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

The Natural Products Magnetic Resonance Database (NP-MRD) for 2025

The Natural Products Magnetic Resonance Database (NP-MRD; https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction, and retrieval of nuclear magnetic resonance (NMR) data on natural products (NPs). The NP-MRD was initially established to support compound de-replication and data dissemination for the NP community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics, and nutrition science fields. Indeed, since its launch in 2022, the NP-MRD has expanded enormously in size, scope, and popularity. The current version of NP-MRD now contains nearly 7× more compounds (281 859 versus 40 908) and 7× more NMR spectra (5.5 million versus 817 278) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11 000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from 165 spectra per year to >10 000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20 000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, notable updates to the database's spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD's database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

Current and emerging tools and strategies for the identification of bioactive natural products in complex mixtures

Covering: up to 2024The prompt identification of (bio)active natural products (NPs) from complex mixtures poses a significant challenge due to the presence of numerous compounds with diverse structures and (bio)activities. Thus, this review provides an overview of current and emerging tools and strategies for the identification of (bio)active NPs in complex mixtures. Traditional approaches of bioassay-guided fractionation (BGF), followed by nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis for compound structure elucidation, continue to play an important role in the identification of active NPs. However, recent advances (2018-2024) have led to the development of novel techniques such as (bio)chemometric analysis, dereplication and combined approaches, which allow efficient prioritization for the elucidation of (bio)active compounds. For researchers involved in the search for bioactive NPs and who want to speed up their discoveries while maintaining accurate identifications, this review highlights the strengths and limitations of each technique and provides up-to-date insights into their combined use to achieve the highest level of confidence in the identification of (bio)active natural products from complex matrices.

Antimicrobial Dihydroflavonols and Isoflavans Isolated from the Root Bark of Dalbergia gloveri

Three new dihydroflavonols, gloverinols A-C (1-3), a new flavon-3-ol, gloverinol D (4), two new isoflavans, gloveriflavan A (5) and B (6), and seven known compounds were isolated from the root bark of Dalbergia gloveri. The structures of the isolates were elucidated by using NMR, ECD, and HRESIMS data analyses. Among the isolated compounds, gloverinol B (2), gloveriflavan B (6), and 1-(2,4-dihydroxyphenyl)-3-hydroxy-3-(4-hydroxyphenyl)-1-propanone (10) were the most active against Staphylococcus aureus, with MIC values of 9.2, 18.4, and 14.2 μM, respectively.

The Influence of Disulfide, Thioacetal and Lanthionine-Bridges on the Conformation of a Macrocyclic Peptide

Cyclisation of peptides by forming thioether (lanthionine), disulfide (cystine) or methylene thioacetal bridges between side chains is established as an important tool to stabilise a given structure, enhance metabolic stability and optimise both potency and selectivity. However, a systematic comparative study of the effects of differing bridging modalities on peptide conformation has not previously been carried out. In this paper, we have used the NMR deconvolution algorithm, NAMFIS, to determine the conformational ensembles, in aqueous solution, of three cyclic analogues of angiotensin(1-7), incorporating either disulfide, or non-reducible thioether or methylene thioacetal bridges. We demonstrate that the major solution conformations are conserved between the different bridged peptides, but the distribution of conformations differs appreciably. This suggests that subtle differences in ring size and bridging structure can be exploited to fine-tune the conformational properties of cyclic peptides, which may modulate their bioactivities.

