通过高通量筛选和强化学习开发治疗鲍曼尼氏菌肺部感染的新型香豆素衍生物。

IF 6.2
Jing Li, Zhou Lu, Liuchang Wang, Huiqing Shi, Bixin Chu, Yingwei Qu, Zichen Ye, Di Qu
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引用次数: 0

摘要

随着鲍曼不动杆菌(A. baumannii)对抗生素的耐药性不断增加,研究人员已将注意力转向新型抗菌剂的开发。其中,基于香豆素的杂环化合物因其独特的生物活性而备受关注,尤其是在抗菌感染领域。本研究合成了一系列香豆素衍生物,并对其杀菌活性进行了筛选(Ren 等,2018 年;Salehian 等,2021 年)。评估了这些化合物对细菌菌株的抑制活性,并探讨了新化合物的相关机理。首先,测定化合物处理后的 MIC 值和细菌生长曲线,以评估其体外抗菌活性。然后,通过测定小鼠存活率、计数细菌 CFU 数量、测量炎症细胞因子水平和组织病理学分析,评估了新化合物对鲍曼不动杆菌感染小鼠的体内抗菌活性。此外,还使用 DCFH-DA 检测试剂盒测量了细菌细胞中的 ROS 水平。此外,还通过分子对接预测和证明了新化合物在感染疾病治疗中的潜在靶点和详细机制。随后,完成了ADMET特征预测,并以受试化合物为训练模板,通过强化学习研究优化了新的、可合成的、有药效的分子。分子对接进一步证明了所选结构与靶蛋白之间的相互作用。这一系列创新研究为开发新型抗鲍曼氏菌感染药物提供了重要的理论和实验数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-Throughput Screening and Reinforcement Learning.

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-Throughput Screening and Reinforcement Learning.

With the increasing resistance of Acinetobacter baumannii (A. baumannii) to antibiotics, researchers have turned their attention to the development of new antimicrobial agents. Among them, coumarin-based heterocycles have attracted much attention due to their unique biological activities, especially in the field of antibacterial infection. In this study, a series of coumarin derivatives were synthesized and screened for their bactericidal activities (Ren et al. 2018; Salehian et al. 2021). The inhibitory activities of these compounds on bacterial strains were evaluated, and the related mechanism of the new compounds was explored. Firstly, the MIC values and bacterial growth curves were measured after compound treatment to evaluate the antibacterial activity in vitro. Then, the in vivo antibacterial activities of the new compounds were assessed on A. baumannii-infected mice by determining the mice survival rates, counting bacterial CFU numbers, measuring inflammatory cytokine levels, and histopathology analysis. In addition, the ROS levels in the bacterial cells were measured with DCFH-DA detection kit. Furthermore, the potential target and detailed mechanism of the new compounds during infection disease therapy were predicted and evidenced with molecular docking. After that, ADMET characteristic prediction was completed, and novel, synthesizable, drug-effective molecules were optimized with reinforcement learning study based on the probed compound as a training template. The interaction between the selected structures and target proteins was further evidenced with molecular docking. This series of innovative studies provides important theoretical and experimental data for the development of new anti-A. baumannii infection drugs.

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