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First virtual screening and experimental validation of inhibitors targeting GES-5 carbapenemase

Articolo
Data di Pubblicazione:
2019
Abstract:
The worldwide spread of beta-lactamases with hydrolytic activity extended to last resort carbapenems is aggravating the antibiotic resistance problem and endangers the successful antimicrobial treatment of clinically relevant pathogens. As recently highlighted by the World Health Organization, new strategies to contain antimicrobial resistance are urgently needed. Class A carbapenemases include members of the KPC, GES and SFC families. These enzymes have the ability to hydrolyse penicillins, cephalosporins and carbapenems, while also being less susceptible to available beta-lactam inhibitors, such as clavulanic acid. The KPC family is the most prevalent. It is mostly found on plasmids in Klebsiella pneumoniae, meaning that great amounts of attention, in terms of inhibitor design and structural biology, have been dedicated to it, whereas no efforts have yet been dedicated to GES-type enzymes, despite their ability to rapidly and horizontally disseminate. We herein report the first in silico screening against GES-5, which is the most dangerous GES-type beta-lactamase, using a library of 800K commercially available candidates that all share drug-like properties, such as their MW, logP, rotatable bonds and HBA/HBD atoms. The best screening results were filtered to enrich the number of different chemotypes, and then submitted to molecular docking. The 34 most promising candidates were selected for in vitro validation in biochemical assays against recombinant GES-5. Six hits acted as inhibitors, in the high micromolar range, towards GES-5 and led to the identification of the first, novel chemotypes with inhibitory activity against this clinically relevant carbapenemase.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Antibiotic resistance; Carbapenemases; Docking; GES-5 Guyana extended-spectrum-lactamase; In vitro inhibition; Virtual screening; Drug Discovery3003 Pharmaceutical Science; Computer Science Applications1707 Computer Vision and Pattern Recognition; Physical and Theoretical Chemistry
Elenco autori:
Spyrakis, Francesca; Bellio, Pierangelo; Quotadamo, Antonio; Linciano, Pasquale; Benedetti, Paolo; D’Arrigo, Giulia; Baroni, Massimo; Cendron, Laura; Celenza, Giuseppe; Tondi, Donatella
Autori di Ateneo:
LINCIANO PASQUALE
Link alla scheda completa:
https://iris.unipv.it/handle/11571/1462638
Pubblicato in:
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
Journal
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www.wkap.nl/journalhome.htm/0920-654X
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