Database for Rapid Dereplication of Known Natural Products Using Data from MS and Fast NMR Experiments

Plant antibacterials: The challenges and opportunities

Nature possesses an inexhaustible reservoir of agents that could serve as alternatives to combat the growing threat of antimicrobial resistance (AMR). While some of the most effective drugs for treating bacterial infections originate from natural sources, they have predominantly been derived from fungal and bacterial species. However, a substantial body of literature is available on the promising antibacterial properties of plant-derived compounds. In this comprehensive review, we address the major challenges associated with the discovery and development of plant-derived antimicrobial compounds, which have acted as obstacles preventing their clinical use. These challenges encompass limited sourcing, the risk of agent rediscovery, suboptimal drug metabolism, and pharmacokinetics (DMPK) properties, as well as a lack of knowledge regarding molecular targets and mechanisms of action, among other pertinent issues. Our review underscores the significance of these challenges and their implications in the quest for the discovery and development of effective plant-derived antimicrobial agents. Through a critical examination of the current state of research, we give valuable insights that will advance our understanding of these classes of compounds, offering potential solutions to the global crisis of AMR. © 2017 Elsevier Inc. All rights reserved.

Anti-diabetic and anti-inflammatory bioactive hits from Coriaria intermedia Matsum. stem and Dracontomelon dao (Blanco) Merr. & Rolfe bark through bioassay-guided fractionation and liquid chromatography-tandem mass spectrometry

Women have been found to be at a higher risk of morbidity and mortality from type 2 diabetes mellitus (T2DM) and asthma. α-Glucosidase inhibitors have been used to treat T2DM, and arachidonic acid 15-lipoxygenase (ALOX15) inhibitors have been suggested to be used as treatments for asthma and T2DM. Compounds that inhibit both enzymes may be studied as potential treatments for people with both T2DM and asthma. This study aimed to determine potential anti-diabetic and anti-inflammatory bioactive hits from Coriaria intermedia Matsum. stem and Dracontomelon dao (Blanco) Merr. & Rolfe bark. A bioassay-guided fractionation framework was used to generate bioactive fractions from C. intermedia stem and D. dao bark. Subsequently, dereplication through ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and database searching was performed to putatively identify the components of one bioactive fraction from each plant. Seven compounds were putatively identified from the C. intermedia stem active fraction, and six of these compounds were putatively identified from this plant for the first time. Nine compounds were putatively identified from the D. dao bark active fraction, and seven of these compounds were putatively identified from this plant for the first time. One putative compound from the C. intermedia stem active fraction (corilagin) has been previously reported to have inhibitory activity against both α-glucosidase and 15-lipoxygenase-1. It is suggested that further studies on the potential of corilagin as an anti-diabetic and anti-inflammatory treatment should be pursued based on its several beneficial pharmacological activities and its low reported toxicity.

Accelerating the discovery of alkyl halide-derived natural products using halide depletion

Even in the genomic era, microbial natural product discovery workflows can be laborious and limited in their ability to target molecules with specific structural features. Here we leverage an understanding of biosynthesis to develop a workflow that targets the discovery of alkyl halide-derived natural products by depleting halide anions, a key biosynthetic substrate for enzymatic halogenation, from microbial growth media. By comparing the metabolomes of bacterial cultures grown in halide-replete and deficient media, we rapidly discovered the nostochlorosides, the products of an orphan halogenase-encoding gene cluster from Nostoc punctiforme ATCC 29133. We further found that these products, a family of unusual chlorinated glycolipids featuring the rare sugar gulose, are polymerized via an unprecedented enzymatic etherification reaction. Together, our results highlight the power of leveraging an understanding of biosynthetic logic to streamline natural product discovery.

Marine indole alkaloid diversity and bioactivity. What do we know and what are we missing?

Covering: marine indole alkaloids (n = 2048) and their reported bioactivities up to the end of 2021Despite increasing numbers of marine natural products (MNPs) reported each year, most have only been examined for cytotoxic, antibacterial, and/or antifungal biological activities with the majority found to be inactive in these assays. In this context, why are natural products continuing to be examined in assays they are unlikely to show significant activity in, and what targets might be more useful for expanding knowledge of their biologically relevant chemical space? We have undertaken a meta-analysis of the biological activities for 2048 marine indole alkaloids (MIAs), a diverse sub-class of MNPs reported up to the end of 2021, and this has highlighted that the bioactivity potentials for up to 86% of published MIAs remains underexplored and/or undefined. Although most published MIAs are not cytotoxic or antimicrobial, there is a continued focus on using these assays to evaluate new structurally related analogues. Using cheminformatics analyses, the chemical diversity of the 2048 MIAs were clustered using fragment based fingerprints and their reported bioactivity potency towards specific disease targets was assessed for structure activity trends. These analyses showed that there are groups of MIAs that possess potent and diverse activities and that many analogues, previously tested only in cellular toxicity assays, could be better exploited to generate structure activity relationships associated with leads to treat emerging diseases. A collection of indole drug and drug-lead structures from non-natural sources were also incorporated into the dataset providing complementary bioactivity profiles that were further used to predict underexplored areas of potential new activity and to better direct future testing of MIAs. Our findings clearly suggest the biological evaluation of MIAs continues to be conducted on a narrow range of bioassays and disease targets, and that shifting the focus to non-toxic disease targets should provide expanded knowledge of biologically relevant chemical space aimed at maximising the potential of MIAs for drug discovery.

