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

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Abstract

Context

Staphylococcus 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.

Methods

Using known AgrA inhibitors, the machine learning algorithms (artificial neural network, naïve 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[6.6.2.04,16.011,15]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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Acknowledgements

The authors are grateful to the Centre for Machine Learning and Intelligence (CMLI) at Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India, for their continuous rendering of and support during the work.

Funding

This work was supported by the DST-CURIE-AI-PhaseII (Project No. 16), Centre for Machine Learning and Intelligence (CMLI) at Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India.

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Authors and Affiliations

  1. Department of Biochemistry, Biotechnology, and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India

    Monica Ramasamy, Aishwarya Vetrivel & Rajeswari Murugesan

  2. Department of Chemistry, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India

    Sharulatha Venugopal

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  1. Monica Ramasamy
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Contributions

Conception and design: Rajeswari Murugesan. Development of methodology and acquisition of data: Monica Ramasamy and Rajeswari Murugesan, Analysis; interpretation of data: Monica Ramasamy, Aishwarya Vetrivel, and Rajeswari Murugesan; writing, review, and/or revision of the manuscript: Monica Ramasamy, Aishwarya Vetrivel, Sharulatha Venugopal, and Rajeswari Murugesan.

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Correspondence to Rajeswari Murugesan.

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Ramasamy, M., Vetrivel, A., Venugopal, S. et al. Identification of inhibitors for Agr quorum sensing system of Staphylococcus aureus by machine learning, pharmacophore modeling, and molecular dynamics approaches. J Mol Model 29, 258 (2023). https://doi.org/10.1007/s00894-023-05647-9

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