Existing tools can generate predictions of genomically encoded natural antibiotic structures from small regions of this vast biosynthetic space 7, 10, but a comprehensive platform is lacking. Moreover, these catalysts are arranged in multi-gene clusters that can be categorized into dozens of distinct families. The challenges inherent to the latter task far exceed those involved in genome annotation: nature employs a dizzying array of enzymatic catalysts to construct structurally complex molecules from simple building blocks. However, whereas a plethora of methods are available to identify the genomic loci responsible for natural antibiotic biosynthesis 7, 8, 9, few tools exist to link these loci to the specific chemical structures of their encoded products. 1), methods to leverage this data towards antibiotic discovery are urgently needed. With the amount of microbial genome sequence information deposited in public databases continuing to increase at an exponential rate (Supplementary Fig. Directed discovery of these unknown antibiotics, guided by genome sequencing data, could provide a means to address the growing clinical need for new antibiotics to combat drug-resistant pathogens 6. These pathways are encoded within the genomes of the producing organisms, and comparative genomics studies have suggested a wealth of novel antibiotics encoded in the genomes of both culturable and unculturable organisms that remain to be discovered 3, 4, 5. The biosynthetic pathways responsible for the production of these molecules have been honed over long evolutionary time scales in order to provide microbes with competitive advantages in their natural environments 2. The overwhelming majority of antibiotics currently in clinical use are derived from naturally occurring small molecules produced by microbes 1. PRISM 4 is freely available as an interactive web application at. 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. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of 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. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance.
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