MixONat, a Software for the Dereplication of Mixtures Based on 13C NMR Spectroscopy

Looking for Actives in the Haystack: Merging HRMS2-Based Molecular Networking, Chemometrics, and 13C NMR-Based Dereplication Approaches

The identification of bioactive natural products (NPs) in complex mixtures has become an important subject of contemporary NP research. In an attempt to address this challenge, the present work proposes an integrated strategy that combines tandem mass spectrometry (MS2)-based molecular networking (MN), a partial least-squares (PLS) chemometric model, as well as 13C NMR-based dereplication using MixONat software. In addition, an advanced glycation end product (AGEs) assay was used for activity evaluation. The approach was implemented on a Garcinia parvifolia bark extract that comprised a high content of prenylated xanthones and had previously shown a notable inhibitory effect on AGE formation. As a main result, the proposed strategy permitted the identification of potentially active metabolites within complex mixtures and their annotation with a higher level of confidence by NMR data. Overall, this comprehensive approach provides a powerful and efficient solution for the targeting and annotating of active compounds in complex NP mixtures.

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.

Recent Advancement in NMR Based Plant Metabolomics: Techniques, Tools, and Analytical Approaches

Plant metabolomics, a rapidly advancing field within plant biology, is dedicated to comprehensively exploring the intricate array of small molecules in plant systems. This entails precisely gathering comprehensive chemical data, detecting numerous metabolites, and ensuring accurate molecular identification. Nuclear magnetic resonance (NMR) spectroscopy, with its detailed chemical insights, is crucial in obtaining metabolite profiles. Its widespread application spans various research disciplines, aiding in comprehending chemical reactions, kinetics, and molecule characterization. Biotechnological advancements have further expanded NMR's utility in metabolomics, particularly in identifying disease biomarkers across diverse fields such as agriculture, medicine, and pharmacology. This review covers the stages of NMR-based metabolomics, including historical aspects and limitations, with sample preparation, data acquisition, spectral processing, analysis, and their application parts.

Xanthone Inhibitors of Unfolded Protein Response Isolated from Calophyllum caledonicum

The unfolded protein response (UPR) is a key component of fungal virulence. The prenylated xanthone γ-mangostin isolated from Garcinia mangostana (Clusiaceae) fruit pericarp, has recently been described to inhibit this fungal adaptative pathway. Considering that Calophyllum caledonicum (Calophyllaceae) is known for its high prenylated xanthone content, its stem bark extract was fractionated using a bioassay-guided procedure based on the cell-based anti-UPR assay. Four previously undescribed xanthone derivatives were isolated, caledonixanthones N-Q (3, 4, 8, and 12), among which compounds 3 and 8 showed promising anti-UPR activities with IC50 values of 11.7 ± 0.9 and 7.9 ± 0.3 μM, respectively.

Deconvolution and Analysis of the 1H NMR Spectra of Crude Reaction Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity of manually analyzing the NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with the experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C-H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with a mean absolute error as low as 1%.

Cross-Modal Retrieval Between 13C NMR Spectra and Structures Based on Focused Libraries

Library matching by comparing carbon-13 nuclear magnetic resonance (13C NMR) spectra with spectral data in the library is a crucial method for compound identification. In our previous paper, we introduced a deep contrastive learning system called CReSS, which used a library that contained more structures. However, CReSS has two limitations: there were no unknown structures in the library, and a redundant library reduces the structure-elucidation accuracy. Herein, we replaced the oversize traditional libraries with focused libraries containing a small number of molecules. A previously generative model, CMGNet, was used to generate focused libraries for CReSS. The combined model achieved a Top-10 accuracy of 54.03% when tested on 6,471 13C NMR spectra. In comparison, CReSS with a random reference structure library achieved an accuracy of only 9.17%. Furthermore, to expand the advantages of the focused libraries, we proposed SAmpRNN, which is a recurrent neural network (RNN). With the large focused library amplified by SAmpRNN, the structure-identification accuracy of the model increased in 70.0% of the 30 random example cases. In general, cross-modal retrieval between 13C NMR spectra and structures based on focused libraries (CFLS) achieved high accuracy and provided more accurate candidate structures than traditional libraries for compound identification.

Automated nuclear magnetic resonance fingerprinting of mixtures

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group-based approach "NMR fingerprinting." In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, 13C, 1H, and 13C DEPT NMR spectra. Our approach is based on the machine-learning method of support vector classification (SVC), which was trained here on thousands of labeled pure-component NMR spectra from open-source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (https://nmr-fingerprinting.de), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group-contribution methods-or simply as a first step in species analysis.