Antiviral Rotenoids and Isoflavones Isolated from Millettia oblata ssp. teitensis

Three new (1-3) and six known rotenoids (5-10), along with three known isoflavones (11-13), were isolated from the leaves of Millettia oblata ssp. teitensis. A new glycosylated isoflavone (4), four known isoflavones (14-18), and one known chalcone (19) were isolated from the root wood extract of the same plant. The structures were elucidated by NMR and mass spectrometric analyses. The absolute configuration of the chiral compounds was established by a comparison of experimental ECD and VCD data with those calculated for the possible stereoisomers. This is the first report on the use of VCD to assign the absolute configuration of rotenoids. The crude leaves and root wood extracts displayed anti-RSV (human respiratory syncytial virus) activity with IC50 values of 0.7 and 3.4 μg/mL, respectively. Compounds 6, 8, 10, 11, and 14 showed anti-RSV activity with IC50 values of 0.4-10 μM, while compound 3 exhibited anti-HRV-2 (human rhinovirus 2) activity with an IC50 of 4.2 μM. Most of the compounds showed low cytotoxicity for laryngeal carcinoma (HEp-2) cells; however compounds 3, 11, and 14 exhibited low cytotoxicity also in primary lung fibroblasts. This is the first report on rotenoids showing antiviral activity against RSV and HRV viruses.

Twenty years of nmrshiftdb2: A case study of an open database for analytical chemistry

In October 2003, 20 years ago, the open-source and open-content database NMRshiftDB was announced. Since then, the database, renamed as nmrshiftdb2 later, has been continuously available and is one of the longer-running projects in the field of open data in chemistry. After 20 years, we evaluate the success of the project and present lessons learnt for similar projects.

NMRium: Teaching nuclear magnetic resonance spectra interpretation in an online platform

NMRium is the first web-based software that allows displaying, processing, interpretation, and teaching of 1D and 2D NMR data in a user-friendly interface. It can import the most common data formats (e.g., JCAMP-DX, Bruker, Varian, and Jeol). While the scope for the use of NMRium encompasses a variety of applications such as being a component in data repositories or electronic lab notebooks (ELN), performing structure elucidation or preparing raw spectral data for publication, it also excels in enhancing teaching of NMR interpretation. In this paper, we present some current possibilities of this new tool. Several series of exercises are already provided on https://www.nmrium.org/teaching.

Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts

This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database's potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.

On the Use of Deuterated Organic Solvents without TMS to Report 1H/13C NMR Spectral Data of Organic Compounds: Current State of the Method, Its Pitfalls and Benefits, and Related Issues

The quite popular, simple but imperfect method of referencing NMR spectra to residual 1H and 13C signals of TMS-free deuterated organic solvents (referred to as Method A) is critically discussed for six commonly used NMR solvents with respect to their δH and δC values that exist in the literature. Taking into account the most reliable data, it was possible to recommend 'best' δX values for such secondary internal standards. The position of these reference points on the δ scale strongly depends on the concentration and type of analyte under study and the solvent medium used. For some solvents, chemically induced shifts (CISs) of residual 1H lines were considered, also taking into account the formation of 1:1 molecular complexes (for CDCl3). Typical potential errors that can occur as a result of improper application of Method A are considered in detail. An overview of all found δX values adopted by users of this method revealed a discrepancy of up to 1.9 ppm in δC reported for CDCl3, most likely caused by the CIS mentioned above. The drawbacks of Method A are discussed in relation to the classical use of an internal standard (Method B), two 'instrumental' schemes in which Method A is often implicitly applied, that is, the default Method C using 2H lock frequencies and Method D based on Ξ values, recommended by the IUPAC but only occasionally used for 1H/13C spectra, and external referencing (Method E). Analysis of current needs and opportunities for NMR spectrometers led to the conclusion that, for the most accurate application of Method A, it is necessary to (a) use dilute solutions in a single NMR solvent and (b) to report δX data applied for the reference 1H/13C signals to the nearest 0.001/0.01 ppm to ensure the precise characterization of new synthesized or isolated organic systems, especially those with complex or unexpected structures. However, the use of TMS in Method B is strongly recommended in all such cases.

