IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-42434-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-42434-9
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-42434-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Agnese I. Curatolo & Ofer Kimchi & Carl P. Goodrich & Ryan K. Krueger & Michael P. Brenner, 2023. "A computational toolbox for the assembly yield of complex and heterogeneous structures," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Biao Ruan & Yanan He & Yingwei Chen & Eun Jung Choi & Yihong Chen & Dana Motabar & Tsega Solomon & Richard Simmerman & Thomas Kauffman & D. Travis Gallagher & John Orban & Philip N. Bryan, 2023. "Design and characterization of a protein fold switching network," Nature Communications, Nature, vol. 14(1), pages 1-14, 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. Kevin E. Wu & Kevin K. Yang & Rianne Berg & Sarah Alamdari & James Y. Zou & Alex X. Lu & Ava P. Amini, 2024. "Protein structure generation via folding diffusion," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Wenwu Zeng & Yutao Dou & Liangrui Pan & Liwen Xu & Shaoliang Peng, 2024. "Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Chase R. Freschlin & Sarah A. Fahlberg & Pete Heinzelman & Philip A. Romero, 2024. "Neural network extrapolation to distant regions of the protein fitness landscape," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    7. Sijie Chen & Tong Lin & Ruchira Basu & Jeremy Ritchey & Shen Wang & Yichuan Luo & Xingcan Li & Dehua Pei & Levent Burak Kara & Xiaolin Cheng, 2024. "Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    8. Cheyenne Ziegler & Jonathan Martin & Claude Sinner & Faruck Morcos, 2023. "Latent generative landscapes as maps of functional diversity in protein sequence space," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    9. Erika Erickson & Japheth E. Gado & Luisana Avilán & Felicia Bratti & Richard K. Brizendine & Paul A. Cox & Raj Gill & Rosie Graham & Dong-Jin Kim & Gerhard König & William E. Michener & Saroj Poudel &, 2022. "Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    10. David Ding & Ada Y. Shaw & Sam Sinai & Nathan Rollins & Noam Prywes & David F. Savage & Michael T. Laub & Debora S. Marks, 2024. "Protein design using structure-based residue preferences," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    11. Shunshi Kohyama & Béla P. Frohn & Leon Babl & Petra Schwille, 2024. "Machine learning-aided design and screening of an emergent protein function in synthetic cells," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    12. Shuangjia Zheng & Tao Zeng & Chengtao Li & Binghong Chen & Connor W. Coley & Yuedong Yang & Ruibo Wu, 2022. "Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    13. Pierre Azoulay & Joshua Krieger & Abhishek Nagaraj, 2024. "Old Moats for New Models: Openness, Control, and Competition in Generative AI," NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4, National Bureau of Economic Research, Inc.
    14. Anthony C. Bishop & Glorisé Torres-Montalvo & Sravya Kotaru & Kyle Mimun & A. Joshua Wand, 2023. "Robust automated backbone triple resonance NMR assignments of proteins using Bayesian-based simulated annealing," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    15. Deyun Qiu & Jinxin V. Pei & James E. O. Rosling & Vandana Thathy & Dongdi Li & Yi Xue & John D. Tanner & Jocelyn Sietsma Penington & Yi Tong Vincent Aw & Jessica Yi Han Aw & Guoyue Xu & Abhai K. Tripa, 2022. "A G358S mutation in the Plasmodium falciparum Na+ pump PfATP4 confers clinically-relevant resistance to cipargamin," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    16. Shuo-Shuo Liu & Tian-Xia Jiang & Fan Bu & Ji-Lan Zhao & Guang-Fei Wang & Guo-Heng Yang & Jie-Yan Kong & Yun-Fan Qie & Pei Wen & Li-Bin Fan & Ning-Ning Li & Ning Gao & Xiao-Bo Qiu, 2024. "Molecular mechanisms underlying the BIRC6-mediated regulation of apoptosis and autophagy," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    17. Dick Schijven & Sourena Soheili-Nezhad & Simon E. Fisher & Clyde Francks, 2024. "Exome-wide analysis implicates rare protein-altering variants in human handedness," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    18. Xiaoke Yang & Mingqi Zhu & Xue Lu & Yuxin Wang & Junyu Xiao, 2024. "Architecture and activation of human muscle phosphorylase kinase," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    19. Zheng Shen & Daxiao Sun & Adriana Savastano & Sára Joana Varga & Maria-Sol Cima-Omori & Stefan Becker & Alf Honigmann & Markus Zweckstetter, 2023. "Multivalent Tau/PSD-95 interactions arrest in vitro condensates and clusters mimicking the postsynaptic density," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    20. Evangelos Katsamakas & Oleg V. Pavlov & Ryan Saklad, 2024. "Artificial intelligence and the transformation of higher education institutions," Papers 2402.08143, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42434-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.