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Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Author

Listed:
  • Bahar Behsaz

    (University of California San Diego
    University of California at San Diego
    Carnegie Mellon University)

  • 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
    University of California San Diego)

  • Helge B. Bode

    (Goethe University Frankfurt
    Goethe University Frankfurt & Senckenberg Research Institute
    Department for Natural Products in Organismic Interactions)

  • Pavel A. Pevzner

    (University of California San Diego)

  • Hosein Mohimani

    (Carnegie Mellon University)

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.

Suggested Citation

  • Bahar Behsaz & Edna Bode & Alexey Gurevich & Yan-Ni Shi & Florian Grundmann & Deepa Acharya & Andrés Mauricio Caraballo-Rodríguez & Amina Bouslimani & Morgan Panitchpakdi & Annabell Linck & Changhui G, 2021. "Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23502-4
    DOI: 10.1038/s41467-021-23502-4
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    Cited by:

    1. Donghui Yan & Muqing Zhou & Abhinav Adduri & Yihao Zhuang & Mustafa Guler & Sitong Liu & Hyonyoung Shin & Torin Kovach & Gloria Oh & Xiao Liu & Yuting Deng & Xiaofeng Wang & Liu Cao & David H. Sherman, 2024. "Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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