Crotofolane Diterpenoids and Other Constituents Isolated from Croton kilwae

Six new crotofolane diterpenoids (1-6) and 13 known compounds (7-19) were isolated from the MeOH-CH2Cl2 (1:1, v/v) extracts of the leaves and stem bark of Croton kilwae. The structures of the new compounds were elucidated by extensive analysis of spectroscopic and mass spectrometric data. The structure of crotokilwaepoxide A (1) was confirmed by single-crystal X-ray diffraction, allowing for the determination of its absolute configuration. The crude extracts and the isolated compounds were investigated for antiviral activity against respiratory syncytial virus (RSV) and human rhinovirus type-2 (HRV-2) in HEp-2 and HeLa cells, respectively, for antibacterial activity against the Gram-positive Bacillus subtilis and the Gram-negative Escherichia coli, and for antimalarial activity against the Plasmodium falciparum Dd2 strain. ent-3β,19-Dihydroxykaur-16-ene (7) and ayanin (16) displayed anti-RSV activities with IC50 values of 10.2 and 6.1 μM, respectively, while exhibiting only modest cytotoxic effects on HEp-2 cells that resulted in selectivity indices of 4.9 and 16.4. Compounds 2 and 5 exhibited modest anti-HRV-2 activity (IC50 of 44.6 μM for both compounds), while compound 16 inhibited HRV-2 with an IC50 value of 1.8 μM. Compounds 1-3 showed promising antiplasmodial activities (80-100% inhibition) at a 50 μM concentration.

Deazaflavin cofactor boosts earthworms Henlea bioluminescence

The bioluminescence of Siberian earthworms Henlea sp. was found to be enhanced by two low molecular weight activators, termed ActH and ActS, found in the hot extracts. The fluorescence emission maximum of the activators matches the bioluminescence spectrum that peaks at 464 nm. We purified 4.3 and 8.8 micrograms of ActH and ActS from 200 worms and explored them using orbitrap HRMS with deep fragmentation and 1D/2D NMR equipped with cryoprobes. Their chemical structures were ascertained using chemical shift prediction services, structure elucidation software and database searches. ActH was identified as the riboflavin analoge archaeal cofactor F0, namely 7,8-didemethyl-8-hydroxy-5-deazariboflavin. ActS is a novel compound, namely ActH sulfated at the 3' ribityl hydroxyl. We designed and implemented a new four step synthesis strategy forActH that outperformed previous synthetic approaches. The synthetic ActH was identical to the natural one and activated Henlea sp. bioluminescence. The bioluminescence enhancement factor X was measured at different ActH concentrations and the Michaelis constant K_m = 0.22 ± 0.01 μM was obtained by nonlinear regression. At an excess of synthetic ActH, the factor X was saturated at X_max = 33.3 ± 0.5, thus opening an avenue to further characterisation of the Henlea sp. bioluminescence system. ActH did not produce bioluminescence without the luciferin with an as yet unknown chemical structure. We propose that ActH and the novel sulfated deazariboflavin ActS either emit the light of the Henlea sp. bioluminescence and/or accept hydride(s) donor upon luciferin oxidation.

NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures

Gaining structural information is a must to allow the unequivocal structural characterization of analytes from natural sources. In liquid state, NMR spectroscopy is almost the only possible alternative to HPLC-MS and hyphenating the effluent of an analyte separation device to the probe head of an NMR spectrometer has therefore been pursued for more than three decades. The purpose of this review article was to demonstrate that, while it is possible to use mass spectrometry and similar methods to differentiate, group, and often assign the differentiating variables to entities that can be recognized as single molecules, the structural characterization of these putative biomarkers usually requires the use of NMR spectroscopy.

Impact of solvent interactions on 1H and 13C chemical shifts investigated using DFT and a reference dataset recorded in CDCl3 and CCl4

1H and 13C chemical shifts of 35 small, rigid molecules were measured under standardized conditions in chloroform-d and in tetrachloromethane. The solvent change mainly affects carbon shifts of polar functional groups. This difference due to specific interactions with CDCl3 cannot be adequately reproduced by DFT calculations in implicit solvent. The new dataset provides an accurate basis for the validation and calibration of shift calculations, especially with respect to improved solvent models.

