Abstract：
This research paper utilizes a fused-in-silico approach alongside bioactivity evaluation to identify active FtsZ inhibitors for drug discovery. Initially, ROC-guided machine learning was employed to obtain almost 13182 compounds from three libraries. After conducting virtual screening to assess the affinity of 2621 acquired compounds, cluster analysis and bonding model analysis led to the discovery of five hit compounds. Additionally, antibacterial activity assays and time-killing kinetics revealed that T3995 could eliminate Staphylococcus aureus ATCC6538 and Bacillus subtilis ATCC9732, with MIC values of 32 and 2 μg/mL. Further morphology and FtsZ polymerization assays indicated that T3995 could be an antimicrobial inhibitor by targeting FtsZ protein. Moreover, hemolytic toxicity evaluation demonstrated that T3995 is safe at or below 16 ug/mL concentration. Additionally, bonding model analysis explained how the compound T3995 can display antimicrobial activity by targeting the FtsZ protein. In conclusion, this study presents a promising FtsZ inhibitor that was discovered through a fused computer method and bioactivity evaluation.
摘要：
本研究论文利用融合的计算机方法以及生物活性评估来鉴定用于药物发现的活性FtsZ抑制剂。最初，采用ROC引导的机器学习从三个库中获得近13182种化合物。在进行虚拟筛选以评估2621种获得的化合物的亲和力后，聚类分析和键合模型分析导致发现了5种命中化合物。此外,抗菌活性测定和时间杀伤动力学表明，T3995可以消除金黄色葡萄球菌ATCC6538和枯草芽孢杆菌ATCC9732，MIC值为32和2μg/mL。进一步的形态学和FtsZ聚合试验表明,T3995可以通过靶向FtsZ蛋白而成为抗微生物抑制剂。此外，溶血毒性评估表明，T3995在16ug/mL浓度或以下是安全的。此外,键合模型分析解释了化合物T3995如何通过靶向FtsZ蛋白来显示抗菌活性。总之,这项研究提出了一种有前途的FtsZ抑制剂，该抑制剂是通过融合计算机方法和生物活性评估发现的。
