@article {328718,
	title = {Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning.},
	journal = {Cell Host Microbe},
	volume = {31},
	year = {2023},
	month = {2023 Aug 09},
	pages = {1260-1274.e6},
	abstract = {<p>Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in\&nbsp;vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A.\&nbsp;baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.</p>
},
	keywords = {Animals, Anti-Bacterial Agents, Anti-Infective Agents, Antimicrobial Peptides, Humans, Machine Learning, Mice, Microbial Sensitivity Tests, Peptide Hydrolases, Peptides},
	issn = {1934-6069},
	doi = {10.1016/j.chom.2023.07.001},
	url = {https://www.sciencedirect.com/science/article/pii/S1931312823002962?via\%3Dihub},
	author = {Maasch, Jacqueline R M A and Torres, Marcelo D T and Melo, Marcelo C R and de la Fuente-Nunez, Cesar}
}
