Natural Product Chemical Space Research Articles

Effectiveness of molecular fingerprints for exploring the chemical space of natural products

Natural products are a diverse class of compounds with promising biological properties, such as high potency and excellent selectivity. However, they have different structural motifs than typical drug-like compounds, e.g., a wider range of molecular weight, multiple stereocenters and higher fraction of sp3-hybridized carbons. This makes the encoding of natural products via molecular fingerprints difficult, thus restricting their use in cheminformatics studies. To tackle this issue, we explored over 30 years of research to systematically evaluate which molecular fingerprint provides the best performance on the natural product chemical space. We considered 20 molecular fingerprints from four different sources, which we then benchmarked on over 100,000 unique natural products from the COCONUT (COlleCtion of Open Natural prodUcTs) and CMNPD (Comprehensive Marine Natural Products Database) databases. Our analysis focused on the correlation between different fingerprints and their classification performance on 12 bioactivity prediction datasets. Our results show that different encodings can provide fundamentally different views of the natural product chemical space, leading to substantial differences in pairwise similarity and performance. While Extended Connectivity Fingerprints are the de-facto option to encoding drug-like compounds, other fingerprints resulted to match or outperform them for bioactivity prediction of natural products. These results highlight the need to evaluate multiple fingerprinting algorithms for optimal performance and suggest new areas of research. Finally, we provide an open-source Python package for computing all molecular fingerprints considered in the study, as well as data and scripts necessary to reproduce the results, at https://github.com/dahvida/NP_Fingerprints.

Building Block Extractor: An MS/MS Data Mining Tool for Targeted Discovery of Natural Products with Specified Features.

The utilization of a building-block-based molecular network is an efficient approach to investigate the unknown chemical space of natural products. However, structure-based automated MS/MS data mining remains challenging. This study introduces building block extractor, a user-friendly MS/MS data mining program that automatically extracts user-defined specified features. In addition to the characteristic product ions and neutral losses, this program integrates the abundance of the product ions and sequential neutral loss features as building blocks for the first time. The discovery of nine undescribed sesquiterpenoid dimers from Artemisia heptapotamica highlights the power of this tool. One of these dimers, artemiheptolide I (9), exhibited in vitro inhibition of influenza A/Hongkong/8/68 (H3N2) with an IC50 of 8.01 ± 6.19 μM. Furthermore, two known guaianolide derivatives (16 and 17) possessed remarkable antiviral activity against influenza A/Puerto Rico/8/1934 H1N1, H3N2, and influenza B/Lee/40 with IC50 values ranging from 3.46 to 11.77 μM. In addition to the efficient discovery of novel natural products, this strategy can be generally applied to grab derivatives with specific fragments and enhance the annotation power of LC-MS/MS analysis.

67 million natural product-like compound database generated via molecular language processing

Natural products are a rich resource of bioactive compounds for valuable applications across multiple fields such as food, agriculture, and medicine. For natural product discovery, high throughput in silico screening offers a cost-effective alternative to traditional resource-heavy assay-guided exploration of structurally novel chemical space. In this data descriptor, we report a characterized database of 67,064,204 natural product-like molecules generated using a recurrent neural network trained on known natural products, demonstrating a significant 165-fold expansion in library size over the approximately 400,000 known natural products. This study highlights the potential of using deep generative models to explore novel natural product chemical space for high throughput in silico discovery.

Evidence of Insulin-Sensitizing and Mimetic Activity of the Sesquiterpene Quinone Avarone, a Protein Tyrosine Phosphatase 1B and Aldose Reductase Dual Targeting Agent from the Marine Sponge Dysidea avara.

