Historical Variation of Structural Novelty in a Natural Product Library

Diversity and taxonomic distribution of bacterial biosynthetic gene clusters predicted to produce compounds with therapeutically relevant bioactivities

Bacteria have long been a source of natural products with diverse bioactivities that have been developed into therapeutics to treat human disease. Historically, researchers have focused on a few taxa of bacteria, mainly Streptomyces and other actinomycetes. This strategy was initially highly successful and resulted in the golden era of antibiotic discovery. The golden era ended when the most common antibiotics from Streptomyces had been discovered. Rediscovery of known compounds has plagued natural product discovery ever since. Recently, there has been increasing interest in identifying other taxa that produce bioactive natural products. Several bioinformatics studies have identified promising taxa with high biosynthetic capacity. However, these studies do not address the question of whether any of the products produced by these taxa are likely to have activities that will make them useful as human therapeutics. We address this gap by applying a recently developed machine learning tool that predicts natural product activity from biosynthetic gene cluster (BGC) sequences to determine which taxa are likely to produce compounds that are not only novel but also bioactive. This machine learning tool is trained on a dataset of BGC-natural product activity pairs and relies on counts of different protein domains and resistance genes in the BGC to make its predictions. We find that rare and understudied actinomycetes are the most promising sources for novel active compounds. There are also several taxa outside of actinomycetes that are likely to produce novel active compounds. We also find that most strains of Streptomyces likely produce both characterized and uncharacterized bioactive natural products. The results of this study provide guidelines to increase the efficiency of future bioprospecting efforts.

ONE-SENTENCE SUMMARY

This paper combines several bioinformatics workflows to identify which genera of bacteria are most likely to produce novel natural products with useful bioactivities such as antibacterial, antitumor, or antifungal activity.

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities

As coronavirus disease 2019 (COVID-19) threatens human health globally, infectious disorders have become one of the most challenging problem for the medical community. Natural products (NP) have been a prolific source of antimicrobial agents with widely divergent structures and a range vast biological activities. A dataset comprising 618 articles, including 646 NP-based compounds from 672 species of natural sources with biological activities against 21 infectious pathogens from five categories, was assembled through manual selection of published articles. These data were used to identify 268 NP-based compounds classified into ten groups, which were used for network pharmacology analysis to capture the most promising lead-compounds such as agelasine D, dicumarol, dihydroartemisinin and pyridomycin. The distribution of maximum Tanimoto scores indicated that compounds which inhibited parasites exhibited low diversity, whereas the chemistries inhibiting bacteria, fungi, and viruses showed more structural diversity. A total of 331 species of medicinal plants with compounds exhibiting antimicrobial activities were selected to classify the family sources. The family Asteraceae possesses various compounds against C. neoformans, the family Anacardiaceae has compounds against Salmonella typhi, the family Cucurbitacea against the human immunodeficiency virus (HIV), and the family Ancistrocladaceae against Plasmodium. This review summarizes currently available data on NP-based antimicrobials against refractory infections to provide information for further discovery of drugs and synthetic strategies for anti-infectious agents.

Anti-Quorum Sensing and Antioxidant Activity of Essential Oils Extracted From Juniperus Species, Growing Spontaneously in Tebessa Region (East of Algeria)

The chemical composition of essential oils (EOs) extracted from the aerial parts of 2 species of Juniperus was determined by Gas Chromatography-Mass Spectrometry (GC-MS). In total, 65 and 58 compounds accounting for 90.3% and 89.8% of the whole chemical composition of Juniperus oxycedrus (JO) and Juniperus phoenicea (JP) were identified, respectively, with α-pinene, α-amorphene, terpinen-4-ol, α-terpinene, and β-elemene, as major components. For the first time, the capacity to inhibit quorum-sensing for Chromobacterium violaceum CV026 and CV12472 by the investigated EOs was evaluated. Both oils exhibited good violacein inhibition on CV12472 with 100.0 ± 0.0% inhibition at minimal inhibition concentration (MIC) values. Besides, the quorum-sensing inhibition of CV026 was high at MIC for JO essential oil from fruits (JOF, 16.3 ± 2.0 mm), JO leaves (JOL, 12.5 ± 3.5 mm), JP fruits (JPF, 19.7 ± 2.5 mm), and JP leaves (JPL, 21.1 ± 5.0 mm). On both CV12472 and CV026, essential oil from J. phoenicea leaves was the most active inhibitor. All investigated EOs inhibited swarming motilities in flagellated Pseudomonas aeruginosa (PA01) in a concentration-dependent manner, and those from JP were more active than EOs from JO. Moreover, these EOs showed good antioxidant potential according to DPPH● and FRAP methods, especially the EO from JO leaves with an IC50 DPPH● inhibition value of 20.2 ± 1.0 mg/mL. Based on the obtained results, the investigated EOs are good candidates to combat microbial resistance be used as alternatives to conventional antibiotics, and equally find applications in food biosafety as preservatives.

