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PreprintCited by 173Antimicrobial Peptides and ActivitiesChemical Synthesis and AnalysisMicrobial Natural Products and Biosynthesis

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

Open access
Pandi, Amir, Adam, David, Zare, Amir, Trinh, Van Tuan, Schaefer, Stefan L., Wiegand, Marie, Klabunde, Björn, Bobkova, Elizaveta, Kushwaha, Manish, Foroughijabbari, Yeganeh, Braun, Peter, Spahn, Christoph, Preußer, Christian, von Strandmann, Elke Pogge, Bode, Helge B., von Buttlar, Heiner, Bertrams, Wilhelm, Jung, Anna Lena, Abendroth, Frank, Schmeck, Bernd, Hummer, Gerhard, Vázquez, Olalla, Erb, Tobias J.·2022·Nature Communications
DOI 10.1101/2022.11.19.517184PMC PMC10632401

Preprint — published as 10.1038/s41467-023-42434-9 · Paper page

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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 production and testing of bioactive peptides within less than 24 hours and <10$ per screen.

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Metadata

Authors
Pandi, A., Adam, D., Zare, A., Trinh, V. T., Schaefer, S. L., Wiegand, M., Klabunde, B., Bobkova, E., Kushwaha, M., Foroughijabbari, Y., Braun, P., Spahn, C., Preußer, C., von Strandmann, E. P., Bode, H. B., von Buttlar, H., Bertrams, W., Jung, A. L., Abendroth, F., Schmeck, B., Hummer, G., Vázquez, O., & Erb, T. J.
Affiliations
  • Max Planck Institute for Terrestrial Microbiology (DE)
  • Philipps University of Marburg (DE)
  • Max Planck Institute of Biophysics (DE)
  • AgroParisTech (FR)
  • Fraunhofer Institute for Translational Medicine and Pharmacology (DE)
  • Goethe University Frankfurt (DE)
  • German Center for Infection Research (DE)
Corresponding author
  • Pandi, Amir
  • Erb, Tobias J.
ORCID
Venue
Nature Communications
Publisher
openRxiv
Published
2022-11-19
Volume
14
Issue
1
Pages
7197-7197
ISSN
2041-1723 (ISSN-L)
DOI
10.1101/2022.11.19.517184
PMC
PMC10632401
OpenAlex
W4388492286
Cited by
173
References
2
Citations by year
2026: 54 · 2025: 67 · 2024: 50 · 2023: 2
FWCI
31.50
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
Funders
  • Gordon and Betty Moore Foundation (GBMF10652)
  • European Molecular Biology Organization (ALTG 165-2020)
  • Deutsche Forschungsgemeinschaft (423428279, TRR81/3 Z04, 109546710, SFB/TR84 TP C01, AB 792/1-1, TP C01, SFB‐TR84, TRR81/3, TRR81, TR-84)
  • Agence Nationale de la Recherche (ANR-21-CE48-0003, ANR21CE48003, ANR-20-BiopNSE)
  • Bundesministerium für Bildung und Forschung (FKZ 031L0140, TP C01, 031L0140, 01EK2203A)
  • Hessisches Ministerium für Wissenschaft und Kunst
  • Max-Planck-Gesellschaft
  • Université Paris-Saclay
Topics
  • Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
  • Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Microbial Natural Products and Biosynthesis
Keywords
antimicrobial peptides, cell-free protein synthesis, de novo peptide design, deep learning, bioactive peptides, multidrug-resistant pathogens, broad-spectrum antimicrobial activity, peptide toxicity, molecular dynamics simulation
MeSH terms
Deep Learning, Antimicrobial Peptides, DNA Replication, Protein Biosynthesis, Molecular Dynamics Simulation

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