Modified ent-Abietane Diterpenoids from the Leaves of Suregada zanzibariensis

The leaf extract of Suregada zanzibariensis gave two new modified ent-abietane diterpenoids, zanzibariolides A (1) and B (2), and two known triterpenoids, simiarenol (3) and β-amyrin (4). The structures of the isolated compounds were elucidated based on NMR and MS data analysis. Single-crystal X-ray diffraction was used to establish the absolute configurations of compounds 1 and 2. The crude leaf extract inhibited the infectivity of herpes simplex virus 2 (HSV-2, IC50 11.5 μg/mL) and showed toxicity on African green monkey kidney (GMK AH1) cells at CC50 52 μg/mL. The isolated compounds 1-3 showed no anti-HSV-2 activity and exhibited insignificant toxicity against GMK AH1 cells at ≥100 μM.

Data format standards in analytical chemistry

Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.

Metabolovigilance: Associating Drug Metabolites with Adverse Drug Reactions

The Metabolovigilance database (https://pharmacogenomics.clas.ucdenver.edu/pharmacogenomics/side-effect/) is a single repository of information on over 15,920 pharmaceuticals and the compounds expected to result from metabolism of these drugs. Metabolovigilance functions as both a web server, providing data directly to users and as a web application, applying user inputs to create logic statements that curate the data presented or downloaded. Using this tool, it is easy to collect information on drugs, their side effects, and the metabolites associated with specific side effects. Information on these compounds can be sorted based on physical properties of the drugs and their metabolites. All of this information can be viewed, sorted, and downloaded for use in other applications. This open-access tool will facilitate molecular studies on the causes of adverse drug reactions and is well suited to integrate with genomic data furthering the goals of personalized medicine.

A data-oriented approach to making new molecules as a student experiment: artificial intelligence-enabling FAIR publication of NMR data for organic esters

The lack of machine-readable data is a major obstacle in the application of nuclear magnetic resonance (NMR) in artificial intelligence (AI). As a way to overcome this, a procedure for capturing primary NMR spectroscopic instrumental data annotated with rich metadata and publication in a Findable, Accessible, Interoperable and Reusable (FAIR) data repository is described as part of an undergraduate student laboratory experiment in a chemistry department. This couples the techniques of chemical synthesis of a never before made organic ester with illustration of modern data management practices and serves to raise student awareness of how FAIR data might improve research quality and replicability. Searches of the registered metadata are shown, which enable actionable finding and accessing of such data. The potential for re-use of the data in AI applications is discussed.

Sampling CASE Application for the Quality Control of Published Natural Product Structures

Structure elucidation with NMR correlation data is dicey, as there is no way to tell how ambiguous the data set is and how reliably it will define a constitution. Many different software tools for computer assisted structure elucidation (CASE) have become available over the past decades, all of which could ensure a better quality of the elucidation process, but their use is still not common. Since 2011, WebCocon has integrated the possibility to generate theoretical NMR correlation data, starting from an existing structural proposal, allowing this theoretical data then to be used for CASE. Now, WebCocon can also read the recently presented NMReDATA format, allowing for uncomplicated access to CASE with experimental data. With these capabilities, WebCocon presents itself as an easily accessible Web-Tool for the quality control of proposed new natural products. Results of this application to several molecules from literature are shown and demonstrate how CASE can contribute to improve the reliability of Structure elucidation with NMR correlation data.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

Antiviral iridoid glycosides from Clerodendrum myricoides

The methanol root extract of Clerodendrum myricoides (Hochst.) Vatke afforded two new (1, 2) and two known (3, 4) iridoid glycosides. The structures of the isolated compounds were established based on NMR, IR, UV and MS data analyses. The crude extract and the isolated constituents were assayed for antiviral activity against the human respiratory syncytial virus (RSV) in human laryngeal epidermoid carcinoma (HEp-2) cells. The crude extract inhibited RSV infectivity at EC₅₀ = 0.21 μg/ml, while it showed cytotoxicity against HEp-2 cells with CC₅₀ = 9 μg/ml. Compound 2 showed 43.2% virus inhibition at 100 μM, while compounds 1 as well as 3 and 4 had only weak antiviral and cytotoxic activities.