Type 2 diabetes mellitus (T2DM) is a complex disease characterized by impaired glucose homeostasis and serious long-term complications. First-line therapeutic options for T2DM treatment are monodrug therapies, often replaced by multidrug therapies to ensure that non-responding patients maintain target glycemia levels. The use of multitarget drugs instead of mono- or multidrug therapies has been emerging as a main strategy to treat multifactorial diseases, including T2DM. Therefore, modern drug discovery in its early stages aims to identify potential modulators for multiple targets; for this purpose, exploration of the chemical space of natural products represents a powerful tool. Our study demonstrates that avarone, a sesquiterpene quinone obtained from the sponge Dysidea avara, is capable of inhibiting in vitro PTP1B, the main negative regulator of the insulin receptor, while it improves insulin sensitivity, and mitochondria activity in C2C12 cells. We observe that when avarone is administered alone, it acts as an insulin-mimetic agent. In addition, we show that avarone acts as a tight binding inhibitor of aldose reductase (AKR1B1), the enzyme involved in the development of diabetic complications. Overall, avarone could be proposed as a novel natural hit to be developed as a multitarget drug for diabetes and its pathological complications.

Noonindoles G-L: Indole Diterpene Glycosides from the Australian Marine-Derived Fungus Aspergillus noonimiae CMB-M0339.

Fungal indole diterpenes (IDTs) occupy a valuable region of bioactive natural product chemical space, displaying potent and selective inhibition of therapeutically important ion channels and with potential application in the treatment of glaucoma, cancer, and neurodegenerative diseases, as well as insecticides and antivirals. We have employed an integrated workflow of analytical scale chemical profiling using GNPS (Global Natural Products Social molecular networking) and cultivation profiling (also known as "MATRIX" miniaturized microbioreactor) to detect, prioritize, optimize the production, isolate, characterize, and identify a new series of indole diterpenes, noonindoles G-L (7-12), from an Australian marine-derived fungus, Aspergillus noonimiae CMB-M0339. The first reported examples of IDT glycosides, the molecular structures for 7-12, were assigned on the basis of detailed spectroscopic analysis and biosynthetic considerations.

C-H modification of natural products: a minimalist enabling tactic for drug discovery, API processing and bioconjugation.

Intrusion into the C-H chemical space of natural products through the strategic deployment of C-H functionalization reactions could lead to incredibly new molecular diversities with an unforeseen impact on biological functions. Based on this hypothesis, semisynthetic C-H modification of natural products is emerging as a minimalist tactic in natural product based drug discovery. Several examples of C-H modification of natural products, resulting in functional gains in key pharmacological attributes viz. potency, aqueous solubility and DMPK profile, along with opportunities in allied areas such as API processing, bioconjugation, and target deconvolution, continue to surface in the recent literature. The strategy has already recorded commercial success in the development of antineoplastic drugs topotecan and irinotecan and in industrial production of pravastatin, calcitriol and artemisinin. This Feature Article highlights the broad contours of this evolving paradigm at the interface of natural product and synthetic chemistry research to accelerate and widen the scope of natural product-based drug discovery.

Plant glycosyltransferases for expanding bioactive glycoside diversity.

Glycosylation is a successful strategy to alter the pharmacological properties of small molecules, and it has emerged as a unique approach to expand the chemical space of natural products that can be explored in drug discovery. Traditionally, most glycosylation events have been carried out chemically, often requiring many protection and deprotection steps to achieve a target molecule. Enzymatic glycosylation by glycosyltransferases could provide an alternative strategy for producing new glycosides. In particular, the glycosyltransferase family has greatly expanded in plants, representing a rich enzymatic resource to mine and expand the diversity of glycosides with novel bioactive properties. This article highlights previous and prospective uses for plant glycosyltransferases in generating bioactive glycosides and altering their pharmacological properties.

3-(3-Bromophenyl)-7-acetoxycoumarin

In natural product synthesis, the procurement of easily accessible starting materials is crucial. Chromenones and their subclass, coumarins, are a wide family of small, oxygen-containing aromatic heterocycles. Phenylcoumarins offer a particularly excellent starting point for a diverse chemical space of natural products, and thus are excellent staring materials for more complex natural products. Herein, we report an efficient synthesis of an easily accessible 3-phenylcoumarin bearing two orthogonally substitutable groups, bromine, and an acetyl-protected phenylic hydroxyl group.