Strategies for the discovery of potential anticancer agents from plants collected from Southeast Asian tropical rainforests as a case study

Covering up to early 2023The present review summarizes recent accomplishments made as part of a multidisciplinary, multi-institutional anticancer drug discovery project, wherein samples comprising higher plants were collected primarily from Southeast Asia, and also from Central America, and the West Indies. In the introductory paragraphs, a short perspective is provided on the current importance of plants in the discovery of cancer therapeutic agents, and the contributions of other groups working towards this objective are mentioned. For our own investigations, following their collection, tropical plants have been subjected to solvent extraction and biological evaluation for their antitumor potential. Several examples of purified plant lead bioactive compounds were obtained and characterized, and found to exhibit diverse structures, including those of the alkaloid, cardiac glycoside, coumarin, cucurbitacin, cyclobenzofuran (rocaglate), flavonoid, lignan, and terpenoid types. In order to maximize the efficiency of work on drug discovery from tropical plant species, strategies to optimize various research components have been developed, including those for the plant collections and taxonomic identification, in accordance with the requirements of contemporary international treaties and with a focus on species conservation. A major component of this aspect of the work is the development of collaborative research agreements with representatives of the source countries of tropical rainforest plants. The phytochemical aspects have included the preparation of plant extracts for initial screening and the selection of promising extracts for activity-guided fractionation. In an attempt to facilitate this process, a TOCSY-based NMR procedure has been applied for the determination of bioactive rocaglate derivatives in samples of Aglaia species (Meliaceae) collected for the project. Preliminary in vitro and in vivo mechanistic studies carried out by the authors are described for two tropical plant-derived bioactive lead compounds, corchorusoside C and (+)-betulin, including work conducted with a zebrafish (Danio rerio) model. In the concluding remarks, a number of lessons are summarized that our group has learned as a result of working on anticancer drug discovery using tropical plants, which we hope will be of interest to future workers.

Applications of machine learning in microbial natural product drug discovery

INTRODUCTION

Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development.

AREAS COVERED

This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery.

EXPERT OPINION

Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.

Fungal Endophytes: Microfactories of Novel Bioactive Compounds with Therapeutic Interventions; A Comprehensive Review on the Biotechnological Developments in the Field of Fungal Endophytic Biology over the Last Decade

The seminal discovery of paclitaxel from endophytic fungus Taxomyces andreanae was a milestone in recognizing the immense potential of endophytic fungi as prolific producers of bioactive secondary metabolites of use in medicine, agriculture, and food industries. Following the discovery of paclitaxel, the research community has intensified efforts to harness endophytic fungi as putative producers of lead molecules with anticancer, anti-inflammatory, antimicrobial, antioxidant, cardio-protective, and immunomodulatory properties. Endophytic fungi have been a valuable source of bioactive compounds over the last three decades. Compounds such as taxol, podophyllotoxin, huperzine, camptothecin, and resveratrol have been effectively isolated and characterized after extraction from endophytic fungi. These findings have expanded the applications of endophytic fungi in medicine and related fields. In the present review, we systematically compile and analyze several important compounds derived from endophytic fungi, encompassing the period from 2011 to 2022. Our systematic approach focuses on elucidating the origins of endophytic fungi, exploring the structural diversity and biological activities exhibited by these compounds, and giving special emphasis to the pharmacological activities and mechanism of action of certain compounds. We highlight the tremendous potential of endophytic fungi as alternate sources of bioactive metabolites, with implications for combating major global diseases. This underscores the significant role that fungi can play in the discovery and development of novel therapeutic agents that address the challenges posed by prevalent diseases worldwide.

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.

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.

Fighting antibiotic resistance-strategies and (pre)clinical developments to find new antibacterials

Antibacterial resistance is one of the greatest threats to human health. The development of new therapeutics against bacterial pathogens has slowed drastically since the approvals of the first antibiotics in the early and mid-20^th century. Most of the currently investigated drug leads are modifications of approved antibacterials, many of which are derived from natural products. In this review, we highlight the challenges, advancements and current standing of the clinical and preclinical antibacterial research pipeline. Additionally, we present novel strategies for rejuvenating the discovery process and advocate for renewed and enthusiastic investment in the antibacterial discovery pipeline.

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.

