Search

The field of metabolomics has witnessed remarkable growth in the context of natural products studies, with Mass Spectrometry (MS) being the predominant analytical tool for data acquisition. However, MS has inherent limitations when it comes to the structural elucidation of key metabolites, which can hinder comprehensive compound identification. This review paper discusses the integration of Nuclear Magnetic Resonance (NMR) spectroscopy as a complementary technique to address these limitations. We explore the concept of Quality Control (QC) samples, emphasizing their potential use for in-depth compound annotation and identification. Additionally, we discuss NMR's advantages, limitations, and strategies to enhance sensitivity. We present examples where MS alone falls short in delivering accurate compound identification and introduce various tools for NMR compound identification in complex mixtures and the integration of MS and NMR data. Finally, we delve into the concept of DBsimilarity to broaden the chemical space understanding, aiding in compound annotation and the creation of compound lists for specific sample analyses. [ABSTRACT FROM AUTHOR]

Strychnos peckii (Loganiaceae) is an important active ingredient in curare poisons in the Amazon rainforest. Although previous studies have identified this species as a promising source of monoterpene indole alkaloids (MIAs), knowledge about other natural products is still scarce. Thus, to detect and guide the isolation of unprecedented bioactive compounds from the leaves of S. peckii, an untargeted high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS) analysis was performed with the leaf aqueous extract. The HPLCMS analysis allowed the detection of eleven compounds, including the alkaloids harman-3-carboxylic acid (5) and N,ß-glucopyranosyl vincosamide (6), and the flavonoids quercetin 3-O-rhamnopyranoside (9) and kaempferol 3-Orhamnopyranoside (10), all not previously reported in the Loganiaceae family. These compounds, along with strictosidine (3), 5-carboxystrictosidine (7), and desoxycordifoline (8) were isolated through modern chromatographic techniques and determined by using NMR spectroscopy in combination with MS. Overall, the untargeted HPLC-MS analysis proved to be a simple and effective approach to guide the isolation of substances not yet identified from S. peckii. [ABSTRACT FROM AUTHOR]

Advanced techniques can accelerate the pace of natural product discovery from microbes, which has been lagging behind the drug discovery era. Therefore, the present review article discusses the various interdisciplinary and cutting-edge techniques to present a concrete strategy that enables the high-throughput screening of novel natural compounds (NCs) from known microbes. Recent bioinformatics methods revealed that the microbial genome contains a huge untapped reservoir of silent biosynthetic gene clusters (BGC). This article describes several methods to identify the microbial strains with hidden mines of silent BGCs. Moreover, antiSMASH 5.0 is a free, accurate, and highly reliable bioinformatics tool discussed in detail to identify silent BGCs in the microbial genome. Further, the latest microbial culture technique, HiTES (high-throughput elicitor screening), has been detailed for the expression of silent BGCs using 500–1000 different growth conditions at a time. Following the expression of silent BGCs, the latest mass spectrometry methods are highlighted to identify the NCs. The recently emerged LAESI-IMS (laser ablation electrospray ionization-imaging mass spectrometry) technique, which enables the rapid identification of novel NCs directly from microtiter plates, is presented in detail. Finally, various trending 'dereplication' strategies are emphasized to increase the effectiveness of NC screening. [ABSTRACT FROM AUTHOR]

In the present study, we have evaluated the cytotoxic activity of 282 extracts from 72 native plant species of the Brazilian Atlantic Forest biome. As a result, Casearia arborea and Sorocea hilarii leaves extracts showed cytotoxic activity against three tumour cell lines tested (B16F10, SW480 and Jurkat). After bioassay-guided fractionation, the bioactive fractions were submitted to the dereplication study via High-performance Liquid Chromatography, connected to High-resolution Mass Spectrometry (HPLC-ESI-QTOF/MS) analysis, combined with a Global Natural Products Social Molecular Networking (GNPS) tool. A combination of bioactivity-guided and dereplication approaches resulted in the putative annotation of 27 clerodane diterpenes and 9 flavonoids as main compounds present in the cytotoxic fractions of C. arborea. Regarding the active fraction of S. hilarii, 10 megastigmans, 17 spirostane steroids derivatives and 2 lignans were putatively identified. In conclusion, Casearia arborea and Sorocea hilarii are potential sources of antitumor compounds. [ABSTRACT FROM AUTHOR]

Recently expanded reports of multidrug-resistant fungal infections underscore the need to develop new and more efficient methods for antifungal drug discovery. A ubiquitous problem in natural product drug discovery campaigns is the rediscovery of known compounds or their relatives; accordingly, we have integrated Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS) for structural dereplication and Yeast Chemical Genomics for bioprocess evaluation into a screening platform to identify such compounds early in the screening process. We identified 450 fractions inhibiting Candida albicans and the resistant strains of C. auris and C. glabrata among more than 40,000 natural product fractions. LC-MS/MS and chemical genomics were then used to identify those with known chemistry and mechanisms of action. The parallel deployment of these orthogonal methods improved the detection of unwanted compound classes over the methods applied individually.

