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

NMR Calculations with Quantum Methods: Development of New Tools for Structural Elucidation and Beyond

Structural elucidation is an important and challenging stage in the discovery of new organic molecules. Single-crystal X-ray analysis provides the most unquestionable results, though in practice the availability of suitable crystals limits its broad use. On the other hand, NMR spectroscopy has become the leading and universal technique to accomplish the task. Despite continuous advances in the field, the misinterpretation of NMR data is commonplace, evidenced by the large number of erroneous structures being published in top journals. Quantum calculations of NMR chemical shifts and scalar coupling constants emerged as ideal complements to facilitate the elucidation process when experimental NMR data is inconclusive. Since seminal reports demonstrated that affordable DFT methods provide NMR predictions accurate enough to differentiate among closely related isomers, the discipline has experienced substantial growth. The impact has been felt in different areas, and nowadays the results of such calculations are routinely seen in high impact literature.This Account describes our investigations in the field of quantum NMR calculations, focusing on the development of tools for structural elucidation and practical applications. We pioneered the use of artificial intelligence methods in the development of novel strategies of structural validation. Our first generation of trained artificial neural networks (ANNs) showed excellent ability to identify mistakes at the atom connectivity level, whereas the use of multidimensional pattern recognition pushed the performance to the stereochemical limit. In a conceptually different approach, we developed DP4+, an updated version of the DP4 probability used to determine the most likely structure among two or more candidates when one set of experimental data is available. Increasing the level of theory in NMR calculations and including unscaled data in the formalism improved the performance of the method, further validated to settle the configuration of challenging motifs such as spiroepoxides or Mosher's derivatives. One of the limitations of DP4+ is related to the relatively large computational cost involved in obtaining DFT-optimized geometries, which led to the development of a fast variant including the valuable information provided by coupling constants (J-DP4 method).These tools were explored to suggest the most probable structure of controversial natural or unnatural products originally misassigned, with some predictions further validated by synthesis (as in the case of pseudorubriflordilactone B). The possibility of predicting the structure of a natural product without requiring authentic sample was investigated in collaboration with Prof. Pilli (UNICAMP, Brazil) in the computer-guided total synthesis and stereochemical revisions of several natural products. Despite these advances, there remain considerable challenges, such as the case of configurational assessment of polar systems featuring multiple intramolecular hydrogen bonding interactions because of the poor energy predictions provided by most DFT methods. In our latest work, we tackle this problem by averaging the results provided by randomly generated ensembles, paving the way for a new paradigm in quantum NMR-assisted structural elucidation.

Quantum mechanical NMR full spin analysis in pharmaceutical identity testing and quality control

Issues related to pharmaceutical quality are arising at an alarming rate. Pharmaceutical quality concerns both the Active Pharmaceutical Ingredients (APIs) and the Finished Drug Product/ Formulation. Recently, there has been a significant increase in the number of reports of harmful impurities in marketed drug formulations. Impurities range from solvents, reactants, adulterants, and catalysts to synthetic byproducts. Quality concerns in commercial preparations may also arise due to shelf life stability. Furthermore, a number of falsified and substandard drug cases have been reported. Most of the techniques which are currently in place can, at best, detect the impurities, but cannot identify them unless they are already known and can be compared to a standard. On the other hand, ¹H NMR spectroscopy detects all the hydrogen containing species, typically provides information to elucidate structures partially or even completely, and through its absolute quantitative capabilities even can detect the presence hydrogen-free species indirectly. The structural properties that produce ¹H NMR signals as characteristic representations of a given molecule are the chemical shifts (δ in ppm) and coupling constants (J in Hz). Along with the line widths (ω_1/2 in Hz), these parameters are bound to both the molecule and the NMR experimental conditions by quantum mechanical (QM) principles. This means that the ¹H NMR spectra of APIs can be precisely calculated and compared to the experimental data. This review explains how ¹H NMR spectroscopy coupled with Full Spin Analysis can contribute towards the quality control of pharmaceuticals by improving structural dereplication and achieving simultaneous quantification of both APIs and their contaminants.

EBC-232 and 323: A Structural Conundrum Necessitating Unification of Five In Silico Prediction and Elucidation Methods

Structurally unique halimanes EBC-232 and EBC-323, isolated from the Australian rainforest plant Croton insularis, proved considerably difficult to elucidate. The two diastereomers, which consist an unusual oxo-6,7-spiro ring system fused to a dihydrofuran, were solved by unification and consultation of five in silico NMR elucidation and prediction methods [i.e., ACDLabs, olefin strain energy (OSE), DP4, DU8+ and TD DFT CD]. Structure elucidation challenges of this nature are prime test case examples for empowering future AI learning in structure elucidation.

