Antimicrobial activity of compounds identified by artificial intelligence discovery engine targeting enzymes involved in Neisseria gonorrhoeae peptidoglycan metabolism

Autor: Ravi Kant, Hannah Tilford, Camila S. Freitas, Dayana A. Santos Ferreira, James Ng, Gwennan Rucinski, Joshua Watkins, Ryan Pemberton, Tigran M. Abramyan, Stephanie C. Contreras, Alejandra Vera, Myron Christodoulides
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Biological Research, Vol 57, Iss 1, Pp 1-23 (2024)
Druh dokumentu: article
ISSN: 0717-6287
DOI: 10.1186/s40659-024-00543-9
Popis: Abstract Background Neisseria gonorrhoeae (Ng) causes the sexually transmitted disease gonorrhoea. There are no vaccines and infections are treated principally with antibiotics. However, gonococci rapidly develop resistance to every antibiotic class used and there is a need for developing new antimicrobial treatments. In this study we focused on two gonococcal enzymes as potential antimicrobial targets, namely the serine protease L,D-carboxypeptidase LdcA (NgO1274/NEIS1546) and the lytic transglycosylase LtgD (NgO0626/NEIS1212). To identify compounds that could interact with these enzymes as potential antimicrobials, we used the AtomNet virtual high-throughput screening technology. We then did a computational modelling study to examine the interactions of the most bioactive compounds with their target enzymes. The identified compounds were tested against gonococci to determine minimum inhibitory and bactericidal concentrations (MIC/MBC), specificity, and compound toxicity in vitro. Results AtomNet identified 74 compounds that could potentially interact with Ng-LdcA and 84 compounds that could potentially interact with Ng-LtgD. Through MIC and MBC assays, we selected the three best performing compounds for both enzymes. Compound 16 was the most active against Ng-LdcA, with a MIC50 value
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