13C NMR Dereplication Using MixONat Software: A Practical Guide to Decipher Natural Products Mixtures

The growing use of herbal medicines worldwide requires ensuring their quality, safety, and efficiency to consumers and patients. Quality controls of vegetal extracts are usually undertaken according to pharmacopeial monographs. Analyses may range from simple chemical experiments to more sophisticated but more accurate methods. Nowadays, metabolomic analyses allow a fast characterization of complex mixtures. In the field, besides mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR) has gained importance in the direct identification of natural products in complex herbal extracts. For a decade, automated dereplication processes based on ¹³C-NMR have been emerging to efficiently identify known major compounds in mixtures. Though less sensitive than MS, ¹³C-NMR has the advantage of being appropriate to discriminate stereoisomers. Since NMR spectrometers nowadays provide useful datasets in a reasonable time frame, we have recently made available MixONat, a software that processes ¹³C as well as distortionless enhancement by polarization transfer (DEPT)-135 and -90 data, allowing carbon multiplicity (i.e., CH₃, CH₂, CH, and C) filtering as a critical step. MixONat requires experimental or predicted chemical shifts (δ _C) databases and displays interactive results that can be refined based on the user's phytochemical knowledge. The present article provides step-by-step instructions to use MixONat starting from database creation with freely available and/or marketed δ _C datasets. Then, for training purposes, the reader is led through a 30 - 60 min procedure consisting of the ¹³C-NMR based dereplication of a peppermint essential oil.

Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network

Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the chemical structure, bonding, stereochemistry, and conformation of organic compounds. The distinct chemical shifts in an NMR spectrum depend upon each atom's local chemical environment and are influenced by both through-bond and through-space interactions with other atoms and functional groups. The in silico prediction of NMR chemical shifts using quantum mechanical (QM) calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations, are expensive. Additionally, the QM predictions themselves may lack sufficient accuracy to identify a correct structure. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict ¹H and ¹³C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model trained using QM calculations. When tested on the CHESHIRE dataset, the proposed model predicts observed ¹³C chemical shifts with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. An automated prediction webserver and graphical interface are accessible online at http://nova.chem.colostate.edu/cascade/. We further demonstrate the model in three applications: first, we use the model to decide the correct organic structure from candidates through experimental spectra, including complex stereoisomers; second, we automatically detect and revise incorrect chemical shift assignments in a popular NMR database, the NMRShiftDB; and third, we use NMR chemical shifts as descriptors for determination of the sites of electrophilic aromatic substitution.

From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Incorporation of 4J-HMBC and NOE Data into Computer-Assisted Structure Elucidation with WebCocon

Over the past decades, different software programs have been developed for the Computer-Assisted Structure Elucidation (CASE) with NMR data using with various approaches. WebCocon is one of them that has been continuously improved over the past 20 years. Here, we present the inclusion of 4J_CH correlations (4J-HMBC) in the HMBC interpretation of Cocon and NOE data in WebCocon. The 4J-HMBC data is used during the structure generation process, while the NOE data is used in post-processing of the results. The marine natural product oxocyclostylidol was selected to demonstrate WebCocon’s enhanced HMBC data processing capabilities. A systematic study of the 4J_CH correlations of oxocyclostylidol was performed. The application of NOEs in CASE is demonstrated using the NOE correlations of the diterpene pyrone asperginol A known from the literature. As a result, we obtained a conformation that corresponds very well to the existing X-ray structure.

NMReDATA: Tools and applications

The nuclear magnetic resonance extracted data (NMReDATA) format has been proposed as a way to store, exchange, and disseminate nuclear magnetic resonance (NMR) data and physical and chemical metadata of chemical compounds. In this paper, we report on analytical workflows that take advantage of the uniform and standardized NMReDATA format. We also give access to a repository of sample data, which can serve for validating software packages that encode or decode files in NMReDATA format.