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. [ABSTRACT FROM AUTHOR]

Introduction: Natural deep eutectic solvents (NADES) have emerged as interesting extractants to develop botanical ingredients. They are nontoxic and biodegradable, nonflammable, easy to prepare, and able to solubilize a wide range of molecules. However, NADES extracts remain difficult to analyze because the metabolites of interest stay highly diluted in the nonvolatile viscous NADES matrix. Objective: This study presents a robust analytical workflow for the chemical profiling of NADES extracts. It is applied to Hypericum perforatum aerial parts extracted with the neutral mixture fructose/glycerol/water (3/1/1, w/w/w), and compared to the chemical profiling of a classical dry methanol extract. Methods: Exploiting polarity differences between metabolites, the H. perforatum NADES extract was partitioned in a liquid–liquid solvent system to trap the hydrophilic NADES constituents in the lower phase. The upper phase, containing a diversity of secondary metabolites from H. perforatum, was fractionated by centrifugal partition chromatography. All fractions were chemically investigated using a 13C NMR dereplication method which involves hierarchical clustering analysis of the whole NMR dataset, a natural metabolite database for metabolite identification, and 2D NMR analyses for validation. Liquid chromatography–mass spectrometry (LC‐MS) analyses were also performed to complete the identification process. Results: A range of 21 metabolites were unambiguously identified, including glycosylated flavonols, lactones, catechins, phenolic acids, lipids, and simple sugars, and 15 additional minor extract constituents were annotated by LC‐MS based on exact mass measurements. Conclusion: The proposed identification process is rapid and nondestructive and provides good prospects to deeply characterize botanical extracts obtained in nonvolatile and viscous NADES systems. Botanical ingredients developed in NADES remain difficult to analyze because the metabolites of interest are diluted in the nonvolatile viscous NADES matrix. A robust analytical workflow was developed on Hypericum perforatum aerial parts extracted with the neutral mixture fructose/glycerol/water (3/1/1, w/w/w). The extract was partitioned in a biphasic system to trap hydrophilic NADES constituents in the lower phase. The upper phase was chemically profiled by NMR dereplication. 21 metabolites were unambiguously identified and 15 minor compounds were annotated by LC‐MS. [ABSTRACT FROM AUTHOR]

The discovery of new natural products has become more challenging because of the re-isolation of compounds and the lack of new sources. Microbes dwelling in extreme conditions of high salinity and temperature are huge prospects for interesting natural metabolites. In this study, the endophytic bacteria Bacillus velezensis 7NPB-3B isolated from the halophyte Salicornia brachiata was screened for its biofilm inhibition against methicillin-resistant Staphylococcus aureus (MRSA). The fractionation of the crude extract was guided by bioassay and LC-HRMS-based metabolomics using multivariate analysis. The 37 fractions obtained by high-throughput chromatography were dereplicated using an in-house MS-Excel macro coupled with the Dictionary of Natural Products database. Successive bioactivity-guided separation yielded one novel compound (1), a diketopiperazine (m/z 469.258 [M − H]−) with an attached saturated decanoic acid chain, and four known compounds (2–5). The compounds were identified based on 1D- and 2D-NMR and mass spectrometry. Compounds 1 and 5 exhibited excellent biofilm inhibition properties of >90% against the MRSA pathogen at minimum inhibition concentrations of 25 and 35 µg/mL, respectively. The investigation resulted in the isolation of a novel diketopiperazine from a bacterial endophyte of an untapped plant using an omics approach. [ABSTRACT FROM AUTHOR]

The field of metabolomics has witnessed remarkable growth in the context of natural products studies, with Mass Spectrometry (MS) being the predominant analytical tool for data acquisition. However, MS has inherent limitations when it comes to the structural elucidation of key metabolites, which can hinder comprehensive compound identification. This review paper discusses the integration of Nuclear Magnetic Resonance (NMR) spectroscopy as a complementary technique to address these limitations. We explore the concept of Quality Control (QC) samples, emphasizing their potential use for in-depth compound annotation and identification. Additionally, we discuss NMR’s advantages, limitations, and strategies to enhance sensitivity. We present examples where MS alone falls short in delivering accurate compound identification and introduce various tools for NMR compound identification in complex mixtures and the integration of MS and NMR data. Finally, we delve into the concept of DBsimilarity to broaden the chemical space understanding, aiding in compound annotation and the creation of compound lists for specific sample analyses.

