Bibliographic Details
| Title: |
Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides |
| Authors: |
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, Christian Preußer, Elke Pogge von Strandmann, Helge B. Bode, Heiner von Buttlar, Wilhelm Bertrams, Anna Lena Jung, Frank Abendroth, Bernd Schmeck, Gerhard Hummer, Olalla Vázquez, Tobias J. Erb |
| Source: |
Nature Communications, Vol 14, Iss 1, Pp 1-14 (2023) |
| Publisher Information: |
Nature Portfolio, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Science |
| Subject Terms: |
Science |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2041-1723 |
| Relation: |
https://doaj.org/toc/2041-1723 |
| DOI: |
10.1038/s41467-023-42434-9 |
| Access URL: |
https://doaj.org/article/adbbacf7db9c45aca2f0981cf0f79344 |
| Accession Number: |
edsdoj.bbacf7db9c45aca2f0981cf0f79344 |
| Database: |
Directory of Open Access Journals |