Facile Detection of Light-Controlled Radical Scavengers from Natural Products Using In Situ UV-LED NMR Spectroscopy

With the recent development of chemical analysis technology, attention has been placed on natural light-sensitive compounds that exhibit photoreactivity to expand the structural diversity of natural product chemistry. Photochemical reactions that proceed via a free radical mechanism could be used to modulate the radical-scavenging ability of natural products as well as involve structural change. As the health benefits of radicals are also presented, there is a need for a controllable radical scavenging method for topical and selective application. In this study, we developed a novel acquisition and processing method to identify light-controlled radical scavengers in plant extracts and evaluate their antioxidant activity under light irradiation based on in situ UV-LED NMR spectroscopy. Using the developed method, licochalcones A and B, in which the trans and cis isomers undergo reversible photoisomerization, were selectively identified from licorice root extract, and their light-induced free radical scavenging activity was confirmed.

Rapid Profiling of Metabolites Combined with Network Pharmacology to Explore the Potential Mechanism of Sanguisorba officinalis L. against Thrombocytopenia

Sanguisorba officinalis L. (SO), a well-known herbal medicine, has been proven to show effect against thrombocytopenia. However, metabolites of SO in vivo are still unclear, and the underlying mechanism of SO against thrombocytopenia from the aspect of metabolites have not been well elucidated. In this study, an improved analytical method combined with UHPLC-QTOF MS and a molecular network was developed for the rapid characterization of metabolites in vivo based on fragmentation patterns. Then, network pharmacology (NP) was used to elucidate the potential mechanism of SO against thrombocytopenia. As a result, a total of 1678 exogenous metabolites were detected in urine, feces, plasma, and bone marrow, in which 104 metabolites were tentatively characterized. These characterized metabolites that originated from plasma, urine, and feces were then imported to the NP analysis. The results showed that the metabolites from plasma, urine, and feces could be responsible for the pharmacological activity against thrombocytopenia by regulating the PI3K-Akt, MAPK, JAK-STAT, VEGF, chemokine, actin cytoskeleton, HIF-1, and pluripotency of stem cells. This study provides a rapid method for metabolite characterization and a new perspective of underlying mechanism study from the aspect of active metabolites in vivo.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Advancements in capturing and mining mass spectrometry data are transforming natural products research

Covering: 2016 up to 2021Mass spectrometry (MS) is an essential technology in natural products research with MS fragmentation (MS/MS) approaches becoming a key tool. Recent advancements in MS yield dense metabolomics datasets which have been, conventionally, used by individual labs for individual projects; however, a shift is brewing. The movement towards open MS data (and other structural characterization data) and accessible data mining tools is emerging in natural products research. Over the past 5 years, this movement has rapidly expanded and evolved with no slowdown in sight; the capabilities of today vastly exceed those of 5 years ago. Herein, we address the analysis of individual datasets, a situation we are calling the '2021 status quo', and the emergent framework to systematically capture sample information (metadata) and perform repository-scale analyses. We evaluate public data deposition, discuss the challenges of working in the repository scale, highlight the challenges of metadata capture and provide illustrative examples of the power of utilizing repository data and the tools that enable it. We conclude that the advancements in MS data collection must be met with advancements in how we utilize data; therefore, we argue that open data and data mining is the next evolution in obtaining the maximum potential in natural products research.

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.

Marine Biodiscovery in a Changing World

The term "marine biodiscovery" has been recently been adopted to describe the area of marine natural products dedicated to the search of new drugs. Several maritime countries such as Australia, New Zealand, South Korea, and Japan as well as some European countries have invested significantly in this area of research over the last 50 years. In the late 2000s, research in this field has received significant interest and support in Ireland for exploring new marine bioresources from the nutrient-rich waters of the Northeastern Atlantic Ocean. Despite undeniable success exemplified by the marketing of new drugs, especially in oncology, the integration of new technical but also environmental aspects should be considered. Indeed, global change, particularly in our oceans, such as climate change, biodiversity loss, and the emergence of microbial pathogens, not only affects the environment but ultimately contributes to social inequalities. In this contribution, new avenues and best practices are proposed, such as the development of biorepositories and shared data for the future of marine biodiscovery research. The extension of this type of scientific work will allow humanity to finally make the optimum use of marine bioresources.

Towards the sustainable discovery and development of new antibiotics

An ever-increasing demand for novel antimicrobials to treat life-threatening infections caused by the global spread of multidrug-resistant bacterial pathogens stands in stark contrast to the current level of investment in their development, particularly in the fields of natural-product-derived and synthetic small molecules. New agents displaying innovative chemistry and modes of action are desperately needed worldwide to tackle the public health menace posed by antimicrobial resistance. Here, our consortium presents a strategic blueprint to substantially improve our ability to discover and develop new antibiotics. We propose both short-term and long-term solutions to overcome the most urgent limitations in the various sectors of research and funding, aiming to bridge the gap between academic, industrial and political stakeholders, and to unite interdisciplinary expertise in order to efficiently fuel the translational pipeline for the benefit of future generations.