Advancements in genome mining, high-throughput sequencing and experimental techniques have generated an enormous amount of data on natural products. This has led to the design and development of advanced machine learning (ML) and artificial intelligence (AI) algorithms which have simplified the search for novel natural products in the 21st century. These algorithms could effectively analyse the chemical structure of natural products and predict their biological function. They could also effectively analyse large sets of data in a sophisticated manner. In this context, this article reviews the various AI/ML algorithms employed in natural products-based drug discovery. Particular attention is paid to case studies employing AI tools in plant and microbial research. Challenges associated with the use of AI tools for natural products research have also been discussed. [ABSTRACT FROM AUTHOR]

Compound identification in complex mixtures by NMR and MS is best achieved through experimental databases (DB) mining. Experimental DB frequently show limitations regarding their completeness, availability or data quality, thus making predicted database of increasing common use. Querying large databases may lead to select unlikely structure candidates. Two approaches to dereplication are thus possible: filtering of a large DB before search or scoring of the results after a large scale search. The present work relies on the former approach. As far as we know, nmrshiftdb2 is the only open‐source 13NMR chemical shift predictor that can be freely operated in batch mode. CFM‐ID 4.0 is one of the best‐performing open‐source tools for ESI‐MS/MS spectra prediction. LOTUS is a freely usable and comprehensive collection of secondary metabolites. Integrating the open source database and software LOTUS, CFM‐ID, and nmrshiftdb2 in a dereplication workflow requires presently programming skills, owing to the diversity of data encoding and processing procedures. A graphical user interface that integrates seamlessly chemical structure collection, spectral data prediction and database building still does not exist, as far as we know. The present work proposes a stand–alone software tool that assists the identification of mixture components in a simple way. [ABSTRACT FROM AUTHOR]

In an ongoing project on the Iranian Helichrysum Mill. species (Asteraceae), the phytochemical profiles of the extracts obtained from the flower and leaves of Helichrysum oligocephalum were investigated utilizing ultra‐performance liquid chromatography–mass spectrometry (MS) coupled with quadrupole time of flight. In addition, the cytotoxic activity of the extracts was evaluated against different cancerous cell lines by AlamarBlue assay. The apoptotic effects of the fractions were measured by propidium iodide staining and flow cytometry. HPLC‐based activity profiling was used to track the cytotoxic constituents. The MS and MSn data were analyzed by MZmine3. Clustering and visualization of the MS data were established by an online platform FreeClust. Concentration‐dependent cytotoxicity was observed for dichloromethane fractions. The active constituents were annotated as pyrone and phloroglucinol derivatives by comparing their MS and MSn profiles to an in‐house library, available databases, Dictionary of Natural Products, and literature. Additionally, plausible MS fragmentation pathways were suggested for the compounds; this can help in the dereplication process of similar compounds from the genus. In conclusion, this plant is rich in valuable phloroglucinol and pyrone compounds and can be used as a source of active phytochemicals. However, further phytochemical and pharmacological studies are necessary. [ABSTRACT FROM AUTHOR]

Introduction: We developed Data Base similarity (DBsimilarity), a user‐friendly tool designed to organize structure databases into similarity networks, with the goal of facilitating the visualization of information primarily for natural product chemists who may not have coding experience. Method: DBsimilarity, written in Jupyter Notebooks, converts Structure Data File (SDF) files into Comma‐Separated Values (CSV) files, adds chemoinformatics data, constructs an MZMine custom database file and an NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities between compounds, and constructs CSV files formatted into similarity networks for Cytoscape. Results: The Lotus database was used as a source for Ginkgo biloba compounds, and DBsimilarity was used to create similarity networks including NPClassifier classification to indicate biosynthesis pathways. Subsequently, a database of validated antibiotics from natural products was combined with the G. biloba compounds to identify promising compounds. The presence of 11 compounds in both datasets points to possible antibiotic properties of G. biloba, and 122 compounds similar to these known antibiotics were highlighted. Next, DBsimilarity was used to filter the NPAtlas database (selecting only those with MIBiG reference) to identify potential antibacterial compounds using the ChEMBL database as a reference. It was possible to promptly identify five compounds found in both databases and 167 others worthy of further investigation. Conclusion: Chemical and biological properties are determined by molecular structures. DBsimilarity enables the creation of interactive similarity networks using Cytoscape. It is also in line with a recent review that highlights poor biological plausibility and unrealistic chromatographic behaviors as significant sources of errors in compound identification. Introducing DBsimilarity: a user‐friendly tool leveraging Jupyter Notebooks to organize structural databases into similarity networks. This innovative approach aids natural product chemists, even without coding skills, by converting Structure Data File (SDF) files to Comma‐Separated Values (CSV), facilitating dereplication, calculating compound similarities, and generating Cytoscape‐compatible networks. The tool's application is showcased through Ginkgo biloba compound analysis, uncovering potential antibiotic candidates via cross‐database correlations. Furthermore, DBsimilarity efficiently filters NPAtlas using ChEMBL as a reference, unveiling numerous antibacterial leads for exploration. [ABSTRACT FROM AUTHOR]

