HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search

Yi-Yuan Lee, Mustafa Guler, Desnor N. Chigumba, Shen Wang, Neel Mittal, Cameron Miller, Benjamin Krummenacher, Haodong Liu, Liu Cao, Aditya Kannan, Keshav Narayan, Samuel T. Slocum, Bryan L. Roth, Alexey Gurevich, Bahar Behsaz, Roland D. Kersten and Hosein Mohimani ()
Additional contact information
Yi-Yuan Lee: Carnegie Mellon University
Mustafa Guler: Carnegie Mellon University
Desnor N. Chigumba: University of Michigan
Shen Wang: Carnegie Mellon University
Neel Mittal: Carnegie Mellon University
Cameron Miller: Carnegie Mellon University
Benjamin Krummenacher: Carnegie Mellon University
Haodong Liu: Carnegie Mellon University
Liu Cao: Carnegie Mellon University
Aditya Kannan: Carnegie Mellon University
Keshav Narayan: Carnegie Mellon University
Samuel T. Slocum: University of North Carolina
Bryan L. Roth: University of North Carolina
Alexey Gurevich: Helmholtz Centre for Infection Research
Bahar Behsaz: Carnegie Mellon University
Roland D. Kersten: University of Michigan
Hosein Mohimani: Carnegie Mellon University

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

Date: 2023
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DOI: 10.1038/s41467-023-39905-4

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