Dereplication Of Natural Products Research Articles

IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication.

Molecular networking has emerged as a standard approach for natural product (NP) discovery. However, the current pipeline based on molecular networks tends to prioritize larger clusters comprising multiple nodes. To address this issue, we present the integrated molecular networking workflow for NP dereplication (IMN4NPD). This approach not only expedites the rapid dereplication of extensive clusters within the molecular network but also places specific emphasis on self-looped or pairs of nodes, which are often overlooked by the current methods. By amalgamating the outputs from various computational tools, we efficiently dereplicate compounds falling into specific categories and provide annotations for both large cluster nodes and self-looped or pair of nodes within the molecular network. Furthermore, we have incorporated several fundamentally distinct similarity algorithms, namely, Spec2Vec and MS2DeepScore, for constructing the t-SNE network. Through comparison with modified cosine similarity, we have observed that integrating additional diverse spectral similarity measures, the resulting t-SNE network enhanced the ability to dereplicate NPs. Demonstrating the use case of an ethanol extract of Plumula nelumbinis, we illustrate that an integration of multiple computational solutions with IMN4NPD aids the dereplication, especially self-looped nodes, and in the discovery of novel compounds in NPs.

The Effects of a High Concentration of Dissolved Oxygen on Actinobacteria from Lake Baikal.

Among the diversity of microorganisms, the rarest and least explored are microorganisms that live in conditions of high oxygen in the environment and can experience the effects of natural oxidative stress. Here we suggest that the actinobacteria of Lake Baikal, sampled in the littoral zone, may produce natural products with antioxidant activity. The current study aimed to assess the effects of experimentally increased amounts of oxygen and ozone on the morphology of actinobacteria, DNA mutations, and antioxidant potential. In this experiment, we cultivated actinobacteria in liquid culture under conditions of natural aeration and increased concentrations of dissolved oxygen and ozone. Over a period of three months, bacterial samples were collected every week for further analysis. Morphological changes were assessed using the Gram method. A search for DNA mutations was conducted for the highly conserved 16S rRNA gene. The evaluation of antioxidant activity was performed using the DPPH test. The biotechnological potential was evaluated using high-resolution liquid chromatography-mass spectrometry approaches supplemented with the dereplication of natural products. We demonstrated the synthesis of at least five natural products by the Streptomyces sp. strain only under conditions of increased oxygen and ozone levels. Additionally, we showed morphological changes in Streptomyces sp. and nucleotide mutations in Rhodococcus sp. exposed to increased concentrations of dissolved oxygen and oxidative stress. Consequently, we demonstrated that an increased concentration of oxygen can influence Lake Baikal actinobacteria.

MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

Easy Structural Dereplication of Natural Products by Means of Predicted Carbon‐13 Nuclear Magnetic Resonance Spectroscopy Data**

AbstractThe present article reports the creation and usage of a general natural product database for the structural dereplication of natural products. This database, acd_lotusv7, derives from the LOTUS natural products database as the sole source of chemical structures. Database construction also relies on the commercial “ACD/C+H Predictors and DB” software for the prediction of the carbon‐13 nuclear magnetic resonance (NMR) spectral data associated with structures. The linkage of each natural compound with a Wikidata resource identifier already present in LOTUS accelerates the access to the primary literature data such as biologic origin and bibliographic references. The open source nmrshiftdb2 web interface and search engine provide a simple and free way to retrieve compound structures stored in acd_lotusv7 from carbon‐13 data and to analyze search results. Dereplication is illustrated by the easy and free retrieval of the structure of three natural compounds of low, medium, and high complexity from published lists of carbon‐13 NMR chemical shifts.

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.

NP-MRD: the Natural Products Magnetic Resonance Database.

