Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides
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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 high throughput and low-cost production and testing of bioactive peptides within less than 24 h.
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- Authors
- Pandi, A., Adam, D., Zare, A., Trinh, V. T., Schaefer, S. L., Burt, M., Klabunde, B., Bobkova, E., Kushwaha, M., Foroughijabbari, Y., Braun, P., Spahn, C., Preußer, C., Pogge von Strandmann, E., 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)
- Corresponding author
- Pandi, Amir
- Erb, Tobias J.
- ORCID
- Venue
- Nature Communications
- Published
- 2023-11-08
- Volume
- 14
- Issue
- 1
- Pages
- 7197-7197
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10632401
- OpenAlex
- W4388492286
- Cited by
- 173
- References
- 69
- Citations by year
- 2026: 54 · 2025: 67 · 2024: 50 · 2023: 2
- FWCI
- 31.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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
- Max-Planck-Gesellschaft
- European Molecular Biology Organization (ALTG 165-2020)
- Topics
- Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Health Sciences › Medicine › Pharmacology › 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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