Antimicrobial resistance is an increasing threat to public health and encouraging the development of new antimicrobials is one of the most important ways to address the problem. This Roadmap article aims to bring together industrial, academic and political partners, and proposes both short-term and long-term solutions to this challenge.

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.

We report accurate DOSY NMR based molecular weight and diffusion coefficient prediction tools. These tools can be used to dereplicate known natural products from databases using structurally rich NMR data as a surrogate for mass spectrometric data.

Authentication of saffron spice accessions from its common substitutes via a multiplex approach of UV/VIS fingerprints and UPLC/MS using molecular networking and chemometrics

Saffron is a spice revered for its unique flavor and health attributes often subjected to fraudulence. In this study, molecular networking as a visualization tool for UPLC/MS dataset of saffron and its common substitutes i.e. safflower and calendula (n = 21) was employed for determining genuineness of saffron and detecting its common substitutes i.e. safflower and calendula. Saffron was abundant in flavonol-O-glycosides and crocetin esters versus richness of flavanones/chalcones glycosides in safflower and cinnamates/terpenes in calendula. OPLS-DA identified differences in UPLC/MS profiles of different saffron accessions where oxo-hydroxy-undecenoic acid-O-hexoside was posed as saffron authentication marker and aided in discrimination between Spanish saffron of high quality from its inferior grade i.e. Iranian saffron along with crocetin di-O-gentiobiosyl ester and kaempferol-O-sophoroside. Kaempferol-O-neohesperidoside and N,N,N,-p-coumaroyl spermidine were characteristic safflower metabolites, whereas, calendulaglycoside C and di-O-caffeoyl quinic acid were unique to calendula. UV/VIS fingerprint spectral regions of picrocrocin (230-260 nm) and crocin derivatives (400-470 nm) were posed as being discriminatory of saffron authenticity and suggestive it can replace UPLC/MS in saffrom quality determination.

The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder in elderly people. The personalized diagnosis and treatment remain challenges all over the world. In recent years, natural products are becoming potential therapies for many complex diseases due to their stability and low drug resistance. With the development of informatics technologies, data-driven natural product discovery and healthcare is becoming reality. For PD, however, the relevant research and tools for natural products are quite limited. Here in this review, we summarize current available databases, tools, and models for general natural product discovery and synthesis. These useful resources could be used and integrated for future PD-specific natural product investigations. At the same time, the challenges and opportunities for future natural-product-based PD care will also be discussed.

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.

Molecular Networking Leveraging the Secondary Metabolomes Space of Halophila stipulaceae (Forsk.) Aschers. and Thalassia hemprichii (Ehrenb. ex Solms) Asch. in Tandem with Their Chemosystematics and Antidiabetic Potentials

The Red Sea is one of the most biodiverse aquatic ecosystems. Notably, seagrasses possess a crucial ecological significance. Among them are the two taxa Halophila stipulacea (Forsk.) Aschers., and Thalassia hemprichii (Ehrenb. ex Solms) Asch., which were formally ranked together with the genus Enhalus in three separate families. Nevertheless, they have been recently classified as three subfamilies within Hydrocharitaceae. The interest of this study is to explore their metabolic profiles through ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS/MS) analysis in synergism with molecular networking and to assess their chemosystematics relationship. A total of 144 metabolites were annotated, encompassing phenolic acids, flavonoids, terpenoids, and lipids. Furthermore, three new phenolic acids; methoxy benzoic acid-O-sulphate (16), O-caffeoyl-O-hydroxyl dimethoxy benzoyl tartaric acid (26), dimethoxy benzoic acid-O-sulphate (30), a new flavanone glycoside; hexahydroxy-monomethoxy flavanone-O-glucoside (28), and a new steviol glycoside; rebaudioside-O-acetate (96) were tentatively described. Additionally, the evaluation of the antidiabetic potential of both taxa displayed an inherited higher activity of H. stipulaceae in alleviating the oxidative stress and dyslipidemia associated with diabetes. Hence, the current research significantly suggested Halophila, Thalassia, and Enhalus categorization in three different taxonomic ranks based on their intergeneric and interspecific relationship among them and supported the consideration of seagrasses in natural antidiabetic studies.

Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures

The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.

Application of Optimized and Validated Agar Overlay TLC-Bioautography Assay for Detecting the Antimicrobial Metabolites of Pharmaceutical Interest

The agar overlay TLC-bioautography is one of the crucial methods for simultaneous in situ detection and separation of antimicrobial metabolites of pharmaceutical interest. The main focus of this research relies on the dereplication of an antimicrobial metabolite coriloxin derived from mycoendophytic Xylaria sp. NBRTSB-20 with a validation of agar overlay TLC-bioautography technique. This polyketide metabolite coriloxin was purified by column chromatography, and its purity was assessed by HPLC, UPLC-ESI-QTOF-MS, FT-IR and NMR spectral analysis. The antimicrobial capability of ethyl acetate extract and the purified compound coriloxin was determined by disc diffusion, minimal inhibitory concentration and agar overlay TLC-bioautography assay. The visible LOD of coriloxin antimicrobial activity was found at 10 μg for Escherichia coli and 20 μg for both Staphylococcus aureus and Fusarium oxysporum. Inter- and intra-day precision was determined as the relative standard deviation is less than 6.56%, which proved that this method was precise. The accuracy was expressed as recovery, and the values were found ranging from 91.18 to 108.73% with RSD values 0.94-2.30%, respectively. The overall findings of this investigation suggest that agar overlay TLC-bioautography assay is a suitable and acceptable method for the in situ determination of antimicrobial pharmaceuticals.