From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples

Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products’ drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.

Discovery of actinomycin L, a new member of the actinomycin family of antibiotics

Streptomycetes are major producers of bioactive natural products, including the majority of the naturally produced antibiotics. While much of the low-hanging fruit has been discovered, it is predicted that less than 5% of the chemical space of natural products has been mined. Here, we describe the discovery of the novel actinomycins L1 and L2 produced by Streptomyces sp. MBT27, via application of metabolic analysis and molecular networking. Actinomycins L1 and L2 are diastereomers, and the structure of actinomycin L2 was resolved using NMR and single crystal X-ray crystallography. Actinomycin L is formed via spirolinkage of anthranilamide to the 4-oxoproline moiety of actinomycin X2, prior to the condensation of the actinomycin halves. Such a structural feature has not previously been identified in naturally occurring actinomycins. Adding anthranilamide to cultures of the actinomycin X2 producer Streptomyces antibioticus, which has the same biosynthetic gene cluster as Streptomyces sp. MBT27, resulted in the production of actinomycin L. This supports a biosynthetic pathway whereby actinomycin L is produced from two distinct metabolic routes, namely those for actinomycin X2 and for anthranilamide. Actinomycins L1 and L2 showed significant antimicrobial activity against Gram-positive bacteria. Our work shows how new molecules can still be identified even in the oldest of natural product families.

New Antagonists of the Membrane Androgen Receptor OXER1 from the ZINC Natural Product Database.

OXER1 (oxoeicosanoid receptor 1) was deorphanized in 1993 and found to be the specific receptor for the arachidonic acid metabolite 5-oxo-ETE. Recently, we have reported that androgen binds to this receptor also, being a membrane androgen receptor, triggering a number of its membrane-mediated actions (cell migration, apoptosis, cell proliferation, Ca2+ movements). In addition, our previous work suggested that a number of natural monomeric and oligomeric polyphenols interact with OXER1, acting similar to testosterone. Here, we interrogated the natural product chemical space and identified nine polyphenolic molecules with interesting in silico pharmacological activities as putative OXER1 antagonists. The molecule with the best pharmacokinetic–pharmacodynamic properties (ZINC15959779) was purchased and tested on OXER1, in prostate cancer cell cultures. It showed that it has actions similar to those of testosterone in inhibiting cAMP, while it had no action in intracellular Ca2+ mobilization or actin cytoskeleton rearrangement/migration. These results are discussed under the prism of structure–activity relationships and in silico models of the OXER1 binding groove. We suggest that these compounds, together with the previously reported (poly)phenolic compounds, can be lead structures for the exploration of the anti-inflammatory and antiproliferative effects of OXER1 antagonists.

NP Navigator: A New Look at the Natural Product Chemical Space.

Natural products (NPs), being evolutionary selected over millions of years to bind to biological macromolecules, remained an important source of inspiration for medicinal chemists even after the advent of efficient drug discovery technologies such as combinatorial chemistry and high-throughput screening. Thus, there is a strong demand for efficient and user-friendly computational tools that allow to analyze large libraries of NPs. In this context, we introduce NP Navigator - a freely available intuitive online tool for visualization and navigation through the chemical space of NPs and NP-like molecules. It is based on the hierarchical ensemble of generative topographic maps, featuring NPs from the COlleCtion of Open NatUral producTs (COCONUT), bioactive compounds from ChEMBL and commercially available molecules from ZINC. NP Navigator allows to efficiently analyze different aspects of NPs - chemotype distribution, physicochemical properties, biological activity and commercial availability of NPs. The latter concerns not only purchasable NPs but also their close analogs that can be considered as synthetic mimetics of NPs or pseudo-NPs.

