Update papers.bib

_bibliography/papers.bib

@@ -206,19 +206,20 @@ @article{soudier2022cell
   year={2022}
 }
 
-@article {Pandi2022.11.19.517184,
-	author = {Pandi, Amir and Adam, David and Zare, Amir and Trinh, Van Tuan and Schaefer, Stefan L. and Wiegand, Marie and Klabunde, Bj{\"o}rn and Bobkova, Elizaveta and Kushwaha, Manish and Foroughijabbari, Yeganeh and Braun, Peter and Spahn, Christoph and Preu{\ss}er, Christian and von Strandmann, Elke Pogge and Bode, Helge B. and von Buttlar, Heiner and Bertrams, Wilhelm and Jung, Anna Lena and Abendroth, Frank and Schmeck, Bernd and Hummer, Gerhard and V{\'a}zquez, Olalla and Erb, Tobias J.},
-	title = {Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides},
-	elocation-id = {2022.11.19.517184},
-	year = {2022},
-	doi = {10.1101/2022.11.19.517184},
-	publisher = {Cold Spring Harbor Laboratory},
-	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.Competing Interest StatementThe authors have declared no competing interest.},
-	URL = {https://www.biorxiv.org/content/early/2022/12/23/2022.11.19.517184},
-	eprint = {https://www.biorxiv.org/content/early/2022/12/23/2022.11.19.517184.full.pdf},
-	journal = {bioRxiv}
+
+
+@article{pandi2023cell,
+  title={Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides},
+  author={Pandi, Amir and Adam, David and Zare, Amir and Trinh, Van Tuan and Schaefer, Stefan L and Burt, Marie and Klabunde, Bj{\"o}rn and Bobkova, Elizaveta and Kushwaha, Manish and Foroughijabbari, Yeganeh and others},
+  journal={Nature Communications},
+  volume={14},
+  number={1},
+  pages={7197},
+  year={2023},
+  publisher={Nature Publishing Group UK London}
 }
 
+
 @article {Pathania2022.05.11.491355,
 	author = {Pathania, Amit and Hopper, Corbin and Pandi, Amir and F{\"u}gger, Matthias and Nowak, Thomas and Kushwaha, Manish},
 	title = {A synthetic communication system uncovers extracellular immunity that self-limits bacteriophage transmission},