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Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS

Author

Listed:
  • Donghui Yan

    (Carnegie Mellon University)

  • Muqing Zhou

    (Carnegie Mellon University)

  • Abhinav Adduri

    (Carnegie Mellon University)

  • Yihao Zhuang

    (University of Michigan)

  • Mustafa Guler

    (Carnegie Mellon University)

  • Sitong Liu

    (Carnegie Mellon University)

  • Hyonyoung Shin

    (Carnegie Mellon University)

  • Torin Kovach

    (Carnegie Mellon University)

  • Gloria Oh

    (Carnegie Mellon University)

  • Xiao Liu

    (Carnegie Mellon University)

  • Yuting Deng

    (Carnegie Mellon University)

  • Xiaofeng Wang

    (University of Michigan)

  • Liu Cao

    (Carnegie Mellon University)

  • David H. Sherman

    (University of Michigan
    University of Michigan)

  • Pamela J. Schultz

    (University of Michigan
    University of Michigan)

  • Roland D. Kersten

    (University of Michigan)

  • Jason A. Clement

    (Baruch S. Blumberg Institute)

  • Ashootosh Tripathi

    (University of Michigan
    University of Michigan
    University of Michigan)

  • Bahar Behsaz

    (Carnegie Mellon University
    Chemia Biosciences Inc)

  • Hosein Mohimani

    (Carnegie Mellon University)

Abstract

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49587-1
    DOI: 10.1038/s41467-024-49587-1
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    References listed on IDEAS

    as
    1. Chad W. Johnston & Michael A. Skinnider & Morgan A. Wyatt & Xiang Li & Michael R. M. Ranieri & Lian Yang & David L. Zechel & Bin Ma & Nathan A. Magarvey, 2015. "An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products," Nature Communications, Nature, vol. 6(1), pages 1-11, December.
    2. 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.
    3. 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 , 2023. "HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Hosein Mohimani & Alexey Gurevich & Alexander Shlemov & Alla Mikheenko & Anton Korobeynikov & Liu Cao & Egor Shcherbin & Louis-Felix Nothias & Pieter C. Dorrestein & Pavel A. Pevzner, 2018. "Dereplication of microbial metabolites through database search of mass spectra," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    5. Michael A. Skinnider & Chad W. Johnston & Mathusan Gunabalasingam & Nishanth J. Merwin & Agata M. Kieliszek & Robyn J. MacLellan & Haoxin Li & Michael R. M. Ranieri & Andrew L. H. Webster & My P. T. C, 2020. "Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    6. 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. "Publisher Correction: Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
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