A chemoinformatic analysis of atoms, scaffolds and functional groups in natural products

In the quest to know why natural products (NPs) have often been considered as privileged scaffolds for drug discovery purposes, many investigations into the differences between NPs and synthetic compounds have been carried out. Several attempts to answer this question have led to the investigation of the atomic composition, scaffolds and functional groups (FGs) of NPs, in comparison with synthetic drugs analysis. This chapter briefly describes an atomic enumeration method for chemical libraries that has been applied for the analysis of NP libraries, followed by a description of the main differences between NPs of marine and terrestrial origin in terms of their general physicochemical properties, most common scaffolds and “drug-likeness” properties. The last parts of the work describe an analysis of scaffolds and FGs common in NP libraries, focusing on huge NP databases, e.g. those in the Dictionary of Natural Products (DNP), NPs from cyanobacteria and the largest chemical class of NP – terpenoids.

A Machine Learning Bioinformatics Method to Predict Biological Activity from Biosynthetic Gene Clusters

Research in natural products, the genetically encoded small molecules produced by organisms in an idiosyncratic fashion, deals with molecular structure, biosynthesis, and biological activity. Bioinformatics analyses of microbial genomes can successfully reveal the genetic instructions, biosynthetic gene clusters, that produce many natural products. Genes to molecule predictions made on biosynthetic gene clusters have revealed many important new structures. There is no comparable method for genes to biological activity predictions. To address this missing pathway, we developed a machine learning bioinformatics method for predicting a natural product's antibiotic activity directly from the sequence of its biosynthetic gene cluster. We trained commonly used machine learning classifiers to predict antibacterial or antifungal activity based on features of known natural product biosynthetic gene clusters. We have identified classifiers that can attain accuracies as high as 80% and that have enabled the identification of biosynthetic enzymes and their corresponding molecular features that are associated with antibiotic activity.

Targeted Genome Mining-From Compound Discovery to Biosynthetic Pathway Elucidation

Natural products are an important source of novel investigational compounds in drug discovery. Especially in the field of antibiotics, Actinobacteria have been proven to be a reliable source for lead structures. The discovery of these natural products with activity- and structure-guided screenings has been impeded by the constant rediscovery of previously identified compounds. Additionally, a large discrepancy between produced natural products and biosynthetic potential in Actinobacteria, including representatives of the order Pseudonocardiales, has been revealed using genome sequencing. To turn this genomic potential into novel natural products, we used an approach including the in-silico pre-selection of unique biosynthetic gene clusters followed by their systematic heterologous expression. As a proof of concept, fifteen Saccharothrixespanaensis genomic library clones covering predicted biosynthetic gene clusters were chosen for expression in two heterologous hosts, Streptomyceslividans and Streptomycesalbus. As a result, two novel natural products, an unusual angucyclinone pentangumycin and a new type II polyketide synthase shunt product SEK90, were identified. After purification and structure elucidation, the biosynthetic pathways leading to the formation of pentangumycin and SEK90 were deduced using mutational analysis of the biosynthetic gene cluster and feeding experiments with ¹³C-labelled precursors.

The chemistry and biology of guanidine secondary metabolites

Covering: 2017-2019Guanidine natural products isolated from microorganisms, marine invertebrates and terrestrial plants, amphibians and spiders, represented by non-ribosomal peptides, guanidine-bearing polyketides, alkaloids, terpenoids and shikimic acid derived, are the subject of this review. The topics include the discovery of new metabolites, total synthesis of natural guanidine compounds, biological activity and mechanism-of-action, biosynthesis and ecological functions.

Molecular Architect: A User-Friendly Workflow for Virtual Screening

Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

Research Tales from the Clardy Laboratory: Function-Driven Natural Product Discovery

Over the past 70 years, the search for small molecules from nature has transformed biomedical research: natural products are the basis for half of all pharmaceuticals; the quest for total synthesis of natural products fueled development of methodologies for organic synthesis; and their biosynthesis presented unprecedented biochemical transformations, expanding our chemo-enzymatic toolkit. Initially, the discovery of small molecules was driven by bioactivity-guided fractionation. However, this approach yielded the frequent rediscovery of already known metabolites. As a result, focus shifted to identifying novel scaffolds through either structure-first methods or genome mining, relegating function as a secondary concern. Over the past two decades, the laboratory of Jon Clardy has taken an alternative route and focused on an ecology-driven, function-first approach in pursuit of uncovering bacterial small molecules with biological activity. In this review, we highlight several examples that showcase this ecology-first approach. Though the highlighted systems are diverse, unifying themes are (1) to understand how microbes interact with their host or environment, (2) to gain insights into the environmental roles of microbial metabolites, and (3) to explore pharmaceutical potential from these ecologically relevant metabolites.

