表面等离子共振
虚拟筛选
对接(动物)
芦丁
计算生物学
2019年冠状病毒病(COVID-19)
丝氨酸蛋白酶
化学
分子动力学
蛋白酶
纳米技术
医学
生物化学
生物
材料科学
计算化学
传染病(医学专业)
酶
兽医学
纳米颗粒
抗氧化剂
疾病
病理
作者
Rong Yang,Linhua Liu,Dansheng Jiang,Lei Liu,Huili Yang,Hongling Xu,Meirong Qin,Ping Wang,Jiangyong Gu,Yufeng Xing
标识
DOI:10.1021/acs.jcim.2c01643
摘要
Background: Coronavirus disease-19 (COVID-19) pneumonia continues to spread in the entire globe with limited medication available. In this study, the active compounds in Chinese medicine (CM) recipes targeting the transmembrane serine protease 2 (TMPRSS2) protein for the treatment of COVID-19 were explored. Methods: The conformational structure of TMPRSS2 protein (TMPS2) was built through homology modeling. A training set covering TMPS2 inhibitors and decoy molecules was docked to TMPS2, and their docking poses were re-scored with scoring schemes. A receiver operating characteristic (ROC) curve was applied to select the best scoring function. Virtual screening of the candidate compounds (CCDs) in the six highly effective CM recipes against TMPS2 was conducted based on the validated docking protocol. The potential CCDs after docking were subject to molecular dynamics (MD) simulations and surface plasmon resonance (SPR) experiment. Results: A training set of 65 molecules were docked with modeled TMPS2 and LigScore2 with the highest area under the curve, AUC, value (0.886) after ROC analysis selected to best differentiate inhibitors from decoys. A total of 421 CCDs in the six recipes were successfully docked into TMPS2, and the top 16 CCDs with LigScore2 higher than the cutoff (4.995) were screened out. MD simulations revealed a stable binding between these CCDs and TMPS2 due to the negative binding free energy. Lastly, SPR experiments validated the direct combination of narirutin, saikosaponin B1, and rutin with TMPS2. Conclusions: Specific active compounds including narirutin, saikosaponin B1, and rutin in CM recipes potentially target and inhibit TMPS2, probably exerting a therapeutic effect on COVID-19.
科研通智能强力驱动
Strongly Powered by AbleSci AI