Tri- and Tetrameric Proanthocyanidins with Dentin Bioactivities from Pinus massoniana

Guided by dentin biomechanical bioactivity, this phytochemical study led to the elucidation of an extended set of structurally demanding proanthocyanidins (PACs). Unambiguous structure determination involved detailed spectroscopic and chemical characterization of four A-type dimers (2 and 4-6), seven trimers (10-16), and six tetramers (17-22). New outcomes confirm the feasibility of determining the absolute configuration of the catechol monomers in oligomeric PACs by one-dimensional (1D) and two-dimensional (2D) NMR. Electronic circular dichroism as well as phloroglucinolysis followed by mass spectrometry and chiral phase high-performance liquid chromatography (HPLC) analysis generated the necessary chiral reference data. In the context of previously reported dentin-bioactive PACs, accurately and precisely assigned ¹³C NMR resonances enabled absolute stereochemical assignments of PAC monomers via (i) inclusion of the ¹³C NMR γ-gauche effect and (ii) determination of differential ¹³C chemical shift values (Δδ_C) in comparison with those of the terminal monomer (unit II) in the dimers 2 and 4-6. Among the 13 fully elucidated PACs, eight were identified as new, and one structure (11) was revised based on new knowledge gained regarding the subtle, stereospecific spectroscopic properties of PACs.

The Zebra Mussel (Dreissena polymorpha) as a Model Organism for Ecotoxicological Studies: A Prior 1H NMR Spectrum Interpretation of a Whole Body Extract for Metabolism Monitoring

The zebra mussel (Dreissena polymorpha) represents a useful reference organism for the ecotoxicological study of inland waters, especially for the characterization of the disturbances induced by human activities. A nuclear magnetic resonance (NMR)-based metabolomic approach was developed on this species. The investigation of its informative potential required the prior interpretation of a reference ¹H NMR spectrum of a lipid-free zebra mussel extract. After the extraction of polar metabolites from a pool of whole-body D. polymorpha powder, the resulting highly complex 1D ¹H NMR spectrum was interpreted and annotated through the analysis of the corresponding 2D homonuclear and heteronuclear NMR spectra. The spectrum interpretation was completed and validated by means of sample spiking with 24 commercial compounds. Among the 238 detected ¹H signals, 53% were assigned, resulting in the identification of 37 metabolites with certainty or high confidence, while 5 metabolites were only putatively identified. The description of such a reference spectrum and its annotation are expected to speed up future analyses and interpretations of NMR-based metabolomic studies on D. polymorpha and to facilitate further explorations of the impact of environmental changes on its physiological state, more particularly in the context of large-scale ecological and ecotoxicological studies.

Evaluation of Non-Uniform Sampling 2D 1H-13C HSQC Spectra for Semi-Quantitative Metabolomics

Metabolomics is the comprehensive study of metabolism, the biochemical processes that sustain life. By comparing metabolites between healthy and disease states, new insights into disease mechanisms can be uncovered. NMR is a powerful analytical method to detect and quantify metabolites. Standard one-dimensional (1D) ¹H-NMR metabolite profiling is informative but challenged by significant chemical shift overlap. Multi-dimensional NMR can increase resolution, but the required long acquisition times lead to limited throughput. Non-uniform sampling (NUS) is a well-accepted mode of acquiring multi-dimensional NMR data, enabling either reduced acquisition times or increased sensitivity in equivalent time. Despite these advantages, the technique is not widely applied to metabolomics. In this study, we evaluated the utility of NUS ¹H–¹³C heteronuclear single quantum coherence (HSQC) for semi-quantitative metabolomics. We demonstrated that NUS improved sensitivity compared to uniform sampling (US). We verified that the NUS measurement maintains linearity, making it possible to detect metabolite changes across samples and studies. Furthermore, we calculated the lower limit of detection and quantification (LOD/LOQ) of common metabolites. Finally, we demonstrate that the measurements are repeatable on the same system and across different systems. In conclusion, our results detail the analytical capability of NUS and, in doing so, empower the future use of NUS ¹H–¹³C HSQC in metabolomic studies.