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

Burkholderia Bacteria Produce Multiple Potentially Novel Molecules that Inhibit Carbapenem-Resistant Gram-Negative Bacterial Pathogens

Antimicrobial resistance in Gram-negative pathogens represents a global threat to human health. This study determines the antimicrobial potential of a taxonomically and geographically diverse collection of 263 Burkholderia (sensu lato) isolates and applies natural product dereplication strategies to identify potentially novel molecules. Antimicrobial activity is almost exclusively present in Burkholderia sensu stricto bacteria and rarely observed in the novel genera Paraburkholderia, Caballeronia, Robbsia, Trinickia, and Mycetohabitans. Fourteen isolates show a unique spectrum of antimicrobial activity and inhibited carbapenem-resistant Gram-negative bacterial pathogens. Dereplication of the molecules present in crude spent agar extracts identifies 42 specialized metabolites, 19 of which represented potentially novel molecules. The known identified Burkholderia metabolites include toxoflavin, reumycin, pyrrolnitrin, enacyloxin, bactobolin, cepacidin, ditropolonyl sulfide, and antibiotics BN-227-F and SF 2420B, as well as the siderophores ornibactin, pyochelin, and cepabactin. Following semipreparative fractionation and activity testing, a total of five potentially novel molecules are detected in active fractions. Given the molecular formula and UV spectrum, two of those putative novel molecules are likely related to bactobolins, and another is likely related to enacyloxins. The results from this study confirm and extend the observation that Burkholderia bacteria present exciting opportunities for the discovery of potentially novel bioactive molecules.

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

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

Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY).

Diffusion-ordered NMR spectroscopy (DOSY) can be used to analyze mixtures of compounds since resonances deriving from different compounds are distinguished by their diffusion coefficients (D). Previously, DOSY has mostly been used for organometallic and polymer analysis, we have now applied DOSY to investigate diffusion coefficients of structurally diverse organic compounds such as natural products (NP). The experimental Ds derived from 55 diverse NPs has allowed us to establish a power law relationship between D and molecular weight (MW) and therefore predict MW from experimental D. We have shown that D is also affected by factors such as hydrogen bonding, molar density and molecular shape of the compound and we have generated new models that incorporate experimentally derived variables for these factors so that more accurate predictions of MW can be calculated from experimental D. The recognition that multiple physicochemical properties affect D has allowed us to generate a polynomial equation based on multiple linear regression analysis of eight calculated physicochemical properties from 63 compounds to accurately correlate predicted D with experimental D for any known organic compound. This equation has been used to calculate predicted D for 217 043 compounds present in a publicly available natural product database (DEREP-NP) and to dereplicate known NPs in a mixture based on matching of experimental D and structural features derived from NMR analysis with predicted D and calculated structural features in the database. These models have been validated by the dereplication of a mixture of two known sesquiterpenes obtained from Tasmannia xerophila and the identification of new alkaloids from the bryozoan Amathia lamourouxi. These new methodologies allow the MW of compounds in mixtures to be predicted without the need for MS analysis, the dereplication of known compounds and identification of new compounds based solely on parameters derived by DOSY NMR.

Cheminformatics in Natural Product-based Drug Discovery.

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product‐based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product‐likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products‐inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule‐based, similarity‐based, shape‐based, pharmacophore‐based and network‐based approaches, docking and machine learning methods.

A high-throughput method for dereplication and assessment of metabolite distribution in Salvia species using LC-MS/MS

Dereplication of crude plant extracts through liquid chromatography-mass spectrometry is a powerful technique for the discovery of novel natural products. Unfortunately, this technique is often plagued by a low level of confidence in natural product identification. This is mainly due to the lack of extensive chromatographic and mass spectrometric optimizations that result in improper and incomplete MS/MS fragmentation data. This study proposes a solution to this problem by the optimization of chromatographic separation and mass spectrometry parameters. We report herein a direct and high-throughput strategy for natural product dereplication in five Salvia species using high-resolution ESI-QTOF-MS/MS data. In the present study, we were able to identify a total of forty-seven natural products in crude extracts of five Salvia species using MS/MS fragmentation data. In addition to dereplication of Salvia species, quantitative profiling of twenty-one bioactive constituents of the genus was also performed on an ion trap mass spectrometer. For the quantitation study, method development focused on chromatographic optimizations to achieve maximum sensitivity. The developed dereplication and quantitation strategy can be extended to develop comprehensive metabolic profiles of other plant genera and species and thus can prove useful in the field of drug discovery from plants.