Water-Soluble Glutamic Acid Derivatives Produced in Culture by Penicillium solitum IS1-A from King George Island, Maritime Antarctica

A new method of screening was developed to generate 770 organic and water-soluble fractions from extracts of nine species of marine sponges, from the growth media of 18 species of marine-derived fungi, and from the growth media of 13 species of endophytic fungi. The screening results indicated that water-soluble fractions displayed significant bioactivity in cytotoxic, antibiotic, anti-Leishmania, anti-Trypanosoma cruzi, and inhibition of proteasome assays. Purification of water-soluble fractions from the growth medium of Penicillium solitum IS1-A provided the new glutamic acid derivatives solitumine A (1), solitumine B (2), and solitumidines A-D (3-6). The structures of compounds 1-6 have been established by analysis of spectroscopic data, chemical derivatizations, and vibrational circular dichroism calculations. Although no biological activity could be observed for compounds 1-6, the new structures reported for 1-6 indicate that the investigation of water-soluble natural products represents a relevant strategy in finding new secondary metabolites.

Molecular networking-based for the target discovery of potent antiproliferative polycyclic macrolactam ansamycins from Streptomyces cacaoi subsp. asoensis

Molecular networking-based for the target discovery of potent antiproliferative polycyclic macrolactam ansamycins.

Approaches for the isolation and identification of hydrophilic, light-sensitive, volatile and minor natural products

Water-soluble, volatile, minor and photosensitive natural products are yet poorly known, and this review discusses the literature reporting the isolation strategies for some of these metabolites.

Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products

This is a new golden age for drug discovery based on natural products derived from both marine and terrestrial sources. Herein, a straightforward but important question is "what are the major structural differences between marine natural products (MNPs) and terrestrial natural products (TNPs)?" To answer this question, we analyzed the important physicochemical properties, structural features, and drug-likeness of the two types of natural products and discussed their differences from the perspective of evolution. In general, MNPs have lower solubility and are often larger than TNPs. On average, particularly from the perspective of unique fragments and scaffolds, MNPs usually possess more long chains and large rings, especially 8- to 10-membered rings. MNPs also have more nitrogen atoms and halogens, notably bromines, and fewer oxygen atoms, suggesting that MNPs may be synthesized by more diverse biosynthetic pathways than TNPs. Analysis of the frequently occurring Murcko frameworks in MNPs and TNPS also reveals a striking difference between MNPs and TNPs. The scaffolds of the former tend to be longer and often contain ester bonds connected to 10-membered rings, while the scaffolds of the latter tend to be shorter and often bear more stable ring systems and bond types. Besides, the prediction from the naïve Bayesian drug-likeness classification model suggests that most compounds in MNPs and TNPs are drug-like, although MNPs are slightly more drug-like than TNPs. We believe that MNPs and TNPs with novel drug-like scaffolds have great potential to be drug leads or drug candidates in drug discovery campaigns.

Searching for original natural products by molecular networking: detection, isolation and total synthesis of chloroaustralasines

Four unprecedented chlorinated monoterpenyl quinolones have been isolated using a molecular networking based prioritisation strategy. The bio-inspired total synthesis of chloroaustralasine A involving a chloroperoxydase-mediated hydroxychlorination is described.

Retrospective analysis of natural products provides insights for future discovery trends

Understanding of the capacity of the natural world to produce secondary metabolites is important to a broad range of fields, including drug discovery, ecology, biosynthesis, and chemical biology, among others. Both the absolute number and the rate of discovery of natural products have increased significantly in recent years. However, there is a perception and concern that the fundamental novelty of these discoveries is decreasing relative to previously known natural products. This study presents a quantitative examination of the field from the perspective of both number of compounds and compound novelty using a dataset of all published microbial and marine-derived natural products. This analysis aimed to explore a number of key questions, such as how the rate of discovery of new natural products has changed over the past decades, how the average natural product structural novelty has changed as a function of time, whether exploring novel taxonomic space affords an advantage in terms of novel compound discovery, and whether it is possible to estimate how close we are to having described all of the chemical space covered by natural products. Our analyses demonstrate that most natural products being published today bear structural similarity to previously published compounds, and that the range of scaffolds readily accessible from nature is limited. However, the analysis also shows that the field continues to discover appreciable numbers of natural products with no structural precedent. Together, these results suggest that the development of innovative discovery methods will continue to yield compounds with unique structural and biological properties.

