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

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

Lewis-Base-Catalyzed N-Allylation of Silyl Carbamate Latent Pronucleophiles with Allylic Fluorides

Silyl carbamates, latent pronucleophile surrogates of carbamates, undergo allylation using allylic fluorides in the presence of common Lewis base catalysts. The reactions are rendered enantioselective in the presence of chiral Lewis base catalysts and produce suitably protected derivatives of enantioenriched chiral β-amino acids. The design of the latent pronucleophile featuring both a silyl group and an electron-deficient carbamate is instrumental in lowering the nucleophilicity of nitrogen and enabling enantioselective allylation in the presence of chiral cinchona alkaloid-based catalysts.

Use of carbon-13 NMR to identify known natural products by querying a nuclear magnetic resonance database-An assessment

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

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

INTRODUCTION

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

OBJECTIVES

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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

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

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

NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.

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

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

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

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

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

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