Ethnobotany and the Role of Plant Natural Products in Antibiotic Drug Discovery

The crisis of antibiotic resistance necessitates creative and innovative approaches, from chemical identification and analysis to the assessment of bioactivity. Plant natural products (NPs) represent a promising source of antibacterial lead compounds that could help fill the drug discovery pipeline in response to the growing antibiotic resistance crisis. The major strength of plant NPs lies in their rich and unique chemodiversity, their worldwide distribution and ease of access, their various antibacterial modes of action, and the proven clinical effectiveness of plant extracts from which they are isolated. While many studies have tried to summarize NPs with antibacterial activities, a comprehensive review with rigorous selection criteria has never been performed. In this work, the literature from 2012 to 2019 was systematically reviewed to highlight plant-derived compounds with antibacterial activity by focusing on their growth inhibitory activity. A total of 459 compounds are included in this Review, of which 50.8% are phenolic derivatives, 26.6% are terpenoids, 5.7% are alkaloids, and 17% are classified as other metabolites. A selection of 183 compounds is further discussed regarding their antibacterial activity, biosynthesis, structure-activity relationship, mechanism of action, and potential as antibiotics. Emerging trends in the field of antibacterial drug discovery from plants are also discussed. This Review brings to the forefront key findings on the antibacterial potential of plant NPs for consideration in future antibiotic discovery and development efforts.

Genomics- and Metabolomics-Based Investigation of the Deep-Sea Sediment-Derived Yeast, Rhodotorula mucilaginosa 50-3-19/20B

Red yeasts of the genus Rhodotorula are of great interest to the biotechnological industry due to their ability to produce valuable natural products, such as lipids and carotenoids with potential applications as surfactants, food additives, and pharmaceuticals. Herein, we explored the biosynthetic potential of R. mucilaginosa 50-3-19/20B collected from the Mid-Atlantic Ridge using modern genomics and untargeted metabolomics tools. R. mucilaginosa 50-3-19/20B exhibited anticancer activity when grown on PDA medium, while antimicrobial activity was observed when cultured on WSP-30 medium. Applying the bioactive molecular networking approach, the anticancer activity was linked to glycolipids, namely polyol esters of fatty acid (PEFA) derivatives. We purified four PEFAs (1–4) and the known methyl-2-hydroxy-3-(1H-indol-2-yl)propanoate (5). Their structures were deduced from NMR and HR-MS/MS spectra, but 1–5 showed no anticancer activity in their pure form. Illumina-based genome sequencing, de novo assembly and standard biosynthetic gene cluster (BGC) analyses were used to illustrate key components of the PEFA biosynthetic pathway. The fatty acid producing BGC3 was identified to be capable of producing precursors of PEFAs. Some Rhodotorula strains are able to convert inulin into high-yielding PEFA and cell lipid using a native exo-inulinase enzyme. The genomic locus for an exo-inulinase enzyme (g1629.t1), which plays an instrumental role in the PEFA production via the mannitol biosynthesis pathway, was identified. This is the first untargeted metabolomics study on R. mucilaginosa providing new genomic insights into PEFA biosynthesis.

UHPLC-ESI-QTOF-MS/MS-Based Molecular Networking Guided Isolation and Dereplication of Antibacterial and Antifungal Constituents of Ventilago denticulata

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.

Molecular networking based LC/MS reveals novel biotransformation products of green coffee by ex vivo cultures of the human gut microbiome

INTRODUCTION

Unroasted green coffee bean is an increasingly popular beverage and weight loss supplement that contains higher levels of chlorogenic acid derivatives and lower alkaloid levels than roasted beans. Nonetheless, how the gut microbiome metabolizes green coffee constituents has not been studied.

OBJECTIVES

To identify possible biotransformation products of green coffee extract by the human gut microbiome, and the potential implications of this process on its biological effects or fate inside the body.

METHODS

Molecular networking via the GNPS platform was employed for the visualization of green coffee metabolite profiles acquired using LC-tandem mass spectrometry post-incubation with an ex vivo culture of the human gut microbiome.

RESULTS

36 Metabolites were annotated including four unreported alkyl cinnamate esters in green coffee along with six novel biotransformation products.

CONCLUSION

Our finding reveals new biotransformation products of cinnamate esters by the gut microbiome mediated via oxidative reactions such as dehydrogenation and hydroxylation, along with methylation, decarboxylation, and deglycosylation. These findings reveal potential interactions between the gut microbiome and green coffee constituents, and paves the way towards studying the effects of these interactions on both microbiome and the human host.