13C NMR dereplication-assisted isolation of bioactive polyphenolic metabolites from Clusia flava Jacq

Presently it is estimated that many of the approximately 4000 new natural products isolated every year following complicated, long, and expensive isolation processes are already known; because of this, developing new strategies for locating secondary metabolites of interest in complex extracts or fractions is important. Currently, chromatographic and spectroscopic techniques are being used to optimize the isolation and identification of natural products. In this investigation we have used 13C NMR dereplication analyses for the quick identification of a number of triterpenes (friedelin, lupeol, betulinic acid), sterols (euphol, β-sitosterol) and fatty acids (palmitic acid) present in semipurified fractions obtained from the stem bark extract of Clusia flava and to assist in the isolation of the bioactive metabolites trapezifolixanthone and paralycolin A. The complete and correct assignment of the 1H and 13C NMR spectroscopic data for paralycolin A is reported for the first time and the antioxidant and antiAGEs activity of both metabolites is described.

Natural Products Dereplication: Databases and Analytical Methods

The development of efficient methods for dereplication has been critical in the re-emergence of the research in natural products as a source of drug leads. Current dereplication workflows rapidly identify already known bioactive secondary metabolites in the early stages of any drug discovery screening campaign based on natural extracts or enriched fractions. Two main factors have driven the evolution of natural products dereplication over the last decades. First, the availability of both commercial and public large databases of natural products containing the key annotations against which the biological and chemical data derived from the studied sample are searched for. Second, the considerable improvement achieved in analytical technologies (including instrumentation and software tools) employed to obtain robust and precise chemical information (particularly spectroscopic signatures) on the compounds present in the bioactive natural product samples. This chapter describes the main methods of dereplication, which rely on the combined use of large natural product databases and spectral libraries, alongside the information obtained from chromatographic, UV-Vis, MS, and NMR spectroscopic analyses of the samples of interest.

13C NMR-based dereplication using MixONat software to decipher potent anti-cholinesterase compounds in Mesua lepidota bark

A bio-assay guided fractionation strategy based on cholinesterase assay combined with 13C NMR-based dereplication was used to identify active metabolites from the bark of Mesua lepidota. Eight compounds were identified with the aid of the 13C NMR-based dereplication software, MixONat, i.e., sitosterol (1), stigmasterol (2), α-amyrin (3), friedelin (6), 3β-friedelinol (7), betulinic acid (9), lepidotol A (10) and lepidotol B (11). Further bio-assay guided isolation of active compounds afforded one xanthone, pyranojacareubin (12) and six coumarins; lepidotol A (10), lepidotol B (11), lepidotol E (13), lepidotin A (14), and lepidotin B (15), including a new Mammea coumarin, lepidotin C (16). All the metabolites showed strong to moderate butyrylcholinesterase (BChE) inhibition. Lepidotin B (15) exhibited the most potent inhibition towards BChE with a mix-mode inhibition profile and a Ki value of 1.03 µM. Molecular docking and molecular dynamics simulations have revealed that lepidotin B (15) forms stable interactions with key residues within five critical regions of BChE. These regions encompass residues Asp70 and Tyr332, the acyl hydrophobic pocket marked by Leu286, the catalytic triad represented by Ser198 and His438, the oxyanion hole (OH) constituted by Gly116 and Gly117, and the choline binding site featuring Trp82. To gauge the binding strength of lepidotin B (15) and to pinpoint pivotal residues at the binding interface, free energy calculations were conducted using the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) approach. This analysis not only predicted a favourable binding affinity for lepidotin B (15) but also facilitated the identification of significant residues crucial for the binding interaction.

Machine learning-assisted structure annotation of natural products based on MS and NMR data

Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

Rapid Chemical Profiling of Filipendula ulmaria Using CPC Fractionation, 2-D Mapping of 13C NMR Data, and High-Resolution LC-MS

Filipendula ulmaria, commonly known as meadowsweet, is a wild herbaceous flowering plant that is widely distributed in Europe. A range of salicylic acid derivatives and flavonol glycosides have been previously associated with the antirheumatic and diuretic properties of F. ulmaria. In the present work, a hydroalcoholic extract from F. ulmaria aerial parts was extensively profiled using an efficient NMR-based dereplication strategy. The approach involves the fractionation of the crude extract by centrifugal partition chromatography (CPC), 13C NMR analysis of the fractions, 2D-cluster mapping of the entire NMR dataset, and, finally, structure elucidation using a natural metabolite database, validated by 2D NMR data interpretation and liquid chromatography coupled with mass spectrometry. The chemodiversity of the aerial parts was extensive, with 28 compounds unambiguously identified, spanning various biosynthetic classes. The F. ulmaria extract and CPC fractions were screened for their potential to enhance skin epidermal barrier function and skin renewal properties using in vitro assays performed on Normal Human Epidermal Keratinocytes. Fractions containing quercetin, kaempferol glycosides, ursolic acid, pomolic acid, naringenin, β-sitosterol, and Tellimagrandins I and II were found to upregulate genes related to skin barrier function, epidermal renewal, and stress responses. This research is significant as it could provide a natural solution for improving hydration and skin renewal properties.