Computer Assisted Structure Elucidation (CASE): Current and future perspectives

The first efforts for the development of methods for Computer-Assisted Structure Elucidation (CASE) were published more than 50 years ago. CASE expert systems based on one-dimensional (1D) and two-dimensional (2D) Nuclear Magnetic Resonance (NMR) data have matured considerably by now. The structures of a great number of complex natural products have been elucidated and/or revised using such programs. In this article, we discuss the most likely directions in which CASE will evolve. We act on the premise that a synergistic interaction exists between CASE, new NMR experiments, and methods of computational chemistry, which are continuously being improved. The new developments in NMR experiments (long-range correlation experiments, pure-shift methods, coupling constants measurement and prediction, residual dipolar couplings [RDCs]), and residual chemical shift anisotropies [RCSAs], evolution of density functional theory (DFT), and machine learning algorithms will have an influence on CASE systems and vice versa. This is true also for new techniques for chemical analysis (Atomic Force Microscopy [AFM], "crystalline sponge" X-ray analysis, and micro-Electron Diffraction [micro-ED]), which will be used in combination with expert systems. We foresee that CASE will be utilized widely and become a routine tool for NMR spectroscopists and analysts in academic and industrial laboratories. We believe that the "golden age" of CASE is still in the future.

Non-uniform sampling for NOESY? A case study on spiramycin

To date, most nuclear magnetic resonance (NMR)-based 3-D structure determinations of both small molecules and of biopolymers utilize the nuclear Overhauser effect (NOE) via NOESY spectra. The acquisition of high-quality NOESY spectra is a prerequisite for quantitative analysis providing accurate interatomic distances. As the acquisition of NOE build-ups is time-consuming, acceleration of the process by the use of non-uniform sampling (NUS) may seem beneficial; however, the quantitativity of NOESY spectra acquired with NUS has not yet been validated. Herein, NOESY spectra with various extents of NUS have been recorded, artificial NUS spectra with two different sampling schemes created, and by using two different NUS reconstruction algorithms the influence of NUS on the data quality was evaluated. Using statistical analyses, NUS is demonstrated to influence the accuracy of quantitative NOE experiments. The NOE-based distances show an increased error as the sampling density decreases. Weak NOE signals are affected more severely by NUS than more intense ones. The application of NUS with NOESY comes at two major costs: the interatomic distances are determined with lower accuracy and long-range correlations are lost.

The DEPTQ+ Experiment: Leveling the DEPT Signal Intensities and Clean Spectral Editing for Determining CHn Multiplicities

We propose a new ¹³C DEPTQ⁺ NMR experiment, based on the improved DEPTQ experiment, which is designed to unequivocally identify all carbon multiplicities (Cq, CH, CH₂, and CH₃) in two experiments. Compared to this improved DEPTQ experiment, the DEPTQ⁺ is shorter and the different evolution delays are designed as spin echoes, which can be tuned to different ¹J_CH values; this is especially valuable when a large range of ¹J_CH coupling constants is to be expected. These modifications allow (i) a mutual leveling of the DEPT signal intensities, (ii) a reduction in J cross-talk in the Cq/CH spectrum, and (iii) more consistent and cleaner CH₂/CH₃ edited spectra. The new DEPTQ⁺ is expected to be attractive for fast ¹³C analysis of small-to medium sized molecules, especially in high-throughput laboratories. With concentrated samples and/or by exploiting the high sensitivity of cryogenically cooled ¹³C NMR probeheads, the efficacy of such investigations may be improved, as it is possible to unequivocally identify all carbon multiplicities, with only one scan, for each of the two independent DEPTQ⁺ experiments and without loss of quality.