Molecular networking aided metabolomic profiling of beet leaves using three extraction solvents and in relation to its anti-obesity effects

In the present study, the efficiency of three different solvents (H2O, acidified H2O, and 70% Methanol) for metabolites extraction from the leaves of sugar beet (Beta vulgaris subsp. vulgaris var. rubra) was investigated along with their inhibitory activity on pancreatic α-amylase and lipase for obesity management. The metabolic profile of the three extracts was analyzed by ultra-performance liquid chromatography (UPLC) coupled with electrospray ionization high-resolution mass spectrometric (ESI-HRMS-MS). Mass spectrometry-based molecular networking was employed to aid in metabolites annotation and for the visual investigation of the known metabolites and their analogues. The study led to the tentative identification of 45 metabolites including amino acids, purine derivatives, phenolic acids, flavonoids, fatty acids, and an alkaloid, articulating 24 compounds as a first time report from beet leaves along with 2 new putatively identified compounds: a flavone feruloyl conjugate (39) and a malonylated acacetin diglycoside (40). The three extracting systems exhibited comparable efficiency for pulling out the secondary metabolites from the beet leaves. The in vitro study supported this finding and demonstrated that the three extracts inhibited the activity of both pancreatic α-amylase and lipase enzymes with no significant difference observed regarding the percentage of the inhibition of the enzymes. Conclusively, the extraction protocol has a minimal effect on the anti-obesity properties of beet leaves.

Bioactive Molecular Networking for Mapping the Antimicrobial Constituents of the Baltic Brown Alga Fucus vesiculosus

The brown alga Fucus vesiculosus is common to the intertidal zones of the Baltic Sea, where it is exposed to high fouling pressures by microorganisms. Our previous studies showed, repeatedly, the consistent antimicrobial activity of F. vesiculosus crude extracts against human pathogens, while untargeted metabolomics analyses have revealed a variety of metabolites. In this study, we applied the UPLC-QToF-MS/MS-based "bioactive molecular networking" (BMN) concept on the most bioactive n-hexane and n-butanol subextracts of Baltic F. vesiculosus coupled with in silico dereplication tools to identify the compounds responsible for antimicrobial activity. The first antimicrobial cluster identified by BMN was galactolipids. Our targeted isolation efforts for this class led to the isolation of six monogalactosyldiacylglycerol (MGDG) derivatives (1-6) and one digalactosyldiacylglycerol (DGDG, 7). The MGDGs 5 and 6 and the DGDG 7 exhibited activity against Staphylococcus aureus. The second compound class with high bioactivity was phlorotannins. In particular, phlorethol-type phlorotannins showed high correlations with antimicrobial activity based on the BMN approach, and two phlorotannins (8-9) were isolated. This study shows that antimicrobial components of F. vesiculosus reside in the algal cell walls and membranes and that BMN provides a complementary tool for the targeted isolation of bioactive metabolites.

Cheminformatics Analysis and Modeling with MacrolactoneDB

Macrolactones, macrocyclic lactones with at least twelve atoms within the core ring, include diverse natural products such as macrolides with potent bioactivities (e.g. antibiotics) and useful drug-like characteristics. We have developed MacrolactoneDB, which integrates nearly 14,000 existing macrolactones and their bioactivity information from different public databases, and new molecular descriptors to better characterize macrolide structures. The chemical distribution of MacrolactoneDB was analyzed in terms of important molecular properties and we have utilized three targets of interest (Plasmodium falciparum, Hepatitis C virus and T-cells) to demonstrate the value of compiling this data. Regression machine learning models were generated to predict biological endpoints using seven molecular descriptor sets and eight machine learning algorithms. Our results show that merging descriptors yields the best predictive power with Random Forest models, often boosted by consensus or hybrid modeling approaches. Our study provides cheminformatics insights into this privileged, underexplored structural class of compounds with high therapeutic potential.

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes

The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.

Induction of Cryptic Antifungal Pulicatin Derivatives from Pantoea agglomerans by Microbial Co-Culture

Microbial co-culture or mixed fermentation proved to be an efficient strategy to expand chemical diversity by the induction of cryptic biosynthetic pathways, and in many cases led to the production of new antimicrobial agents. In the current study, we report a rare example of the induction of silent/cryptic bacterial biosynthetic pathway by the co-culture of Durum wheat plant roots-associated bacterium Pantoea aggolomerans and date palm leaves-derived fungus Penicillium citrinum. The initial co-culture indicated a clear fungal growth inhibition which was confirmed by the promising antifungal activity of the co-culture total extract against Pc. LC-HRMS chemical profiling demonstrated a huge suppression in the production of secondary metabolites (SMs) of axenic cultures of both species with the emergence of new metabolites which were dereplicated as a series of siderophores. Large-scale co-culture fermentation led to the isolation of two new pulicatin derivatives together with six known metabolites which were characterised using HRESIMS and NMR analyses. During the in vitro antimicrobial evaluation of the isolated compounds, pulicatin H (2) exhibited the strongest antifungal activity against Pc, followed by aeruginaldehyde (1) and pulicatin F (4), hence explaining the initial growth suppression of Pc in the co-culture environment.