Chemodiversity of propolis samples collected in various areas of Benin and Congo: Chromatographic profiling and chemical characterization guided by 13 C NMR dereplication

INTRODUCTION

Propolis is a resinous natural substance collected by honeybees from buds and exudates of various trees and plants; it is widely accepted that the composition of propolis depends on the phytogeographic characteristics of the site of collection.

OBJECTIVES

The aim of this study was to determine the phytochemical composition of ethanolic extracts from eight propolis batches collected in different regions of Benin (north, center, and south) and Congo, Africa.

MATERIAL AND METHODS

Characterization of propolis samples was performed by using different hyphenated chromatographic methods combined with carbon-13 nuclear magnetic resonance (¹³ C NMR) dereplication with MixONat software. Their antioxidant or anti-advanced glycation end-product (anti-AGE) activity was then evaluated by using diphenylpicrylhydrazyl and bovine serum albumin assays, respectively.

RESULTS

Chromatographic analyses combined with ¹³ C NMR dereplication showed that two samples from the center of Benin exhibited, in addition to a huge amount of pentacyclic triterpenes, methoxylated stilbenoids or phenanthrenoids, responsible for the antioxidant activity of the extract for the first one. Among them, combretastatins might be cytotoxic. For the second one, the prenylated flavanones known in Macaranga-type propolis were responsible for its significant anti-AGE activity. The sample from Congo was composed of many triterpene derivatives belonging to Mangifera indica species.

CONCLUSION

Therefore, propolis from the center of Benin seems to be of particular interest, due to its antioxidant and anti-AGE properties. Nevertheless, as standardization of propolis is difficult in tropical zones due to its great chemodiversity, a systematic phytochemical analysis is required before promoting the use of propolis in food and health products in Africa.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Modern Trends in Natural Antibiotic Discovery

Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.

Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on 13C NMR Spectra and Prior Knowledge

Structure elucidation of unknown compounds based on nuclear magnetic resonance (NMR) remains a challenging problem in both synthetic organic and natural product chemistry. Library matching has been an efficient method to assist structure elucidation. However, it is limited by the coverage of libraries. In addition, prior knowledge such as molecular fragments is neglected. To solve the problem, we propose a conditional molecular generation net (CMGNet) to allow input of multiple sources of information. CMGNet not only uses ¹³C NMR spectrum data as input but molecular formulas and fragments of molecules are also employed as input conditions. Our model applies large-scale pretraining for molecular understanding and fine-tuning on two NMR spectral data sets of different granularity levels to accommodate structure elucidation tasks. CMGNet generates structures based on ¹³C NMR data, molecular formula, and fragment information, with a recovery rate of 94.17% in the top 10 recommendations. In addition, the generative model performed well in the generation of various classes of compounds and in the structural revision task. CMGNet has a deep understanding of molecular connectivities from ¹³C NMR, molecular formula, and fragments, paving the way for a new paradigm of deep learning-assisted inverse problem-solving.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Unveiling antiplasmodial alkaloids from a cumulative collection of Strychnos extracts by multi-informative molecular networks

Malaria, a disease known for thousands of years and caused by parasites of the Plasmodium genus, continues to cause many deaths throughout the world today, particularly due to the emergence of parasite resistance to the current therapeutic arsenal. Plants of the Strychnos genus, remarkable due to their multiple traditional uses as well as their alkaloid content, are promising candidates to develop new antimalarial treatments. Indeed, previous research on this plant group has shown promising (≤ 5 µg/ml) or good (between 5 and 15 µg/ml) antiplasmodial activities. Using the chloroquine-sensitive strain of Plasmodium falciparum (3D7), and artemisinin as positive control, a screening of antiplasmodial activities from 43 crude methanolic extracts from 28 species of the Strychnos genus was carried out in three independent assays. A total of 12 extracts had good (6 extracts) or promising (6 extracts) antiplasmodial activities. These results allowed both to confirm known activities but also to detect new ones. These extracts were then analyzed by HPLC-ESI(+)-Q/TOF, and the processed MS/MS data allowed to generate a molecular network in which the antiplasmodial activities were implemented as metadata. The exploration of the molecular network revealed the presence of alkaloids still unknown, and potentially active against malaria, in particular alkaloids close to usambarensine and its derivatives. This study shows that the emergence of molecular networking offers new leads for identifications of alkaloids from the Strychnos genus. The presence of unknown alkaloids potentially active against malaria confirms all the interest to continue in studying the Strychnos genus. Bioassay- and mass-guided fractionations as well as various dereplication tools would allow to identify and characterize these interesting alkaloids further.