The quick identification of known organic low molecular weight compounds, also known as structural dereplication, is a highly important task in the chemical profiling of natural resource extracts. To that end, a method that relies on carbon‐13 nuclear magnetic resonance (NMR) spectroscopy, elaborated in earlier works of the author's research group, requires the availability of a dedicated database that establishes relationships between chemical structures, biological and chemical taxonomy, and spectroscopy. The construction of such a database, called acd_lotus, was reported earlier, and its usefulness was illustrated by only three examples. This article presents the results of structure searches carried out starting from 58 carbon‐13 NMR data sets recorded on compounds selected in the metabolomics section of the biological magnetic resonance bank (BMRB). Two compound retrieval methods were employed. The first one involves searching in the acd_lotus database using commercial software. The second one operates through the freely accessible web interface of the nmrshiftdb2 database, which includes the compounds present in acd_lotus and many others. The two structural dereplication methods have proved to be efficient and can be used together in a complementary way. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Despite considerable advances in medicine and technology, humanity still faces many deadly diseases such as cancer and malaria. In order to find appropriate treatments, the discovery of new bioactive substances is essential. Therefore, research is now turning to less frequently explored habitats with exceptional biodiversity such as the marine environment. Many studies have demonstrated the therapeutic potential of bioactive compounds from marine macro- and microorganisms. In this study, nine microbial strains isolated from an Indian Ocean sponge, Scopalina hapalia, were screened for their chemical potential. The isolates belong to different phyla, some of which are already known for their production of secondary metabolites, such as the actinobacteria. This article aims at describing the selection method used to identify the most promising microorganisms in the field of active metabolites production. The method is based on the combination of their biological and chemical screening, coupled with the use of bioinformatic tools. The dereplication of microbial extracts and the creation of a molecular network revealed the presence of known bioactive molecules such as staurosporin, erythromycin and chaetoglobosins. Molecular network exploration indicated the possible presence of novel compounds in clusters of interest. The biological activities targeted in the study were cytotoxicity against the HCT-116 and MDA-MB-231 cell lines and antiplasmodial activity against Plasmodium falciparum 3D7. Chaetomium globosum SH-123 and Salinispora arenicola SH-78 strains actually showed remarkable cytotoxic and antiplasmodial activities, while Micromonospora fluostatini SH-82 demonstrated promising antiplasmodial effects. The ranking of the microorganisms as a result of the different screening steps allowed the selection of a promising strain, Micromonospora fluostatini SH-82, as a premium candidate for the discovery of new drugs. [ABSTRACT FROM AUTHOR]

The structure elucidation of small organic molecules (<1500 Dalton) through 1D and 2D nuclear magnetic resonance (NMR) data analysis is a potentially challenging, combinatorial problem. This publication presents Sherlock, a free and open-source Computer-Assisted Structure Elucidation (CASE) software where the user controls the chain of elementary operations through a versatile graphical user interface, including spectral peak picking, addition of automatically or user-defined structure constraints, structure generation, ranking and display of the solutions. A set of forty-five compounds was selected in order to illustrate the new possibilities offered to organic chemists by Sherlock for improving the reliability and traceability of structure elucidation results. [ABSTRACT FROM AUTHOR]

Penicillium setosum represents a Penicillium species recently described, with little up-to-date information about its metabolic and biological potential. Due to this scenario, we performed chemical and biological studies of P. setosum CMLD18, a strain isolated from Swinglea glutinosa (Rutaceae). HRMS-MS guided dereplication strategies and anti-leukemia assays conducted the isolation and characterization of six compounds after several chromatographic procedures: 2-chloroemodic acid (2), 2-chloro-1,3,8-trihydroxy-6- (hydroxymethyl)-anthraquinone (7), 7-chloroemodin (8), bisdethiobis(methylthio)acetylaranotine (9), fellutanine C (10), and 4-methyl-5,6-diihydro-2H-pyran-2-one (15). From the assayed metabolites, (10) induced cellular death against Kasumi-1, a human leukemia cell line, as well as good selectivity for it, displaying promising cytotoxic activity. Here, the correct NMR signal assignments for (9) are also described. Therefore, this work highlights more detailed knowledge about the P. setosum chemical profile as well as its biological potential, offering prospects for obtaining natural products with anti-leukemia capabilities. [ABSTRACT FROM AUTHOR]

As a part of natural defense, plants initiate the secretion of gum containing numerous pharmacologically active essential metabolites. A fraction of such gum-resin from Araucaria cunninghamii Mudie, when screened against human cancer cell lines, was found to be active. Further, it was subjected to an LCMS-DNP (Dictionary of Natural Products) based dereplication study followed by a detailed phytochemical investigation to obtain pure metabolites. Also, the gum resin of A. cunninghamii was found to be a rich source of abietanes and labdanes. The LCMS-DNP-based dereplication study identified many known metabolites, which were isolated for the first time from this plant as well as a new labdane diterpenoid (9). The compounds were characterized via spectroscopic techniques, which were subsequently compared with the already existing literature data. The metabolites were screened against seven human cancer cell lines. The anticancer activity was further supported by molecular docking studies. [ABSTRACT FROM AUTHOR]

MOLECULAR NETWORKS: AN ANALYSIS ON ANNOTATIONS AND DISCOVERY OF NEW ASSETS. To speed up the discovery of bioactive natural products (NP), chemists have sought advanced approaches in analytical and computational chemistry in attempt to organize and extract information from large data sets. In this sense, the molecular networks (MN) successfully organized enormous sets of mass spectrometry (MS) data together with samples metadata information in an intuitive visualization in the spectral similarity networks format. GNPS (Global Natural Products Social Molecular Networking), a free online platform for storing and processing MSn data, is a leading application of spectral matching with public databases aimed at the dereplication and discovery of new bioactive products through molecular networks. In this review, we address the concept of GNPS spectral similarity networks, as well as their complementary computational tools, benefits and limitations applied in NP studies associated with dereplication, chemical ecology, functional genetics and determination of biosynthetic pathways.

