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.

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Towards the sustainable discovery and development of new antibiotics

Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other in silico applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at https://coconut.naturalproducts.net
 .

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COCONUT online: Collection of Open Natural Products database

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com
 . Large-scale sequencing efforts have uncovered a large number of secondary metabolic pathways, but the chemicals they synthesise remain unknown. Here the authors present PRISM 4, which predicts the chemical structures encoded by microbial genome sequences, including all classes of bacterial antibiotics in clinical use.

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Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences.

Computational analysis of biosynthetic gene clusters (BGCs) has revolutionized natural product discovery by enabling the rapid investigation of secondary metabolic potential within microbial genome sequences. Grouping homologous BGCs into Gene Cluster Families (GCFs) facilitates mapping their architectural and taxonomic diversity and provides insights into the novelty of putative BGCs, through dereplication with BGCs of known function. While multiple databases exist for exploring BGCs from publicly available data, no public resources exist that focus on GCF relationships. Here, we present BiG-FAM, a database of 29,955 GCFs capturing the global diversity of 1,225,071 BGCs predicted from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs). The database offers rich functionalities, such as multi-criterion GCF searches, direct links to BGC databases such as antiSMASH-DB, and rapid GCF annotation of user-supplied BGCs from antiSMASH results. BiG-FAM can be accessed online at https://bigfam.bioinformatics.nl.

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BiG-FAM : the biosynthetic gene cluster families database

Multi-drug resistant pathogens have become a major threat to human health and new antibiotics are urgently needed. Most antibiotics are derived from secondary metabolites produced by bacteria. In order to avoid suicide, these bacteria usually encode resistance genes, in some cases within the biosynthetic gene cluster (BGC) of the respective antibiotic compound. Modern genome mining tools enable researchers to computationally detect and predict BGCs that encode the biosynthesis of secondary metabolites. The major challenge now is the prioritization of the most promising BGCs encoding antibiotics with novel modes of action. A recently developed target-directed genome mining approach allows researchers to predict the mode of action of the encoded compound of an uncharacterized BGC based on the presence of resistant target genes. In 2017, we introduced the 'Antibiotic Resistant Target Seeker' (ARTS). ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets by rapidly linking housekeeping and known resistance genes to BGC proximity, duplication and horizontal gene transfer (HGT) events. Here, we present ARTS 2.0 available at http://arts.ziemertlab.com. ARTS 2.0 now includes options for automated target directed genome mining in all bacterial taxa as well as metagenomic data. Furthermore, it enables comparison of similar BGCs from different genomes and their putative resistance genes.

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ARTS 2.0: feature updates and expansion of the Antibiotic Resistant Target Seeker for comparative genome mining.

Despite rapid evolution in the area of microbial natural products chemistry, there is currently no open access database containing all microbially produced natural product structures. Lack of availability of these data is preventing the implementation of new technologies in natural products science. Specifically, development of new computational strategies for compound characterization and identification are being hampered by the lack of a comprehensive database of known compounds against which to compare experimental data. The creation of an open access, community-maintained database of microbial natural product structures would enable the development of new technologies in natural products discovery and improve the interoperability of existing natural products data resources. However, these data are spread unevenly throughout the historical scientific literature, including both journal articles and international patents. These documents have no standard format, are often not digitized as machine readable text, and are not publicly available. Further, none of these documents have associated structure files (e.g., MOL, InChI, or SMILES), instead containing images of structures. This makes extraction and formatting of relevant natural products data a formidable challenge. Using a combination of manual curation and automated data mining approaches we have created a database of microbial natural products (The Natural Products Atlas, www.npatlas.org) that includes 24 594 compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. This database is accompanied by an interactive web portal that permits searching by structure, substructure, and physical properties. The Web site also provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. These interactive tools offer a powerful knowledge base for natural products discovery with a central interface for structure and property-based searching and presents new viewpoints on structural diversity in natural products. The Natural Products Atlas has been developed under FAIR principles (Findable, Accessible, Interoperable, and Reusable) and is integrated with other emerging natural product databases, including the Minimum Information About a Biosynthetic Gene Cluster (MIBiG) repository, and the Global Natural Products Social Molecular Networking (GNPS) platform. It is designed as a community-supported resource to provide a central repository for known natural product structures from microorganisms and is the first comprehensive, open access resource of this type. It is expected that the Natural Products Atlas will enable the development of new natural products discovery modalities and accelerate the process of structural characterization for complex natural products libraries.

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The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery.

Yellowjackets in the genera Vespula and Dolichovespula are prevalent eusocial insects of great ecological and economic significance, but the chemical signals of their sexual communication systems have defied structural elucidation. Herein, we report the identification of sex attractant pheromone components of virgin bald-faced hornet queens (Dolichovespula maculata). We analyzed body surface extracts of queens by coupled gas chromatographic-electroantennographic detection (GC-EAD), isolated the compounds that elicited responses from male antennae by high-performance liquid chromatography (HPLC), and identified these components by GC mass spectrometry (MS), HPLC-MS, and NMR spectroscopy. In laboratory olfactometer experiments, synthetic (2Z,7E)-3,7-dimethyldeca-2,7-diendioic acid (termed here maculatic acid A) and (2Z,7E)-10-methoxy-3,7-dimethyldeca-10-oxo-deca-2,7-dienoic acid (termed here maculatic acid C) in binary combination significantly attracted bald-faced hornet males. These are the first sex attractant pheromone components identified in yellowjackets.

Maculatic Acids—Sex Attractant Pheromone Components of Bald-Faced Hornets

As part of an ongoing program to identify sex attractant pheromone components that mediate sexual communication in yellowjacket wasps, a novel sesquiterpene was isolated from body surface extracts of virgin bald-faced hornet queens, Dolichovespula maculata. The gross structure of this sesquiterpene was proposed through microscale spectroscopic analyses, and the configuration of the central olefin was subsequently confirmed by total synthesis. This new natural product (termed here dolichovespulide) represents an important addition to the relatively small number of terpenoids reported from the taxonomic insect family Vespidae.

Catherine McCaughey

Papers

The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery.

Maculatic Acids—Sex Attractant Pheromone Components of Bald-Faced Hornets

Isolation, Structure Elucidation, and Total Synthesis of Dolichovespulide, a Sesquiterpene from Dolichovespula Yellowjackets.