Discovery of potential antiStaphylococcus aureus natural products and their mechanistic studies using machine learning and molecular dynamic simulations

文献类型： 外文期刊

第一作者： Wang, Zinan

作者： Wang, Zinan;Zhang, Min;Liang, Shan;Pan, Fei;Tian, Wenli;Zhang, Min;Liang, Shan

作者机构：

关键词： Anti-S aureus; QSAR; Molecular dynamic simulation; Hesperetic acid; 2-HTPA

期刊名称：HELIYON （ 影响因子：4.0； 五年影响因子：4.1 ）

ISSN：

年卷期： 2024 年 10 卷 9 期

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收录情况： SCI

摘要： The structure-activity analysis (SAR) and machine learning were used to investigate potential anti-S. aureus agents in a faster method. In this study, 24 oxygenated benzene ring components with S. aureus inhibition capacity were confirmed by literature exploring and in-house experiments, and the SAR analysis suggested that the hydroxyl group position may affect the antiS. aureus activity. The 2D-MLR-QSAR model with 9 descriptors was further evaluated as the best model among the 21 models. After that, hesperetic acid and 2-HTPA were further explored and evaluated as the potential anti-S. aureus agents screening in the natural product clustering library through the best QSAR model calculation. The antibacterial capacities of hesperetic acid and 2HTPA had been investigated and proved the similar predictive pMIC value resulting from the QSAR model. Besides, the two novel components were able to inhibit the growth of S. aureus by disrupting the cell membrane through the molecular dynamics simulation (MD), which further evidenced by scanning electron microscopy (SEM) test and PI dye results. Overall, these results are highly suggested that QSAR can be used to predict the antibacterial agents targeting S. aureus, which provides a new paradigm to research the molecular structure-antibacterial capacity relationship.

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