Comprehensive Summary: The tribe Delphineae, comprising two genera, Aconitum L. and Delphinium L., is the richest source of bioactive diterpenoid alkaloids (DAs), while their highly similar chemical structures create a major challenge for dereplication from complex mixture. To solve the inefficient discovery process of natural DAs, a novel dereplication strategy based on pattern recognition of specific heteronuclear multiple bond correlation (HMBC) signals has been established in this study. This strategy utilized the diagnostic signals in the HMBC spectra of C19‐DAs, which could indicate the structural categories. The newly designed software MatchNat allowed in silico recognition of C19‐DA types. The developed dereplication system was demonstrated to be a practical strategy to dereplicate a variety of natural products from two Delphineae plants, including Aconitum vilmorinianum Kom. and Delphinium pachycentrum Hemsl. Known compounds could be rapidly recognized from the mixture, and novel DAs were found with the aid of MatchNat. Consequently, targeted isolation led to the explosive discovery of a series of novel DAs. This dereplication system has the potential to be adopted for dereplication of any other class of natural products and has shown promising application as a complementary tool in the nuclear magnetic resonance analysis of traditional Chinese medicines. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: The tribe Delphineae, comprising two genera, Aconitum L. and Delphinium L., is the richest source of bioactive diterpenoid alkaloids (DAs), while their highly similar chemical structures create a major challenge for dereplication from complex mixture. To solve the inefficient discovery process of natural DAs, a novel dereplication strategy based on pattern recognition of specific heteronuclear multiple bond correlation (HMBC) signals has been established in this study. This strategy utilized the diagnostic signals in the HMBC spectra of C19‐DAs, which could indicate the structural categories. The newly designed software MatchNat allowed in silico recognition of C19‐DA types. The developed dereplication system was demonstrated to be a practical strategy to dereplicate a variety of natural products from two Delphineae plants, including Aconitum vilmorinianum Kom. and Delphinium pachycentrum Hemsl. Known compounds could be rapidly recognized from the mixture, and novel DAs were found with the aid of MatchNat. Consequently, targeted isolation led to the explosive discovery of a series of novel DAs. This dereplication system has the potential to be adopted for dereplication of any other class of natural products and has shown promising application as a complementary tool in the nuclear magnetic resonance analysis of traditional Chinese medicines. [ABSTRACT FROM AUTHOR]

Complete structural elucidation of natural products is commonly performed by nuclear magnetic resonance spectroscopy (NMR), but annotating compounds to most likely structures using high-resolution tandem mass spectrometry is a faster and feasible first step. The CASMI contest 2016 (Critical Assessment of Small Molecule Identification) provided spectra of eighteen compounds for the best manual structure identification in the natural products category. High resolution precursor and tandem mass spectra (MS/MS) were available to characterize the compounds. We used the Seven Golden Rules, Sirius2 and MS-FINDER software for determination of molecular formulas, and then we queried the formulas in different natural product databases including DNP, UNPD, ChemSpider and REAXYS to obtain molecular structures. We used different in-silico fragmentation tools including CFM-ID, CSI:FingerID and MS-FINDER to rank these compounds. Additional neutral losses and product ion peaks were manually investigated. This manual and time consuming approach allowed for the correct dereplication of thirteen of the eighteen natural products.

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.

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.

Lack of new anti-cancer and anti-infective agents directed the pharmaceutical research to natural products' discovery especially from actinomycetes as one of the major sources of bioactive compounds. Metabolomics- and dereplication-guided approach has been used successfully in chemical profiling of bioactive actinomycetes. We aimed to study the metabolomic profile of five bioactive actinomycetes to investigate the interesting metabolites responsible for their antimicrobial and anti-cancer activities. Three actinomycetes, namely, Streptomyces sp. SH8, SH10 and SH13, were found to exhibit broad spectrum of antimicrobial activities, whereas isolate SH4 showed the broadest antimicrobial activity against all tested strains. In addition, isolates SH8, SH10 and SH12 displayed potent cytotoxicity against the breast cancer cell line Michigan Cancer Foundation-7 (MCF-7), whereas isolates SH4 and SH12 exhibited potent anti-cancer activity against the hepatoma cell line hepatoma G2 (HepG2) compared with their weak inhibitory properties on the normal breast cells MCF-10A and normal liver cells transformed human liver epithelial-2 (THLE2), respectively. All bioactive isolates were molecularly identified as Streptomyces sp. via 16S rRNA gene sequencing. Our actinobacterial dereplication analysis revealed putative identification of several bioactive metabolites including tetracycline, oxytetracycline and a macrolide antibiotic, novamethymycin. Together, chemical profiling of bioactive Streptomycetes via dereplication and metabolomics helped in assigning their unique metabolites and predicting the bioactive compounds instigating their diverse bioactivities.