The Advantage of Automatic Peer-Reviewing of 13C-NMR Reference Data Using the CSEARCH-Protocol

A systematic investigation of the experimental ¹³C-NMR spectra published in Molecules during the period of 1996 to 2015 with respect to their quality using CSEARCH-technology is described. It is shown that the systematic application of the CSEARCH-Robot-Referee during the peer-reviewing process prohibits at least the most trivial assignment errors and wrong structure proposals. In many cases, the correction of the assignments/chemical shift values is possible by manual inspection of the published tables; in certain cases, reprocessing of the original experimental data might help to clarify the situation, showing the urgent need for a public domain repository. A comparison of the significant key numbers derived for Molecules against those of other important journals in the field of natural product chemistry shows a quite similar level of quality for all publishers responsible for the six journals under investigation. From the results of this study, general rules for data handling, data storage, and manuscript preparation can be derived, helping to increase the quality of published NMR-data and making these data available as validated reference material.

Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

Biflavanones, Chalconoids, and Flavonoid Analogues from the Stem Bark of Ochna holstii

Two new biflavanones (1 and 2), three new bichalconoids (3-5), and 11 known flavonoid analogues (6-16) were isolated from the stem bark extract (CH3OH-CH2Cl2, 7:3, v/v) of Ochna holstii. The structures of the isolated metabolites were elucidated by NMR spectroscopic and mass spectrometric analyses. The crude extract and the isolated metabolites were evaluated for antibacterial activity against Bacillus subtilis (Gram-positive) and Escherichia coli (Gram-negative) as well as for cytotoxicity against the MCF-7 human breast cancer cell line. The crude extract and holstiinone A (1) exhibited moderate antibacterial activity against B. subtilis with MIC values of 9.1 μg/mL and 14 μM, respectively. The crude extract and lophirone F (14) showed cytotoxicity against MCF-7 with EC50 values of 11 μg/mL and 24 μM, respectively. The other isolated metabolites showed no significant antibacterial activities (MIC > 250 μM) and cytotoxicities (EC50 ≥ 350 μM).

Antibacterial and cytotoxic biflavonoids from the root bark of Ochna kirkii

The new isoflavonoid kirkinone A (1) and biflavonoid kirkinone B (2) along with six known compounds (3-8) were isolated from the methanolic extract of the root bark of Ochna kirkii. The compounds were identified by NMR spectroscopic and mass spectrometric analyses. Out of the eight isolated natural products, calodenin B (4) and lophirone A (6) showed significant antibacterial activity against the Gram-positive bacterium Bacillus subtilis with MIC values of 2.2 and 28 μM, and cytotoxicity against the MCF-7 human breast cancer cell line with EC₅₀ values of 219.3 and 19.2 μM, respectively. The methanolic crude extract of the root bark exhibited cytotoxicity at EC₅₀ 8.4 μg/mL. The isolated secondary metabolites and the crude extract were generally inactive against the Gram-negative Escherichia coli (MIC ≥400 μg/mL). Isolation of biflavonoids and related secondary metabolites from O. kirkii demonstrates their chemotaxonomic significance to the genus Ochna and to other members of the family Ochnaceae.

Quantum mechanical reference spectrum simulation for precursors and degradation products of chemicals relevant to the Chemical Weapons Convention

A selection of acidic, alkaline and neutral degradation products relevant to the Chemical Weapons Convention was studied in wide range of pH conditions to determine their spin systems as well as spectral parameters. The pH dependence of chemical shifts and J couplings was parameterized using Henderson-Hasselbalch-based functions using dichloromethane as additional shift reference in TSP-d₄ referenced spectra. The resulting parameters allowed calculation of precise chemical shifts and J coupling constants in arbitrary pH conditions. The validity of the obtained spin system definitions and parameters as a source of quantum mechanically simulated reference data in chemical verification analysis is demonstrated.

