An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products

Chad W. Johnston, Michael A. Skinnider, Morgan A. Wyatt, Xiang Li, Michael R. M. Ranieri, Lian Yang, David L. Zechel, Bin Ma and Nathan A. Magarvey ()
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Chad W. Johnston: M. G. DeGroote Institute for Infectious Disease Research
Michael A. Skinnider: M. G. DeGroote Institute for Infectious Disease Research
Morgan A. Wyatt: M. G. DeGroote Institute for Infectious Disease Research
Xiang Li: M. G. DeGroote Institute for Infectious Disease Research
Michael R. M. Ranieri: M. G. DeGroote Institute for Infectious Disease Research
Lian Yang: The David R. Cheriton School of Computer Science, University of Waterloo
David L. Zechel: Department of Chemistry; Queens University
Bin Ma: The David R. Cheriton School of Computer Science, University of Waterloo
Nathan A. Magarvey: M. G. DeGroote Institute for Infectious Disease Research

Nature Communications, 2015, vol. 6, issue 1, 1-11

Abstract: Abstract Bacterial natural products are a diverse and valuable group of small molecules, and genome sequencing indicates that the vast majority remain undiscovered. The prediction of natural product structures from biosynthetic assembly lines can facilitate their discovery, but highly automated, accurate, and integrated systems are required to mine the broad spectrum of sequenced bacterial genomes. Here we present a genome-guided natural products discovery tool to automatically predict, combinatorialize and identify polyketides and nonribosomal peptides from biosynthetic assembly lines using LC–MS/MS data of crude extracts in a high-throughput manner. We detail the directed identification and isolation of six genetically predicted polyketides and nonribosomal peptides using our Genome-to-Natural Products platform. This highly automated, user-friendly programme provides a means of realizing the potential of genetically encoded natural products.

Date: 2015
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DOI: 10.1038/ncomms9421

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