Despite considerable advances in medicine and technology, humanity still faces many deadly diseases such as cancer and malaria. In order to find appropriate treatments, the discovery of new bioactive substances is essential. Therefore, research is now turning to less frequently explored habitats with exceptional biodiversity such as the marine environment. Many studies have demonstrated the therapeutic potential of bioactive compounds from marine macro- and microorganisms. In this study, nine microbial strains isolated from an Indian Ocean sponge, Scopalina hapalia, were screened for their chemical potential. The isolates belong to different phyla, some of which are already known for their production of secondary metabolites, such as the actinobacteria. This article aims at describing the selection method used to identify the most promising microorganisms in the field of active metabolites production. The method is based on the combination of their biological and chemical screening, coupled with the use of bioinformatic tools. The dereplication of microbial extracts and the creation of a molecular network revealed the presence of known bioactive molecules such as staurosporin, erythromycin and chaetoglobosins. Molecular network exploration indicated the possible presence of novel compounds in clusters of interest. The biological activities targeted in the study were cytotoxicity against the HCT-116 and MDA-MB-231 cell lines and antiplasmodial activity against Plasmodium falciparum 3D7. Chaetomium globosum SH-123 and Salinispora arenicola SH-78 strains actually showed remarkable cytotoxic and antiplasmodial activities, while Micromonospora fluostatini SH-82 demonstrated promising antiplasmodial effects. The ranking of the microorganisms as a result of the different screening steps allowed the selection of a promising strain, Micromonospora fluostatini SH-82, as a premium candidate for the discovery of new drugs.

Inga edulis is traditionally used as anti-inflammatory and antidiarrheal and has been investigated as potential sources of biologically active natural products. In this study, dereplication strategy using HPLC-SPE-TT, RP-HPLC-PDA and NMR spectroscopy was employed, and this resulted in the identification of sixteen compounds from the leaves extract of I. edulis, including four triterpenes (lupeol, α-amirin, olean-18-ene acid and frideline), three flavonoids, eight phenolic acids, an anthocyanin derived from delphinidin-3-glycoside and a mixture of five acylated anthocyanins. The chemical identification was performed based on NMR data, chemosystematics aspects, UV spectra and by comparison with the retention time and UV spectra of authentic standards. The metabolic profile of the species indicated the presence of phenolic compounds as major constituents justifying its strong antioxidant potential performed in β-carotene test. The techniques used have shown effective strategies for the early detection of active natural products from plant extracts, as these approaches are still crucially absent. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the possibility to use Baikal endemic amphipods as a source for screening of novel natural products. Analysis of amphipod’s E. verrucosus hemolymph by HPLC-MS was performed for the first time. A number of natural products with new molecular mass for Baikal endemic macroinvertebrates were found. We found a compound as chrysin by comparison of MS1, MS2 profiles and molecular mass of natural products registered in Dictionary of Natural Products and MZ-cloud databases. Also, we found new compound with mass 493.80277 Da, circulating in the amphipod’s hemolymph.

The aim of the study was to conduct a dereplication analysis of crude extract of deepwater Baikal’s endemic amphipod species, Ommatogammarus albinus. Detected masses provide some evidence that amphipod’s extract might contain both host and microbiota secondary metabolites. Some of the masses correspond with known structures isolated from eubacteria. Also, two compounds did not match with any registered natural products from the database Dictionary of Natural Products. Those findings allow suggesting that Baikal’s endemics and their microbiota are promising sources of novel natural products.

Natural products isolated by microorganisms are interesting in the search for new compounds with several biological activities. However, low concentration and structural diversity make the isolation a time-consuming step. Tandem mass spectrometry is a well-established technology for the identification and characterization of target microbial natural products due to high sensitivity and selectivity of these experiments. We developed a method employing neutral loss experiments (LC-ESI-MS/MS) to identify luminacins in microbial crude extracts. The luminacins class exhibited conserved fragmentation pattern with loss at 172 Da relative to glycosides fragment and this loss was used in searching for compounds belonging to this class. Therefore, the crude extract produced by Streptomyces sp. 39 PL was analysed and five luminacins were isolated - one is a novel luminacin I at 466 Da. [ABSTRACT FROM AUTHOR]