The Three Pillars of Natural Product Dereplication. Alkaloids from the Bulbs of Urceolina peruviana (C. Presl) J.F. Macbr. as a Preliminary Test Case

The role and importance of the identification of natural products are discussed in the perspective of the study of secondary metabolites. The rapid identification of already reported compounds, or structural dereplication, is recognized as a key element in natural product chemistry. The biological taxonomy of metabolite producing organisms, the knowledge of metabolite molecular structures, and the availability of metabolite spectroscopic signatures are considered as the three pillars of structural dereplication. The role and the construction of databases is illustrated by references to the KNApSAcK, UNPD, CSEARCH, and COCONUT databases, and by the importance of calculated taxonomic and spectroscopic data as substitutes for missing or lost original ones. Two NMR-based tools, the PNMRNP database that derives from UNPD, and KnapsackSearch, a database generator that provides taxonomically focused libraries of compounds, are proposed to the community of natural product chemists. The study of the alkaloids from Urceolina peruviana, a plant from the Andes used in traditional medicine for antibacterial and anticancer actions, has given the opportunity to test different approaches to dereplication, favoring the use of publicly available data sources.

Prenylated Flavonoids from the Roots of Tephrosia rhodesica

Five new compounds-rhodimer (1), rhodiflavan A (2), rhodiflavan B (3), rhodiflavan C (4), and rhodacarpin (5)-along with 16 known secondary metabolites, were isolated from the CH₂Cl₂-CH₃OH (1:1) extract of the roots of Tephrosia rhodesica. They were identified by NMR spectroscopic, mass spectrometric, X-ray crystallographic, and ECD spectroscopic analyses. The crude extract and the isolated compounds 2-5, 9, 15, and 21 showed activity (100% at 10 μg and IC₅₀ = 5-15 μM) against the chloroquine-sensitive (3D7) strain of Plasmodium falciparum.

NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation.

This overarching goal is achieved by working towards a number of key objectives:

Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories.

Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack.

Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula.

Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers.

Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI.

Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

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.

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix Factorization

Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time-frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T₂* relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.

Oxygenated Cyclohexene Derivatives and Other Constituents from the Roots of Monanthotaxis trichocarpa

Three new oxygenated cyclohexene derivatives, trichocarpeols A (1), B (2), and C (3), along with nine known secondary metabolites, were isolated from the methanolic root extract of Monanthotaxis trichocarpa. They were identified by NMR spectroscopic and mass spectrometric analyses, and the structure of trichocarpeol A (1) was confirmed by single-crystal X-ray diffraction. Out of the 12 isolated natural products, uvaretin (4) showed activity against the Gram-positive bacterium Bacillus subtilis with a MIC value of 18 μM. None of the isolated metabolites was active against the Gram-negative Escherichia coli at a ∼5 mM (2000 μg/mL) concentration. Whereas 4 showed cytotoxicity at EC50 10.2 μM against the MCF-7 human breast cancer cell line, the other compounds were inactive or not tested.

Conformational Investigations in Flexible Molecules Using Orientational NMR Constraints in Combination with 3J-Couplings and NOE Distances

The downscaling of NMR tensorial interactions, such as dipolar couplings, from tens of kilohertz to a few hertz in low-order media is the result of dynamics spanning several orders of magnitudes, including vibrational modes (~ns-fs), whole-molecule reorientation (~ns) and higher barrier internal conformational exchange (<ms). In this work, we propose to employ these dynamically averaged interactions to drive an “alignment-tensor-free” molecular dynamic simulation with orientation constraints (MDOC) in order to efficiently access the conformational space sampled by flexible small molecules such as natural products. Key to this approach is the application of tensorial pseudo-force restraints which simultaneously guide the overall reorientation and conformational fluctuations based on defined memory function over the running trajectory. With the molecular mechanics force-field, which includes bond polarization theory (BPT), and complemented with other available NMR parameters such as NOEs and scalar J-couplings, MDOC efficiently arrives at dynamic ensembles that reproduce the entire NMR dataset with exquisite accuracy and theoretically reveal the systems conformational space and equilibrium. The method as well as its potential towards configurational elucidation is presented on diastereomeric pairs of flexible molecules: a small 1,4-diketone 1 with a single rotatable bond as well as a 24-ring macrolide related to the natural product mandelalide A 2.