Natural Products as a Foundation for Drug Discovery

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

Free Marine Natural Products Databases for Biotechnology and Bioengineering

Marine organisms and micro-organisms are a source of natural compounds with unique chemical features. These chemical properties are useful for the discovery of new functions and applications of marine natural products (MNPs). To extensively exploit the potential implementations of MNPs, they are gathered in chemical databases that allow their study and screening for applications of biotechnological interest. However, the classification of MNPs is currently poor in generic chemical databases. The present availability of free-access-focused MNP databases is scarce and the molecular diversity of these databases is still very low when compared to the paid-access ones. In this review paper, the current scenario of free-access MNP databases is presented as well as the hindrances involved in their development, mainly compound dereplication. Examples and opportunities for using freely accessible MNP databases in several important areas of biotechnology are also assessed. The scope of this paper is, as well, to notify the latent potential of these information sources for the discovery and development of new MNPs in biotechnology, and push future efforts to develop a public domain MNP database freely available for the scientific community.

Systematic Isolation and Structure Elucidation of Urinary Metabolites Optimized for the Analytical-Scale Molecular Profiling Laboratory

Annotation and identification of metabolite biomarkers is critical for their biological interpretation in metabolic phenotyping studies, presenting a significant bottleneck in the successful implementation of untargeted metabolomics. Here, a systematic multistep protocol was developed for the purification and de novo structural elucidation of urinary metabolites. The protocol is most suited for instances where structure elucidation and metabolite annotation are critical for the downstream biological interpretation of metabolic phenotyping studies. First, a bulk urine pool was desalted using ion-exchange resins enabling large-scale fractionation using precise iterations of analytical scale chromatography. Primary urine fractions were collected and assembled into a “fraction bank” suitable for long-term laboratory storage. Secondary and tertiary fractionations exploited differences in selectivity across a range of reversed-phase chemistries, achieving the purification of metabolites of interest yielding an amount of material suitable for chemical characterization. To exemplify the application of the systematic workflow in a diverse set of cases, four metabolites with a range of physicochemical properties were selected and purified from urine and subjected to chemical formula and structure elucidation by respective magnetic resonance mass spectrometry (MRMS) and NMR analyses. Their structures were fully assigned as tetrahydropentoxyline, indole-3-acetic-acid-O-glucuronide, p-cresol glucuronide, and pregnanediol-3-glucuronide. Unused effluent was collected, dried, and returned to the fraction bank, demonstrating the viability of the system for repeat use in metabolite annotation with a high degree of efficiency.

Traditional Uses, Bioactive Constituents, Biological Functions, and Safety Properties of Oviductus ranae as Functional Foods in China

Oviductus ranae is an animal-based traditional Chinese material widely used as tonics in China for hundreds of years. Various bioactive components are present in OR including proteins, amino acids, steroids, fatty acids, phospholipids, nucleosides, vitamins, hydantoins, and mineral elements. These constituents exert a myriad of biological functions such as immunomodulatory, antioxidant, antifatigue, antiaging, estrogen-like, hepatoprotective, hypolipidemic, antiosteoporotic, antidepressant, antitumor, antitussive, expectorant, anti-inflammatory, and antiasthmatic activities. Unlike other traditional Chinese crude drugs recorded in Chinese Pharmacopoeia, OR is seldom prescribed as medicine but often consumed as nutraceuticals to optimize health. In this review, the traditional uses, bioactive constituents, biological functions, and safety properties of OR as functional foods in China were summarized and discussed. It is expected that this review will provide useful information for anyone who is interested in OR.

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

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

Unearthing fungal chemodiversity and prospects for drug discovery

Natural products have drastically improved our lives by providing an excellent source of molecules to fight cancer, pathogens, and cardiovascular diseases that have revolutionized medicine. Fungi are prolific producers of diverse natural products and several recent advances in synthetic biology, genetics, bioinformatics, and natural product chemistry have greatly enhanced our ability to efficiently mine their genomes for the discovery of novel drugs. In this article, we provide an overview of improved heterologous expression platforms for targeted production of fungal secondary metabolites, of advances in chemical and bioinformatics dereplication, and of novel bioinformatic platforms to discover biosynthetic genes involved in the production of metabolites with specific bioactivities. These advances, coupled with the presence of vast numbers of biosynthetic gene clusters in fungal genomes whose natural products remain unknown, have revitalized efforts to mine the fungal treasure chest and renewed the promise of discovering new drugs.