Introduction: Molecular networks are now established as the method of choice for tandem mass spectrometry dereplication and similarity‐based structure elucidation. Node identification can be used to start the propagation of the structure elucidation of unknown compounds progressively. Objective: To demonstrate the capabilities of using the LipidXplorer data results along with molecular networking to identify nodes and aid sequential structure elucidation of unknown compounds. Material and Methods: Molecular fragmentation query language (MFQL) files were written to identify glycoalkaloids based on known structures described for Solanum species. A dataset generated from liquid chromatography‐high resolution mass spectrometry (LC‐HRMS) analysis of Solanum pseudoquina sample were submitted to dereplication on both LipidXplorer software and Global Natural Products Social Molecular Network (GNPS) online system. The resulting attribute table from GNPS calculations was merged with the LipidXplorer results and this merged file was used for network visualisation in Cytoscape. Nodes in the molecular network were labelled using the LipidXplorer identifiers, thus assisting the structure elucidation of unidentified compounds. Results: The combination of the LipidXplorer glycoalkaloids list and GNPS analysis was used in Cytoscape to label nodes in the molecular network. The analysis of the network using these labelled starting points triggered the structure elucidation of closely related nodes leading to the identification of 30 compounds using the LipidXplorer output and four purified and structure elucidated compounds, including a new glycoalkaloids identified as 3‐O‐(β‐d‐xylopyranosyl)‐(20R,25S)‐22,26‐epimino‐16‐acetyl‐cholesta‐5,22(N)‐diene. Conclusion: A significant compound identification completely based on molecular formula and fragmentation queries was achieved. This new and effective approach could help researches to expand the identification rate of compounds in dereplication studies using molecular networks. To extend the compound identification rate for dereplication studies using molecular networking, we implemented the direct use of the data generated from the database independent method developed on LipidXplorer in the Global Natural Products Social Molecular Network (GNPS) table results. Features detected using the molecular formula restrictions translated into molecular fragmentation query language (MFQL) files were merged with the attribute table from GNPS to enable Cytoscape to plot the LipidXplorer identifiers as node labels. Thus, due to the flexibility of the MFQL files to annotate and classify compounds based on their molecular formula and fragmentation patterns together with the structure elucidation capability of molecular network we were able to significantly increase the putative compound identification. This application was employed to identify several glycoalkaloids from Solanum pseudoquina as well as a new glycoalkaloid identified as 3‐O‐(β‐d‐xylopyranosyl)‐(20R,25S)‐22,26‐epimino‐16‐acetyl‐cholesta‐5,22(N)‐diene. [ABSTRACT FROM AUTHOR]

Introduction: The conventional process of natural drug development involves the purification and isolation of bioactive compounds from natural extracts, followed by manual structure elucidation using spectral data (e.g., NMR, MS). Two major challenges in this process are: (1) isolating and identifying new bioactive compounds in a reasonable time and cost, and (2) producing compounds with desired activity and toxicity profiles. Dereplication, which utilizes existing data on previously isolated bioactive compounds to identify the presence/absence of new compounds with minimal human intervention, offers a solution. With advancements in computer technology and machine learning, there is an opportunity to develop a computational platform integrating spectral data and experimental data of natural substances. Methods: A new database was curated consisting of various existing databases with the option of integrating future in-house isolated compound data, specifically for dereplication. Chemical compounds in the database were represented as tokens/vectors generated by calculating various chemical descriptors. Machine learning models, including naive Bayes classifier, support vector machines, random forest, and convolutional neural networks, were developed for structure elucidation in the dereplication process. The performance of these models was evaluated using precision, recall, F1-score, and AUC of the ROC curve. Results: The developed machine learning models were able to predict compound structures from preliminary spectral measurements with a precision of 78%, recall of 67%, an F1-score of 0.72, and an AUC of 0.79. These results are promising and the machine learning models can still be optimized further with additional data. Conclusion: The integration of machine learning algorithms with a curated database offers a promising approach for automating the dereplication process and discovery of active compounds from natural materials, potentially accelerating natural drug development. Future potential directions include external validation of the developed machine learning models using manual structure elucidation of natural product isolates. [ABSTRACT FROM AUTHOR]

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.

The discovery of novel natural products (NPs) that will serve as lead structures has to be an ongoing effort to fill the respective development pipelines. However, identification of NPs, which possess a potential for application in e.g., the pharma or agro sector, must be as cost effective and fast as possible. Furthermore, the amount of sample available for initial testing is usually very limited, not least because of the fact that the impact on the environment, i.e., the sampled biosystem, should be kept minimal. Here, our pipeline SeaPEPR is described, in which a primary bioactivity screening of crude extracts is combined with the analysis of their metabolic fingerprint. This enabled prioritization of samples for subsequent microfractionation and dereplication of the active compounds early in the workflow. As a case study, 76 marine sponge-derived extracts were screened against a microbial screening panel. Thereunder, human pathogenic bacteria (Escherichia coli ATCC35218 and Staphylococcus aureus ATCC33592) and yeast (Candida albicans FH2173), as well as the phytopathogenic fungus Septoria tritici MUCL45407. Overall, nine extracts revealed activity against at least one test organism. Metabolic fingerprinting enabled assigning four active extracts into one metabolic group; therefore, one representative was selected for subsequent microfractionation. Dereplication of the active fractions showed a new dibrominated aplysinopsin and a hypothetical chromazonarol stereoisomer derivative. Furthermore, inhibitory activity against the common plant pest Septoria tritici was discovered for NPs of marine origin.

