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Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides

Author

Listed:
  • Amir Pandi

    (Max Planck Institute for Terrestrial Microbiology)

  • David Adam

    (Max Planck Institute for Terrestrial Microbiology
    Bundeswehr Institute of Microbiology)

  • Amir Zare

    (Max Planck Institute for Terrestrial Microbiology)

  • Van Tuan Trinh

    (Philipps-University Marburg)

  • Stefan L. Schaefer

    (Max Planck Institute of Biophysics)

  • Marie Burt

    (Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL))

  • Björn Klabunde

    (Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL))

  • Elizaveta Bobkova

    (Max Planck Institute for Terrestrial Microbiology)

  • Manish Kushwaha

    (Micalis Institute)

  • Yeganeh Foroughijabbari

    (Max Planck Institute for Terrestrial Microbiology)

  • Peter Braun

    (Bundeswehr Institute of Microbiology
    German Center for Infection Research (DZIF)
    Infection and Pandemic Research)

  • Christoph Spahn

    (Max Planck Institute for Terrestrial Microbiology)

  • Christian Preußer

    (Philipps-University Marburg
    Philipps-University of Marburg)

  • Elke Pogge von Strandmann

    (Philipps-University Marburg
    Philipps-University of Marburg)

  • Helge B. Bode

    (Max Planck Institute for Terrestrial Microbiology
    Goethe University Frankfurt
    Philipps-University Marburg
    Senckenberg Gesellschaft für Naturforschung)

  • Heiner Buttlar

    (Bundeswehr Institute of Microbiology
    German Center for Infection Research (DZIF))

  • Wilhelm Bertrams

    (Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL))

  • Anna Lena Jung

    (Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL)
    Philipps-University Marburg)

  • Frank Abendroth

    (Philipps-University Marburg)

  • Bernd Schmeck

    (Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL)
    SYNMIKRO Center of Synthetic Microbiology
    Philipps-University Marburg
    University Medical Center Marburg, Philipps-University Marburg)

  • Gerhard Hummer

    (Max Planck Institute of Biophysics
    Goethe University Frankfurt)

  • Olalla Vázquez

    (Philipps-University Marburg
    SYNMIKRO Center of Synthetic Microbiology)

  • Tobias J. Erb

    (Max Planck Institute for Terrestrial Microbiology
    SYNMIKRO Center of Synthetic Microbiology)

Abstract

Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost. Here, we established a cell-free protein synthesis (CFPS) pipeline for the rapid and inexpensive production of antimicrobial peptides (AMPs) directly from DNA templates. To validate our platform, we used deep learning to design thousands of AMPs de novo. Using computational methods, we prioritized 500 candidates that we produced and screened with our CFPS pipeline. We identified 30 functional AMPs, which we characterized further through molecular dynamics simulations, antimicrobial activity and toxicity. Notably, six de novo-AMPs feature broad-spectrum activity against multidrug-resistant pathogens and do not develop bacterial resistance. Our work demonstrates the potential of CFPS for high throughput and low-cost production and testing of bioactive peptides within less than 24 h.

Suggested Citation

  • Amir Pandi & David Adam & Amir Zare & Van Tuan Trinh & Stefan L. Schaefer & Marie Burt & Björn Klabunde & Elizaveta Bobkova & Manish Kushwaha & Yeganeh Foroughijabbari & Peter Braun & Christoph Spahn , 2023. "Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42434-9
    DOI: 10.1038/s41467-023-42434-9
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    References listed on IDEAS

    as
    1. Paulina Szymczak & Marcin Możejko & Tomasz Grzegorzek & Radosław Jurczak & Marta Bauer & Damian Neubauer & Karol Sikora & Michał Michalski & Jacek Sroka & Piotr Setny & Wojciech Kamysz & Ewa Szczurek, 2023. "Discovering highly potent antimicrobial peptides with deep generative model HydrAMP," Nature Communications, Nature, vol. 14(1), pages 1-23, December.
    2. Paulina Szymczak & Marcin Możejko & Tomasz Grzegorzek & Radosław Jurczak & Marta Bauer & Damian Neubauer & Karol Sikora & Michał Michalski & Jacek Sroka & Piotr Setny & Wojciech Kamysz & Ewa Szczurek, 2023. "Author Correction: Discovering highly potent antimicrobial peptides with deep generative model HydrAMP," Nature Communications, Nature, vol. 14(1), pages 1-1, December.
    3. Noelia Ferruz & Steffen Schmidt & Birte Höcker, 2022. "ProtGPT2 is a deep unsupervised language model for protein design," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Po-Ssu Huang & Scott E. Boyken & David Baker, 2016. "The coming of age of de novo protein design," Nature, Nature, vol. 537(7620), pages 320-327, September.
    5. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    6. Alex Hawkins-Hooker & Florence Depardieu & Sebastien Baur & Guillaume Couairon & Arthur Chen & David Bikard, 2021. "Generating functional protein variants with variational autoencoders," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-23, February.
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    Cited by:

    1. Wan-Qiu Liu & Xiangyang Ji & Fang Ba & Yufei Zhang & Huiling Xu & Shuhui Huang & Xiao Zheng & Yifan Liu & Shengjie Ling & Michael C. Jewett & Jian Li, 2024. "Cell-free biosynthesis and engineering of ribosomally synthesized lanthipeptides," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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