4.4 Article

Identification of inhibitors for Agr quorum sensing system of Staphylococcus aureus by machine learning, pharmacophore modeling, and molecular dynamics approaches

Journal

JOURNAL OF MOLECULAR MODELING
Volume 29, Issue 8, Pages -

Publisher

SPRINGER
DOI: 10.1007/s00894-023-05647-9

Keywords

Staphylococcus aureus; Quorum sensing; Agr system; Machine learning; COCONUT database

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By disrupting the quorum sensing network of Staphylococcus aureus, the prevention of bacterial infection is achieved. Through drug screening and molecular simulation, five potential inhibitors have been identified and preliminary evaluations have been conducted.
ContextStaphylococcus aureus is a highly pathogenic organism that is the most common cause of postoperative complications as well as severe infections like bacteremia and infective endocarditis. By mediating the formation of biofilms and the expression of virulent genes, the quorum sensing (QS) mechanism is a major contributor to the development of these diseases. By hindering its QS network, an innovative approach to avoiding this bacterial infection is taken. Targeting the AgrA of the Agr system serves as beneficial in holding the top position in the QS system cascade.MethodsUsing known AgrA inhibitors, the machine learning algorithms (artificial neural network, naive Bayes, random forest, and support vector machine) and pharmacophore model were developed. The potential lead compounds were screened against the Zinc and COCONUT databases using the best pharmacophore hypothesis. The hits were then subjected second screening process using the best machine learning model. The predicted active compounds were then reranked based on the docking score. The stability of AgrA-lead compounds was studied using molecular dynamics approaches, and an ADME profile was also carried out. Five lead compounds, namely, CNP02386963,4,5-trihydroxy-2-[({7,13,14-trihydroxy-3,10-dioxo-2,9-dioxatetracyclo[6.6.2.0(4),(16).0(11),(15)]hexadeca-1(14),4,6,8(16),11(15),12-hexaen-6-yl}oxy)methyl]benzoic acid, CNP0129274 4-(dimethylamino)-1,5,6,10,12,12a-hexahydroxy-6-methyl-3,11-dioxo-3,4,4a,5,5a,6,11,12a-octahydrotetracene-2-carboxamide, CNP0242717 3-Hydroxyasebotin, CNP0361624 3,4,5-trihydroxy-6-[(2,4,5,6,7-pentahydroxy-1-oxooctan-3-yl)oxy]oxane-2-carboxylic acid, and CNP0285058 2-{[4,5-dihydroxy-6-(hydroxymethyl)-3-[(3,4,5-trihydroxy-6-methyloxan-2-yl)oxy]oxan-2-yl]oxy}-2-(4-hydroxyphenyl)acetonitrile were obtained using the two-step virtual screening process. The molecular dynamics study revealed that the CNP0238696 was found to be stable in the binding pocket of AgrA. ADME profiles show that this compound has two Lipinski violations and low bioavailability. Further studies should be performed to assess the anti-biofilm activity of the lead compound in vitro.

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