Ion Mobility Mass Spectrometry as an Efficient Tool for Identification of Streptorubin B in Streptomyces coelicolor M145.

Ion mobility spectrometry was utilized to corroborate the identity of streptorubin B (2) as the natural product produced by Streptomyces coelicolor. Natural product 2 was initially assigned as butylcycloheptylprodigiosin (3), and only relatively recently was this assignment clarified. We present additional evidence of this assignment by comparing collisional cross sections (Ω) of synthetic standards of 2, 3, and metacycloprodigiosin (4) to the cyclic prodiginine produced by S. coelicolor. Calculated theoretical Ω values demonstrate that cyclic prodiginines could be identified without standards. This work highlights ion mobility as an efficient tool for the dereplication of natural products.

Dereplication of Natural Products with Antimicrobial and Anticancer Activity from Brazilian Cyanobacteria.

Cyanobacteria are photosynthetic organisms that produce a large diversity of natural products with interesting bioactivities for biotechnological and pharmaceutical applications. Cyanobacterial extracts exhibit toxicity towards other microorganisms and cancer cells and, therefore, represent a source of potentially novel natural products for drug discovery. We tested 62 cyanobacterial strains isolated from various Brazilian biomes for antileukemic and antimicrobial activities. Extracts from 39 strains induced selective apoptosis in acute myeloid leukemia (AML) cancer cell lines. Five of these extracts also exhibited antifungal and antibacterial activities. Chemical and dereplication analyses revealed the production of nine known natural products. Natural products possibly responsible for the observed bioactivities and five unknown, chemically related chlorinated compounds present only in Brazilian cyanobacteria were illustrated in a molecular network. Our results provide new information on the vast biosynthetic potential of cyanobacteria isolated from Brazilian environments.

Combining untargeted and targeted metabolomics approaches for the standardization of polyherbal formulations through UPLC-MS/MS.

Polyherbal formulations are an integral part of various indigenous medicinal systems such as Traditional Chinese Medicine (TCM) and Ayurveda. The presence of a very large number of compounds makes the quality control of polyherbal formulations very difficult. To overcome this problem, we have developed a comprehensive strategy for the dereplication of natural products in polyherbal formulations by using Adhatoda vasica as a case study. The strategy is based on five major steps: the collection of plant samples from different locations to observe the effects of environmental variables; LC-ESI-MS/MS-based untargeted metabolite profiling of the plant samples to identify marker compounds using extensive chemometric analysis of the obtained data; the identification of marker compounds in polyherbal products; the isolation, purification and characterization of the marker compounds; and MRM-based quantitative analysis of the isolated marker compounds using LC-ESI-MS/MS. Using this strategy, we identified a total of 51 compounds in the methanolic extract of A. vasica plants from 14 accessions. Chemical fingerprinting of the plant led to the identification of characteristic peaks that were used to confirm the presence of A. vasica in complex polyherbal formulations. Four quinazoline alkaloids (marker compounds) were isolated, purified and quantified in various herbal formulations containing A. vasica. This method demonstrates a comprehensive strategy based on untargeted and targeted metabolite analysis that can be used for the standardization of complex polyherbal formulations.

Targeted Isolation of Neuroprotective Dicoumaroyl Neolignans and Lignans from Sageretia theezans Using in Silico Molecular Network Annotation Propagation-Based Dereplication.