Marine natural products

Covering: January to December 2017This review covers the literature published in 2017 for marine natural products (MNPs), with 740 citations (723 for the period January to December 2017) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms. The emphasis is on new compounds (1490 in 477 papers for 2017), together with the relevant biological activities, source organisms and country of origin. Reviews, biosynthetic studies, first syntheses, and syntheses that led to the revision of structures or stereochemistries, have been included. Geographic distributions of MNPs at a phylogenetic level are reported.

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

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

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation

It is a common problem in natural product therapeutic lead discovery programs that despite good bioassay results in the initial extract, the active compound(s) may not be isolated during subsequent bioassay-guided purification. Herein, we present the concept of bioactive molecular networking to find candidate active molecules directly from fractionated bioactive extracts. By employing tandem mass spectrometry, it is possible to accelerate the dereplication of molecules using molecular networking prior to subsequent isolation of the compounds, and it is also possible to expose potentially bioactive molecules using bioactivity score prediction. Indeed, bioactivity score prediction can be calculated with the relative abundance of a molecule in fractions and the bioactivity level of each fraction. For that reason, we have developed a bioinformatic workflow able to map bioactivity score in molecular networks and applied it for discovery of antiviral compounds from a previously investigated extract of Euphorbia dendroides where the bioactive candidate molecules were not discovered following a classical bioassay-guided fractionation procedure. It can be expected that this approach will be implemented as a systematic strategy, not only in current and future bioactive lead discovery from natural extract collections but also for the reinvestigation of the untapped reservoir of bioactive analogues in previous bioassay-guided fractionation efforts.

HSQC-TOCSY Fingerprinting for Prioritization of Polyketide- and Peptide-Producing Microbial Isolates

Microbial products are a promising source for drug leads as a result of their unique structural diversity. However, reisolation of already known natural products significantly hampers the discovery process, and it is therefore important to incorporate effective microbial isolate selection and dereplication protocols early in microbial natural product studies. We have developed a systematic approach for prioritization of microbial isolates for natural product discovery based on heteronuclear single-quantum correlation-total correlation spectroscopy (HSQC-TOCSY) nuclear magnetic resonance profiles in combination with antiplasmodial activity of extracts. The HSQC-TOCSY experiments allowed for unfractionated microbial extracts containing polyketide and peptidic natural products to be rapidly identified. Here, we highlight how this approach was used to prioritize extracts derived from a library of 119 ascidian-associated actinomycetes that possess a higher potential to produce bioactive polyketides and peptides.

Analysing and Navigating Natural Products Space for Generating Small, Diverse, But Representative Chemical Libraries

Armed with the digital availability of two natural products libraries, amounting to some 195 885 molecular entities, we ask the question of how we can best sample from them to maximize their "representativeness" in smaller and more usable libraries of 96, 384, 1152, and 1920 molecules. The term "representativeness" is intended to include diversity, but for numerical reasons (and the likelihood of being able to perform a QSAR) it is necessary to focus on areas of chemical space that are more highly populated. Encoding chemical structures as fingerprints using the RDKit "patterned" algorithm, we first assess the granularity of the natural products space using a simple clustering algorithm, showing that there are major regions of "denseness" but also a great many very sparsely populated areas. We then apply a "hybrid" hierarchical K-means clustering algorithm to the data to produce more statistically robust clusters from which representative and appropriate numbers of samples may be chosen. There is necessarily again a trade-off between cluster size and cluster number, but within these constraints, libraries containing 384 or 1152 molecules can be found that come from clusters that represent some 18 and 30% of the whole chemical space, with cluster sizes of, respectively, 50 and 27 or above, just about sufficient to perform a QSAR. By using the online availability of molecules via the Molport system (www.molport.com), we are also able to construct (and, for the first time, provide the contents of) a small virtual library of available molecules that provided effective coverage of the chemical space described. Consistent with this, the average molecular similarities of the contents of the libraries developed is considerably smaller than is that of the original libraries. The suggested libraries may have use in molecular or phenotypic screening, including for determining possible transporter substrates.

High Resolution NMR Spectroscopy as a Structural and Analytical Tool for Unsaturated Lipids in Solution

Mono- and polyunsaturated lipids are widely distributed in Nature, and are structurally and functionally a diverse class of molecules with a variety of physicochemical, biological, medicinal and nutritional properties. High resolution NMR spectroscopic techniques including 1H-, 13C- and 31P-NMR have been successfully employed as a structural and analytical tool for unsaturated lipids. The objective of this review article is to provide: (i) an overview of the critical 1H-, 13C- and 31P-NMR parameters for structural and analytical investigations; (ii) an overview of various 1D and 2D NMR techniques that have been used for resonance assignments; (iii) selected analytical and structural studies with emphasis in the identification of major and minor unsaturated fatty acids in complex lipid extracts without the need for the isolation of the individual components; (iv) selected investigations of oxidation products of lipids; (v) applications in the emerging field of lipidomics; (vi) studies of protein-lipid interactions at a molecular level; (vii) practical considerations and (viii) an overview of future developments in the field.