with 2D data. Quantum interaction and linkage tables (QuILTs) are introduced for a more intuitive visualization of 1D J-coupling relationships, NOESY correlations, and heteronuclear experiments. Overall, this study represents a significant contribution to best practices in NMR-based structural analysis and dereplication.

The acquisition of 1D 1H NMR (HNMR) spectra is one of earliest steps in characterizing natural products and other organic molecules. For publication, HNMR information usually is “converted” into a table format, and sometimes spectral plots are provided. However, this transformation is lossy and frequently insufficient for unambiguous dereplication. This ambiguity can even lead to structural revision, such as in the recent case of aquatolide (1), a sesquiterpene lactone from Asteriscus aquaticus. Our study demonstrates that public dissemination of original (digital) HNMR data (FIDs) can be a powerful means of enhancing the reproducibility of structural assignments and, thus, any downstream biological studies. Using the archived 800 MHz HNMR spectrum, and employing a semi-automated quantum mechanics-driven spectral analysis (HiFSA), we were able to rule out the initial assignment (1a), confirm the revision (1b), and achieve the full interpretation of the HNMR fingerprints. Using additional examples of constitutional and diastereomeric isomers which exhibit complex and near-identical HNMR spectra, we show that the public sharing of original HNMR data (FIDs) is not only essential for robust structural assignments, but can enhance the reproducibility of research with bioactive natural products and other organic molecules simply and productively.

The present study demonstrates the importance of adequate precision when reporting the δ and J parameters of frequency domain 1H NMR (HNMR) data. Using a variety of structural classes (terpenoids, phenolics, alkaloids) from different taxa (plants, cyanobacteria), this study develops rationales that explain the importance of enhanced precision in NMR spectroscopic analysis and rationalizes the need for reporting Δδ and ΔJ values at the 0.1–1 ppb and 10 mHz level, respectively. Spectral simulations paired with iteration are shown to be essential tools for complete spectral interpretation, adequate precision, and unambiguous HNMR-driven dereplication and metabolomic analysis. The broader applicability of the recommendation relates to the physicochemical properties of hydrogen (1H) and its ubiquity in organic molecules, making HNMR spectra an integral component of structure elucidation and verification. Regardless of origin or molecular weight, the HNMR spectrum of a compound can be very complex and encode a wealth of structural information that is often obscured by limited spectral dispersion and the occurrence of higher order effects. This altogether limits spectral interpretation, confines decoding of the underlying spin parameters, and explains the major challenge associated with the translation of HNMR spectra into tabulated information. On the other hand, the reproducibility of the spectral data set of any (new) chemical entity is essential for its structure elucidation and subsequent dereplication. Handling and documenting HNMR data with adequate precision is critical for establishing unequivocal links between chemical structure, analytical data, metabolomes, and biological activity. Using the full potential of HNMR spectra will facilitate the general reproducibility for future studies of bioactive chemicals, especially of compounds obtained from the diversity of terrestrial and marine organisms.