The integration of LC-MS/MS molecular networking and in silico MS/MS fragmentation is an emerging method for dereplication of natural products. In the present study, a targeted isolation of natural products using a new in silico-based annotation tool named Network Annotation Propagation (NAP) is described. NAP improves accuracy of in silico fragmentation analyses by reranking candidate structures based on the network topology from MS/MS-based molecular networking. Annotation for the MS/MS spectral network of the Sageratia theezans twig extract was performed using NAP, and most molecular families within the network, including the known triterpenoids 1-7, could be putatively annotated, without relying on any previous reports of molecules from this species. Based on the in silico dereplication results, molecules were prioritized for isolation. In total, six dicoumaroyl 8- O-4' neolignans (8-13) and three dicoumaroyl lignans (14-16) were isolated from the twigs of S. theezans and structurally characterized by spectroscopic analyses. Isolates were evaluated for their neuroprotective activity, and compounds 14-16 showed potent protective effects against glutamate-induced oxidative stress in mouse HT22 cells at a concentration of 12.5 μM.

Dereplication of natural products from complex extracts by regression analysis and molecular networking: case study of redox-active compounds from Viola alba subsp. dehnhardtii

Introduction : In natural product research, bioassay-guided fractionation was previously widely employed but is now judged to be inadequate in terms of time and cost, particularly if only known compounds are ultimately isolated. The development of metabolomics, along with improvements in analytical tools, allows comprehensive metabolite profiling. This enables dereplication to target unknown active compounds early in the purification workflow. Objectives :Starting from an ethanolic extract of violet leaves, this study aims to predict redox active compounds within a complex matrix through an untargeted metabolomics approach and correlation analysis. Methods : Rapid fractionation of crude extracts was carried out followed by multivariate data analysis (MVA) of liquid chromatography–high resolution mass spectrometry (LC–HRMS) profiles. In parallel, redox active properties were evaluated by the capacity of the molecules to reduce 2,2-diphenyl-1-picrylhydrazyl (DPPH·) and superoxide (O2 ·−) radicals using UV–Vis and electron spin resonance spectroscopies (ESR), respectively. A spectral similarity network (molecular networking) was used to highlight clusters involved in the observed redox activities. Results : Dereplication on Viola alba subsp. dehnhardtii highlighted a reproducible pool of redox active molecules. Polyphenols, particularly O-glycosylated coumarins and C-glycosylated flavonoids, were identified and de novo dereplicated through molecular networking. Confirmatory analyses were undertaken by thin layer chromatography (TLC)–DPPH–MS assays and nuclear magnetic resonance (NMR) spectra of the most active compounds. Conclusion : Our dereplication strategy allowed the screening of leaf extracts to highlight new biologically active metabolites in few steps with a limited amount of crude material and reduced time-consuming manipulations. This approach could be applied to any kind of natural extract for the study of various biological activities.

13C NMR-based dereplication of natural products: New opportunities for reducing isolation efforts

13C NMR-based dereplication of natural products: New opportunities for reducing isolation efforts

Integration of molecular networking and in-silico MS/MS fragmentation for sensitive high throughput natural products dereplication

Integration of molecular networking and in-silico MS/MS fragmentation for sensitive high throughput natural products dereplication

Dereplication of Natural Products Using GC-TOF Mass Spectrometry: Improved Metabolite Identification by Spectral Deconvolution Ratio Analysis.

Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, thereby avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential, and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication was initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts.

Erratum for: Combined LC/UV/MS and NMR Strategies for the Dereplication of Marine Natural Products.

Drug discovery from marine natural products has experienced a revival since the beginning of this century. To be successful in this field, rapid dereplication (identification of already known bioactive compounds) is essential in order to assess the chemical novelty of crude extracts and their fractions. Access to the appropriate state-of-the-art analytical instrumentation and to suitable databases is a fundamental requirement in such a task. A brief survey of the most robust LC/UV/MS- and NMR-based approaches employed for marine natural product dereplication is presented alongside a description of the procedures followed to achieve this goal in our research group.