Cell-Free Exploration of the Natural Product Chemical Space.

Natural products and secondary metabolites comprise an indispensable resource from living organisms that have transformed areas of medicine, agriculture, and biotechnology. Recent advances in high-throughput DNA sequencing and computational analysis suggest that the vast majority of natural products remain undiscovered. To accelerate the natural product discovery pipeline, cell-free metabolic engineering approaches used to develop robust catalytic networks are being repurposed to access new chemical scaffolds, and new enzymes capable of performing diverse chemistries. Such enzymes could serve as flexible biocatalytic tools to further expand the unique chemical space of natural products and secondary metabolites, and provide a more sustainable route to manufacture these molecules. Herein, we highlight select examples of natural product biosynthesis using cell-free systems and propose how cell-free technologies could facilitate our ability to access and modify these structures to transform synthetic and chemical biology.

Case studies on computer-based identification of natural products as lead molecules

AbstractThe development and application of computer-aided drug design/discovery (CADD) techniques (such as structured-base virtual screening, ligand-based virtual screening and neural networks approaches) are on the point of disintermediation in the pharmaceutical drug discovery processes. The application of these CADD methods are standing out positively as compared to other experimental approaches in the identification of hits. In order to venture into new chemical spaces, research groups are exploring natural products (NPs) for the search and identification of new hits and more efficient leads as well as the repurposing of approved NPs. The chemical space of NPs is continuously increasing as a result of millions of years of evolution of species and these data are mainly stored in the form of databases providing access to scientists around the world to conduct studies using them. Investigation of these NP databases with the help of CADD methodologies in combination with experimental validation techniques is essential to identify and propose new drug molecules. In this chapter, we highlight the importance of the chemical diversity of NPs as a source for potential drugs as well as some of the success stories of NP-derived candidates against important therapeutic targets. The focus is on studies that applied a healthy dose of the emerging CADD methodologies (structure-based, ligand-based and machine learning).

Correction to "A Novel G Protein-Biased and Subtype-Selective Agonist for a G Protein-Coupled Receptor Discovered from Screening Herbal Extracts".

[This corrects the article DOI: 10.1021/acscentsci.9b01125.].

Electronic complementarity permits hindered butenolide heterodimerization and discovery of novel cGAS/STING pathway antagonists.

sp3-hybridized attached-rings are common motifs in secondary metabolites and represent tetrahedral equivalents to the biaryl substructures that overpopulate synthetic libraries. Few methods are available that can link fully substituted carbon atoms of two rings with stereocontrol. Here we have developed a stereoselective, heteroselective butenolide coupling that exhibits an unusually fast rate of C-C bond formation driven by exquisite complementarity of the reacting π systems. Heterodimerization generates a compound collection with topological complexity and diverse principal moments of inertia. The special status of the sp3-sp3 attached-ring motif is demonstrated in a high-throughput screen for inhibitors of the cyclic GMP-AMP synthase/stimulator of interferon genes pathway, which recruited these butenolide heterodimers from a field of 250,000 compounds. The driving forces underlying this general attached-ring coupling identify a novel paradigm for the accession of wider natural product chemical space, accelerating the discovery of selective lead compounds.

A Novel G Protein-Biased and Subtype-Selective Agonist for a G Protein-Coupled Receptor Discovered from Screening Herbal Extracts.

Subtype selectivity and functional bias are vital in current drug discovery for G protein-coupled receptors (GPCRs) as selective and biased ligands are expected to yield drug leads with optimal on-target benefits and minimal side-effects. However, structure-based design and medicinal chemistry exploration remain challenging in part because of highly conserved binding pockets within subfamilies. Herein, we present an affinity mass spectrometry approach for screening herbal extracts to identify active ligands of a GPCR, the 5-HT2C receptor. Using this method, we discovered a naturally occurring aporphine 1857 that displayed strong selectivity for activating 5-HT2C without activating the 5-HT2A or 5-HT2B receptors. Remarkably, this novel ligand exhibited exclusive bias toward G protein signaling for which key residues were identified, and it showed comparable in vivo efficacy for food intake suppression and weight loss as the antiobesity drug, lorcaserin. Our study establishes an efficient approach to discovering novel GPCR ligands by exploring the largely untapped chemical space of natural products.

