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In silico identification of novel peptides with antibacterial activity against multidrug resistant Staphylococcus aureus

Oyama, Linda B.; Olleik, Hamza; Nery Teixeira, Ana Carolina; Guidini, Matheus M.; Pickup, James A.; Cookson, Alan R.; Vallin, Hannah; Wilkinson, Toby; Bazzolli, Denise; Richards, Jennifer; Wootton, Mandy; Mikut, Ralf ORCID iD icon; Hilpert, Kai; Maresca, Marc; Perrier, Josette; Hess, Matthias; Mantovani, Hilario C.; Fernandez-Fuentes, Narcis; Creevey, Christopher J.; ... mehr

Abstract (englisch):

Herein we report the identification and characterisation of two linear antimicrobial peptides (AMPs), HG2 and HG4, with activity against a wide range of multidrug resistant (MDR) bacteria, especially methicillin resistant Staphylococcus aureus (MRSA) strains, a highly problematic group of Gram-positive bacteria in the hospital and community environment. To identify the novel AMPs presented here, we employed the classifier model design, a feature extraction method using molecular descriptors for amino acids for the analysis, visualization, and interpretation of AMP activities from a rumen metagenomic dataset. This allowed for the in silico discrimination of active and inactive peptides in order to define a small number of promising novel lead AMP test candidates for chemical synthesis and experimental evaluation. In vitro data suggest that the chosen AMPs are fast acting, show strong biofilm inhibition and dispersal activity and are efficacious in an in vivo model of MRSA USA300 infection, whilst showing little toxicity to human erythrocytes and human primary cell lines ex vivo. Observations from biophysical AMP-lipid-interactions and electron microscopy suggest that the newly identified peptides interact with the cell membrane and may be involved in the inhibition of other cellular processes. ... mehr

Preprint §
DOI: 10.5445/IR/1000092791
Veröffentlicht am 03.04.2019
DOI: 10.1101/577221
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2019
Sprache Englisch
Identifikator urn:nbn:de:swb:90-927919
KITopen-ID: 1000092791
HGF-Programm 47.01.01 (POF III, LK 01) Biol.Netzwerke u.Synth.Regulat. ITG+ITC
Erschienen in bioRxiv beta
Verlag Cold Spring Harbor Laboratory Press
Seiten 42 S.
Externe Relationen Abstract/Volltext
Schlagwörter Antimicrobial peptides, MRSA, multidrug resistant infections, rumen microbiome
Nachgewiesen in Dimensions
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