Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Bahar Behsaz, Edna Bode, Alexey Gurevich, Yan-Ni Shi, Florian Grundmann, Deepa Acharya, Andrés Mauricio Caraballo-Rodríguez, Amina Bouslimani, Morgan Panitchpakdi, Annabell Linck, Changhui Guan, Julia Oh, Pieter C. Dorrestein, Helge B. Bode (), Pavel A. Pevzner () and Hosein Mohimani ()
Additional contact information
Bahar Behsaz: University of California San Diego
Edna Bode: Goethe University Frankfurt
Alexey Gurevich: St. Petersburg State University
Yan-Ni Shi: Goethe University Frankfurt
Florian Grundmann: Goethe University Frankfurt
Deepa Acharya: University of Wisconsin–Madison
Andrés Mauricio Caraballo-Rodríguez: University of California San Diego
Amina Bouslimani: University of California San Diego
Morgan Panitchpakdi: University of California San Diego
Annabell Linck: Goethe University Frankfurt
Changhui Guan: The Jackson Laboratory of Medical Genomics
Julia Oh: The Jackson Laboratory of Medical Genomics
Pieter C. Dorrestein: University of California at San Diego
Helge B. Bode: Goethe University Frankfurt
Pavel A. Pevzner: University of California San Diego
Hosein Mohimani: Carnegie Mellon University

Nature Communications, 2021, vol. 12, issue 1, 1-17

Abstract: Abstract Non-Ribosomal Peptides (NRPs) represent a biomedically important class of natural products that include a multitude of antibiotics and other clinically used drugs. NRPs are not directly encoded in the genome but are instead produced by metabolic pathways encoded by biosynthetic gene clusters (BGCs). Since the existing genome mining tools predict many putative NRPs synthesized by a given BGC, it remains unclear which of these putative NRPs are correct and how to identify post-assembly modifications of amino acids in these NRPs in a blind mode, without knowing which modifications exist in the sample. To address this challenge, here we report NRPminer, a modification-tolerant tool for NRP discovery from large (meta)genomic and mass spectrometry datasets. We show that NRPminer is able to identify many NRPs from different environments, including four previously unreported NRP families from soil-associated microbes and NRPs from human microbiota. Furthermore, in this work we demonstrate the anti-parasitic activities and the structure of two of these NRP families using direct bioactivity screening and nuclear magnetic resonance spectrometry, illustrating the power of NRPminer for discovering bioactive NRPs.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23502-4

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DOI: 10.1038/s41467-021-23502-4

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