Search for Potential Inducible Nitric Oxide Synthase Inhibitors with Favorable ADMET Profiles for the Therapy of Helicobacter pylori Infections.

Helicobacter pylori is a gram-negative bacterium related to chronic gastritis, peptic ulcer and gastric carcinoma. During its infection process, promotes excessive inflammatory response, increasing the release of reactive species and inducing the production of pro-inflammatory mediators. Inducible Nitric Oxide Synthase (iNOS) plays a crucial role in the gastric carcinogenesis process and a key mediator of inflammation and host defense systems, which is expressed in macrophages induced by inflammatory stimuli. In chronic diseases such as Helicobacter pylori infections, the overproduction of NO due to the prolonged induction of iNOS is of major concern. In this sense, the search for potential iNOS inhibitors is a valuable strategy in the overall process of Helicobacter pylori pathogeny. In silico techniques were applied in the search of interesting compounds against Inducible Nitric Oxide Synthase enzyme in a chemical space of natural products and derivatives from the Analyticon Discovery databases. The five compounds with the best iNOS inhibition profile were selected for activity and toxicity predictions. Compound 9 (CAS 88198-99-6) displayed significant potential for iNOS inhibition, forming hydrogen bonds with residues from the active site and an ionic interaction with heme. This compound also displayed good bioavailability and absence of toxicity/or from its probable metabolites. The top-ranked compounds from the virtual screening workflow show promising results regarding the iNOS inhibition profile. The results evidenced the importance of the ionic bonding during docking selection, playing a crucial role in binding and positioning during ligand-target selection for iNOS.

Analysis of the Acid/Base Profile of Natural Products from Different Sources.

For many years drug discovery and other areas in chemistry have successfully relied on natural products. Recent advances in computational methods have made possible to study the chemical space of natural products from different sources. Ionizable acidic and basic functional groups heavily influence physicochemical properties and thus a molecule's absorption, distribution, metabolism, excretion, and toxicity characteristics as well as their affinity for biological targets. This work reports the generation and critical comparison of the acid/base profiles of ten chemical databases including seven natural products sets from different origins, a set of semisynthetic compounds, a collection of approved drugs, and a compendium of food chemicals. Similarities were found in the proportion of the main charge state categories among the natural products databases with few differences in their pKa distributions. Clear differences were observed between natural products and the approved drugs and semi-synthetic natural products databases, whereas natural products share some trends with the food chemical database. We noted that the natural products collections comprise around 45 % of neutral compounds. The proportion of single acids was approximately twice that found for FDA drugs, and they demonstrated a similar distribution of pKa values. In contrast to drugs, only 5 % of compounds among the natural products sets had a single basic group. Likewise, simple ampholytes were less prevalent in the natural products databases relative to drugs.

Cheminformatics Explorations of Natural Products.

The chemistry of natural products is fascinating and has continuously attracted the attention of the scientific community for many reasons including, but not limited to, biosynthesis pathways, chemical diversity, the source of bioactive compounds and their marked impact on drug discovery. There is a broad range of experimental and computational techniques (molecular modeling and cheminformatics) that have evolved over the years and have assisted the investigation of natural products. Herein, we discuss cheminformatics strategies to explore the chemistry and applications of natural products. Since the potential synergisms between cheminformatics and natural products are vast, we will focus on three major aspects: (1) exploration of the chemical space of natural products to identify bioactive compounds, with emphasis on drug discovery; (2) assessment of the toxicity profile of natural products; and (3) diversity analysis of natural product collections and the design of chemical collections inspired by natural sources.