Why Polyphenols have Promiscuous Actions? An Investigation by Chemical Bioinformatics

Despite their diverse pharmacological effects, polyphenols are poor for use as drugs, which have been traditionally ascribed to their low bioavailability. However, Baell and co-workers recently proposed that the redox potential of polyphenols also plays an important role in this, because redox reactions bring promiscuous actions on various protein targets and thus produce non-specific pharmacological effects. To investigate whether the redox reactivity behaves as a critical factor in polyphenol promiscuity, we performed a chemical bioinformatics analysis on the structure-activity relationships of twenty polyphenols. It was found that the gene expression profiles of human cell lines induced by polyphenols were not correlated with the presence or not of redox moieties in the polyphenols, but significantly correlated with their molecular structures. Therefore, it is concluded that the promiscuous actions of polyphenols are likely to result from their inherent structural features rather than their redox potential.

Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

It is widely accepted that drug discovery often requires a systems-level polypharmacology approach to tackle problems such as lack of efficacy and emerging resistance of single-targeted compounds. Network pharmacology approaches are increasingly being developed and applied to find new therapeutic opportunities and to re-purpose approved drugs. However, these recent advances have been relatively slow to be translated into the field of natural products. Here, we argue that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases. We give an overview of the key network pharmacology concepts and recent experimental-computational approaches that have been successfully applied to natural product research, including unbiased elucidation of mechanisms of action as well as systematic prediction of effective therapeutic combinations. We focus specifically on anticancer applications that use in vivo and in vitro functional phenotypic measurements, such as genome-wide transcriptomic response profiles, which enable a global modelling of the multi-target activity at the level of the biological pathways and interaction networks. We also provide representative examples of other disease applications, databases and tools as well as existing and emerging resources, which may prove useful for future natural product research. Finally, we offer our personal view of the current limitations, prospective developments and open questions in this exciting field.

The impact of oxygen on metabolic evolution: a chemoinformatic investigation

The appearance of planetary oxygen likely transformed the chemical and biochemical makeup of life and probably triggered episodes of organismal diversification. Here we use chemoinformatic methods to explore the impact of the rise of oxygen on metabolic evolution. We undertake a comprehensive comparative analysis of structures, chemical properties and chemical reactions of anaerobic and aerobic metabolites. The results indicate that aerobic metabolism has expanded the structural and chemical space of metabolites considerably, including the appearance of 130 novel molecular scaffolds. The molecular functions of these metabolites are mainly associated with derived aspects of cellular life, such as signal transfer, defense against biotic factors, and protection of organisms from oxidation. Moreover, aerobic metabolites are more hydrophobic and rigid than anaerobic compounds, suggesting they are better fit to modulate membrane functions and to serve as transmembrane signaling factors. Since higher organisms depend largely on sophisticated membrane-enabled functions and intercellular signaling systems, the metabolic developments brought about by oxygen benefit the diversity of cellular makeup and the complexity of cellular organization as well. These findings enhance our understanding of the molecular link between oxygen and evolution. They also show the significance of chemoinformatics in addressing basic biological questions.

Elucidating the link between the rise of planetary oxygen and biological evolution is a challenging topic in evolutionary biology. Previous studies in this area were dominated by biological investigations. The recent simulations of metabolic networks under anaerobic or aerobic conditions revealed that aerobic metabolism gave rise to 1,000+ new reactions. Since metabolites are small molecules and metabolic reactions are basically chemical reactions, we think that the impact of oxygen on metabolic evolution can be well studied by chemoinformatics. In this paper, we use chemoinformatic methods to perform a comprehensive comparative analysis of the chemical structures, properties and reactions of anaerobic and aerobic metabolites. It was found that aerobic metabolism has considerably expanded the structural space of metabolites by inventing 130 novel molecular scaffolds. Moreover, aerobic metabolism also helped organisms to explore a new chemical space by increasing the hydrophobicity and rigidity of metabolites. Since hydrophobic metabolites are fitting to modulate membrane functions and to serve as transmembrane signaling factors, these metabolic innovations definitely benefit the establishment of complex cellular organizations. The present findings not only help to understand the molecular link between oxygen and evolution but also suggest that chemoinformatics is of special value in addressing some basic biological questions.