Dereplication of Fungal Metabolites by NMR-Based Compound Networking Using MADByTE

Strategies for natural product dereplication are continually evolving, essentially in lock step with advances in MS and NMR techniques. MADByTE is a new platform designed to identify common structural features between samples in complex extract libraries using two-dimensional NMR spectra. This study evaluated the performance of MADByTE for compound dereplication by examining two classes of fungal metabolites, the resorcylic acid lactones (RALs) and spirobisnaphthalenes. First, a pure compound database was created using the HSQC and TOCSY data from 19 RALs and 10 spirobisnaphthalenes. Second, this database was used to assess the accuracy of compound class clustering through the generation of a spin system feature network. Seven fungal extracts were dereplicated using this approach, leading to the correct prediction of members of both families from the extract set. Finally, NMR-guided isolation led to the discovery of three new palmarumycins (20-22). Together these results demonstrate that MADByTE is effective for the detection of specific compound classes in complex mixtures and that this detection is possible for both known and new natural products.

Cross-Modal Retrieval between 13C NMR Spectra and Structures for Compound Identification Using Deep Contrastive Learning

Library matching using carbon-13 nuclear magnetic resonance (¹³C NMR) spectra has been a popular method adopted in compound identification systems. However, the usability of existing approaches has been restricted as enlarging a library containing both a chemical structure and spectrum is a costly and time-consuming process. Therefore, we propose a fundamentally different, novel approach to match ¹³C NMR spectra directly against a molecular structure library. We develop a cross-modal retrieval between spectrum and structure (CReSS) system using deep contrastive learning, which allows us to search a molecular structure library using the ¹³C NMR spectrum of a compound. In the test of searching 41,494 ¹³C NMR spectra against a reference structure library containing 10.4 million compounds, CReSS reached a recall@10 accuracy of 91.64% and a processing speed of 0.114 s per query spectrum. When further incorporating a filter with a molecular weight tolerance of 5 Da, CReSS achieved a new remarkable recall@10 of 98.39%. Furthermore, CReSS has potential in detecting scaffolds of novel structures and demonstrates great performance for the task of structural revision. CReSS is built and developed to bridge the gap between ¹³C NMR spectra and structures and could be generally applicable in compound identification.

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.

Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms

Plants and associated microorganisms are essential sources of natural products against human cancer diseases, partly exemplified by plant-derived anticancer drugs such as Taxol (paclitaxel). Natural products provide diverse mechanisms of action and can be used directly or as prodrugs for further anticancer optimization. Despite the success, major bottlenecks can delay anticancer lead discovery and implementation. Recent advances in sequencing and omics-related technology have provided a mine of information for developing new therapeutics from natural products. Artificial intelligence (AI), including machine learning (ML), has offered powerful techniques for extensive data analysis and prediction-making in anticancer leads discovery. This review presents an overview of current AI-guided solutions to discover anticancer lead compounds, focusing on natural products from plants and associated microorganisms.

Using 13 C-NMR dereplication to aid in the identification of xanthones present in the stem bark extract of Calophyllum brasiliense

INTRODUCTION

Xanthones are metabolites with a variety of biological properties. The Clusiaceae family, which until recently included the genus Calophyllum, is recognised for its production of monohydroxylated and polyhydroxylated xanthones. Presently, C. brasiliense is the only Calophyllum spp. known to occur in the Yucatan peninsula.

OBJECTIVE

To use a combination of traditional phytochemical methods and carbon-13 nuclear magnetic resonance (¹³ C-NMR) dereplication analysis to identify xanthones in the stem bark of C. brasiliense.

MATERIAL AND METHODS

Initial fractionation and purification of the stem bark extract of C. brasiliense produced macluraxanthone (1). Additional xanthones, together with chromanones and terpenoids, were identified using ¹³ C-NMR dereplication analysis in different semipurified fractions obtained from the low and medium polarity fractions of the stem bark extract of C. brasiliense.

