<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maasch, Jacqueline R M A</style></author><author><style face="normal" font="default" size="100%">Torres, Marcelo D T</style></author><author><style face="normal" font="default" size="100%">Melo, Marcelo C R</style></author><author><style face="normal" font="default" size="100%">de la Fuente-Nunez, Cesar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell Host Microbe</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell Host Microbe</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Anti-Bacterial Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Anti-Infective Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Antimicrobial Peptides</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Microbial Sensitivity Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">Peptide Hydrolases</style></keyword><keyword><style  face="normal" font="default" size="100%">Peptides</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Aug 09</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S1931312823002962?via%3Dihub</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">1260-1274.e6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&amp;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.&amp;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.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><custom1><style face="normal" font="default" size="100%">&lt;p&gt;https://www.ncbi.nlm.nih.gov/pubmed/37516110?dopt=Abstract&lt;/p&gt;
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