The Discreet Structural Diversity of Briarellins: DU8+ Guided Multiple Structure Revisions Yielded Two Unknown Structural Types

Briarellins, a subset of C2-C11 cyclized cembranoids, were proposed to contain a C3-C14 ether or lactone bridge, similar to asbestinins. However, the total synthesis of the proposed structure of briarellin J revealed a misassignment. We revisited briarellins, computationally, with the help of a recently developed hybrid DFT/parametric method, DU8+, and revised the structures of briarellin C14-C3 ε-lactones to new structural types containing either a C14-C11 or C14-C12 lactone bridge. The original structures of briarellin and asbestinin ethers were confirmed.

Classification of Flavonoid Metabolomes via Data Mining and Quantification of Hydroxyl NMR Signals

Utilizing the distinct HMBC cross-peak patterns of lower-field range (LFR; 11.80-14.20 ppm) hydroxyl singlets, presented NMR methodology characterizes flavonoid metabolomes both qualitatively and quantitatively. It enables simultaneous classification of the structural types of 5-OH flavonoids and biogenetically related 2'-OH chalcones, as well as quantification of individual metabolites from 1H NMR spectra, even in complex mixtures. Initially, metabolite-specific LFR 1D 1H and 2D HMBC patterns were established via literature mining and experimental data interpretation, demonstrating that LFR HMBC patterns encode the different structural types of 5-OH flavonoids/2'-OH chalcones. Taking advantage of the simplistic multiplicity of the H,H-uncoupled LFR 5-/2'-OH singlets, individual metabolites could subsequently be quantified by peak fitting quantitative 1H NMR (PF-qHNMR). Metabolomic analysis of enriched fractions from three medicinal licorice (Glycyrrhiza) species established proof-of-concept for distinguishing three major structural types and eight subtypes in biomedical applications. The method identified 15 G. uralensis (GU) phenols from the six possible subtypes of 5,7-diOH (iso)flav(an)ones with 6-, 8-, and nonprenyl substitution, including the new 6-prenyl-licoisoflavanone (1) and two previously unknown compounds (4 and 7). Relative (100%) qNMR established quantitative metabolome patterns suitable for species discrimination and plant metabolite studies. Absolute qNMR with combined external and internal (solvent) calibration (ECIC) identified and quantified 158 GU metabolites. HMBC-supported qHNMR analysis of flavonoid metabolomes ("flavonomics") empowers the exploration of structure-abundance-activity relationships of designated bioactivity. Its ability to identify and quantify numerous metabolites simultaneously and without identical reference materials opens new avenues for natural product discovery and botanical quality control and can be adopted to other flavonoid- and chalcone-containing taxa.

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

Structure elucidation of bacterial nonribosomal lipopeptides

We provide a summary of the tools, which allow elucidate the structures of nonribosomal lipopetides.

Making natural products from renewable feedstocks: back to the roots?

This review highlights the utilization of biomass-derived building blocks in the total synthesis of natural products.

Orthogonal Method for Double-Bond Placement via Ozone-Induced Dissociation Mass Spectrometry (OzID-MS)

Most often, the structures of secondary metabolites are solved using a suite of NMR techniques. However, there are times when it can be challenging to position double bonds, particularly those that are fully substituted or when there are multiple double bonds in similar chemical environments. Ozone-induced dissociation mass spectrometry (OzID-MS) serves as an orthogonal structure elucidation tool, using predictable fragmentation patterns that are generated after ozonolysis across a carbon-carbon double bond. This technique is finding growing use in the lipidomics community, suggestive of its potential value for secondary metabolites. This methodology was evaluated by confirming the double-bond positions in five fungal secondary metabolites, specifically, ent-sartorypyrone E (1), sartorypyrone A (2), sorbicillin (3), trichodermic acid A (4), and AA03390 (5). This demonstrated its potential with a variety of chemotypes, ranging from polyketides to terpenoids and including those in both conjugated and nonconjugated polyenes. In addition, the potential of using this methodology in the context of a mixture was piloted by studying Aspergillus fischeri, first examining a traditional extract and then sampling a live fungal culture in situ. While the intensity of signals varied from pure compound to extract to in situ, the utility of the technique was preserved.