RESULTS

Initial identification of macluraxanthone (1) was confirmed by ¹³ C-NMR dereplication analysis; additionally, ¹³ C-NMR dereplication analysis allowed the identification of a number of monohydroxylated and polyhydroxylated xanthones, together with chromanones and terpenoids.

CONCLUSION

This study confirms C. brasiliense as a rich source of xanthones and the ¹³ C-NMR dereplication analysis as a suitable method to quickly identify the presence of different families of secondary metabolites in semipurified fractions.

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.

Taxonomy-Focused Natural Product Databases for Carbon-13 NMR-Based Dereplication

The recent revival of the study of organic natural products as renewable sources of medicinal drugs, cosmetics, dyes, and materials motivated the creation of general purpose structural databases. Dereplication, the efficient identification of already reported compounds, relies on the grouping of structural, taxonomic and spectroscopic databases that focus on a particular taxon (species, genus, family, order, etc.). A set of freely available python scripts, CNMR_Predict, is proposed for the quick supplementation of taxon oriented search results from the naturaL prOducTs occUrrences database (LOTUS, lotus.naturalproducts.net) with predicted carbon-13 nuclear magnetic resonance data from the ACD/Labs CNMR predictor and DB software (acdlabs.com) to provide easily searchable databases. The database construction process is illustrated using Brassica rapa as a taxon example.

Synthetic biology approaches to actinomycete strain improvement

Their biochemical versatility and biotechnological importance make actinomycete bacteria attractive targets for ambitious genetic engineering using the toolkit of synthetic biology. But their complex biology also poses unique challenges. This mini review discusses some of the recent advances in synthetic biology approaches from an actinomycete perspective and presents examples of their application to the rational improvement of industrially relevant strains.

Combining NMR-Based Metabolic Profiling and Genome Mining for the Accelerated Discovery of Archangiumide, an Allenic Macrolide from the Myxobacterium Archangium violaceum SDU8

An unprecedented 19-membered allenic macrolide archangiumide (1) was discovered from the myxobacterium Archangium violaceum SDU8 by integrating NMR-based metabolic profiling and genome mining. Its biosynthesis pathway was proposed based on the architectural analysis of the encoding trans-AT PKS genes and validated by isotope labeling. The methodology of combing 2D NMR-based metabolic profiling and bioinformatics-aided structure prediction, as exemplified by this study, is anticipated to improve discovery efficiency of a broader range of microbial "dark matter".

The year 2020 in natural product bioinformatics: an overview of the latest tools and databases

Covering: 2020 Bioinformatic approaches to document and analyse chemical structures, biosynthetic gene clusters and analytical data play an important role in the study of natural products. Every year, such a large number of new algorithms, tools and databases are released, that it is difficult to keep track of all the latest developments. The aim of this short article is to provide a concise overview of and reference to the major tools, methods and databases that have been released in the past year.

A thorough evaluation of matrix-free laser desorption ionization on structurally diverse alkaloids and their direct detection in plant extracts

Alkaloids represent a major group of natural products (NPs), derived from highly diverse organisms. These structurally varied specialized metabolites are widely used for medicinal purposes and also known as toxic contaminants in agriculture and dietary supplements. While the detection of alkaloids is generally facilitated by GC- or LC-MS, these techniques do require considerable efforts in sample preparation and method optimization. Bypassing these limitations and also reducing experimental time, matrix-free laser desorption ionization (LDI) and related methods may provide an interesting alternative. As many alkaloids show close structural similarities to matrices used in matrix-assisted laser desorption ionization (MALDI), they should ionize upon simple laser irradiation without matrix support. With this in mind, the current work presents a systematic evaluation of LDI properties of a wide range of structurally diverse alkaloids. Facilitating a direct comparison between LDI and ESI-MS fragmentation, all tested compounds were further studied by electrospray ionization (ESI). Moreover, crude plant extracts of Atropa belladonna, Cinchona succirubra, and Colchicum autumnale were analyzed by LDI in order to evaluate direct alkaloid detection and dereplication from complex mixtures. Finally, dose-dependent evaluation of MALDI and LDI detection using an extract of Rosmarinus officinalis spiked with atropine, colchicine, or quinine was conducted. Overall, present results suggest that LDI provides a versatile analytical tool for analyzing structurally diverse alkaloids as single compounds and from complex mixtures. It may further serve various potential applications ranging from quality control to the screening for toxic compounds as well as the build up of MS databases. Graphical abstract.