Ventilago denticulata is an herbal medicine for the treatment of wound infection; therefore this plant may rich in antibacterial agents. UHPLC-ESI-QTOF-MS/MS-Based molecular networking guided isolation and dereplication led to the identification of antibacterial and antifungal agents in V. denticulata. Nine antimicrobial agents in V. denticulata were isolated and characterized; they are divided into four groups including (I) flavonoid glycosides, rhamnazin 3-rhamninoside (7), catharticin or rhamnocitrin 3-rhamninoside (8), xanthorhamnin B or rhamnetin 3-rhamninoside (9), kaempferol 3-rhamninoside (10) and flavovilloside or quercetin 3-rhamninoside (11), (II) benzisochromanquinone, ventilatones B (12) and A (15), (III) a naphthopyrone ventilatone C (16) and (IV) a triterpene lupeol (13). Among the isolated compounds, ventilatone C (16) was a new compound. Moreover, kaempferol, chrysoeriol, isopimpinellin, rhamnetin, luteolin, emodin, rhamnocitrin, ventilagodenin A, rhamnazin and mukurozidiol, were tentatively identified as antimicrobial compounds in extracts of V. denticulata by a dereplication method. MS fragmentation of rhamnose-containing compounds gave an oxonium ion, C6H9O3+ at m/z 129, while that of galactose-containing glycosides provided the fragment ion at m/z 163 of C6H11O5+. These fragment ions may be used to confirm the presence of rhamnose or galactose in mass spectrometry-based analysis of natural glycosides or oligosaccharide attached to biomolecules, that is, glycoproteins.

Investigation of the chemical profile of the industrially important black filamentous fungus Aspergillus aculeatus by UHPLC-DAD-HRMS and subsequent dereplication has led to the discovery of several novel compounds. Isolation and extensive 1D and 2D NMR spectroscopic analyses allowed for structural elucidation of a dioxomorpholine, a unique okaramine, an aflavinine and three novel structures of mixed biosynthetic origin, which we have named aculenes A–C. Moreover, known analogues of calbistrins, okaramines and secalonic acids were detected. All novel compounds were tested for antifungal activity against Candida albicans, however all showed only weak or no activity. Aspergillus aculeatus IBT 21030 was additionally shown to be capable of producing sclerotia. Examination of the sclerotia revealed a highly regulated production of metabolites in these morphological structures.

The discovery of new chemical entities from natural sources has been historically driven by the biological activity observed in crude extracts or by the significant abundance of secondary metabolites, through a laborious workflow, frequently culminating on the re-isolation of known compounds. This high rate of rediscovery was one of the main contributing factors leading to the decreased interest in natural products from pharmaceutical companies and academic research groups until very recently. Largely driven by the technological advances in analytical instrumentation, mainly mass spectrometry (MS) and nuclear magnetic resonance (NMR), a renaissance on natural products research and associated drug discovery programs is being witnessed. The development of hybrid platforms combining the strengths of MS and NMR enables a faster and more accurate identification of known metabolites, allowing the prioritization of new natural products, not only with new chemical scaffolds but also frequently exhibiting relevant biological effects that may ultimately provide new lead drugs. Furthermore, the synergism resulting from the combination of both techniques allows almost unlimited access to the chemical space available formed by new natural sources. [ABSTRACT FROM AUTHOR]

The microbiome of three different sites at the Peruvian Pacific coast was analyzed, revealing a lower bacterial biodiversity at Isla Foca than at Paracas and Manglares, with 89 bacterial genera identified, as compared to 195 and 173 genera, respectively. Only 47 of the bacterial genera identified were common to all three sites. In order to obtain promising strains for the putative production of novel antimicrobials, predatory bacteria were isolated from these sampling sites, using two different bait organisms. Even though the proportion of predatory bacteria was only around 0.5% in the here investigated environmental microbiomes, by this approach in total 138 bacterial strains were isolated as axenic culture. 25% of strains showed antibacterial activity, thereby nine revealed activity against clinically relevant methicillin resistant Staphylococcus aureus (MRSA) and three against enterohemorrhagic Escherichia coli (EHEC) strains. Phylogeny and physiological characteristics of the active strains were investigated. First insights into the chemical basis of the antibacterial activity indicated the biosynthetic production of the known compounds ariakemicin, kocurin, naphthyridinomycin, pumilacidins, resistomycin, and surfactin. However, most compounds remained elusive until now. Hence, the obtained results implicate that the microbiome present at the various habitats at the Peruvian coastline is a promising source for heterotrophic bacterial strains showing high potential for the biotechnological production of antibiotics. [ABSTRACT FROM AUTHOR]