Innovative omics-based approaches for prioritisation and targeted isolation of natural products - new strategies for drug discovery

Covering: 2013 to 2019 The exploration of the chemical diversity of extracts from various biological sources has led to major drug discoveries. Over the past two decades, despite the introduction of advanced methodologies for natural product (NP) research (e.g., dereplication and high content screening), successful accounts of the validation of NPs as lead therapeutic candidates have been limited. In this context, one of the main challenges faced is related to working with crude natural extracts because of their complex composition and the inadequacies of classical bioguided isolation studies given the pace of high-throughput screening campaigns. In line with the development of metabolomics, genomics and chemometrics, significant advances in metabolite profiling have been achieved and have generated high-quality massive genome and metabolome data on natural extracts. The unambiguous identification of each individual NP in an extract using generic methods remains challenging. However, the establishment of structural links among NPs via molecular network analysis and the determination of common features of extract composition have provided invaluable information to the scientific community. In this context, new multi-informational-based profiling approaches integrating taxonomic and/or bioactivity data can hold promise for the discovery and development of new bioactive compounds and return NPs back to an exciting era of development. In this article, we examine recent studies that have the potential to improve the efficiency of NP prioritisation and to accelerate the targeted isolation of key NPs. Perspectives on the field's evolution are discussed.

Natural Products as a Foundation for Drug Discovery

Many natural products have been used as drugs for the treatment of diverse indications. Although most U.S. pharmaceutical companies have reduced or eliminated their in-house natural-product research over the years, new approaches for compound screening and chemical synthesis are resurrecting interest in exploring the therapeutic value of natural products. The aim of this commentary is to review emerging strategies and techniques that have made natural products a viable strategic choice for inclusion in drug discovery programs. Published 2019. U.S. Government.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

Cytotoxic Sesquiterpenoid Quinones and Quinols, and an 11-Membered Heterocycle, Kauamide, from the Hawaiian Marine Sponge Dactylospongia elegans

Several known sesquiterpenoid quinones and quinols (1-9), and kauamide (10), a new polyketide-peptide containing an 11-membered heterocycle, were isolated from the extracts of the Hawaiian marine sponge Dactylospongia elegans. The planar structure of 10 was determined from spectroscopic analyses, and its relative and absolute configurations were established from density functional theory (DFT) calculations of the GIAO NMR shielding tensors, and advanced Marfey's analysis of the N-MeLeu residue, respectively. Compounds 1 and 3 showed moderate inhibition of β-secretase 1 (BACE1), whereas 1-9 exhibited moderate to potent inhibition of growth of human glioma (U251) cells. Compounds 1-2 and 4-7 were also active against human pancreatic carcinoma (Panc-1) cells.

Challenges and Opportunities in Cancer Metabolomics

Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture-based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.

Natural products targeting strategies involving molecular networking: different manners, one goal

Covering: up to 2019Landmark advances in bioinformatics tools have recently enhanced the field of natural products research, putting today's natural product chemists in the enviable position of being able to perform the efficient targeting/discovery of previously undescribed molecules by expediting the prioritization of the isolation workflow. Among these advances, MS/MS molecular networking has appeared as a promising approach to dereplicate complex natural product mixtures, leading to a real revolution in the "art of natural product isolation" by accelerating the pace of research of this field. This review illustrates through selected cornerstone studies the new thinking in natural product isolation by drawing a parallel between the different underlying philosophies behind the use of molecular networking in targeting natural products.

Computationally-assisted discovery and structure elucidation of natural products

Computer hardware development coupled with the development of quantum chemistry, new computational models and algorithms, and user-friendly interfaces have lowered the barriers to the use of computation in the discovery and structure elucidation of natural products. Consequently, the use of computational chemistry software as a tool to discover and determine the structure of natural products has become more common in recent years. In this review, we provide several examples of recent studies that used computer technology to facilitate the discovery and structure determination of various natural products.

Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data

There is an increasing focus on the part of academic institutions, funding agencies, and publishers, if not researchers themselves, on preservation and sharing of research data. Motivations for sharing include research integrity, replicability, and reuse. One of the barriers to publishing data is the extra work involved in preparing data for publication once a journal article and its supporting information have been completed. In this work, a method is described to generate both human and machine-readable supporting information directly from the primary instrumental data files and to generate the metadata to ensure it is published in accordance with findable, accessible, interoperable, and reusable (FAIR) guidelines. Using this approach, both the human readable supporting information and the primary (raw) data can be submitted simultaneously with little extra effort. Although traditionally the data package would be sent to a journal publisher for publication alongside the article, the data package could also be published independently in an institutional FAIR data repository. Workflows are described that store the data packages and generate metadata appropriate for such a repository. The methods both to generate and to publish the data packages have been implemented for NMR data, but the concept is extensible to other types of spectroscopic data as well.

The role of computer-assisted structure elucidation (CASE) programs in the structure elucidation of complex natural products

Computer-assisted structure elucidation can help to determine the structures of complex natural products while minimizing the risk of structure errors.