药理学
对接(动物)
2019-20冠状病毒爆发
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
计算生物学
医学
生物信息学
2019年冠状病毒病(COVID-19)
生物
病毒学
内科学
传染病(医学专业)
疾病
兽医学
爆发
作者
Yuanping Li,Liqin Zhao,Hu Wen,Yuanping Li
标识
DOI:10.1016/j.joim.2023.09.001
摘要
The aim of this study is to identify molecules from traditional Chinese medicine (TCM) with potential activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. We applied the Apriori algorithm to identify important combinations of herbs in the TCM prescriptions for the treatment of coronavirus disease 2019 (COVID-19). Then, we explored the active components and core targets using network pharmacology. In addition, the molecular docking approach was performed to investigate the interaction of these components with the main structural and non-structural proteins, as well as the mutants. Furthermore, their stability in the binding pockets was further evaluated with the molecular dynamics approach. A combination of Amygdalus Communis Vas., Ephedra Herba and Scutellaria baicalensis Georgi was selected as the important herbal combination, and 11 main components and 20 core targets against COVID-19 were obtained. These components, including luteolin, naringenin, stigmasterol, baicalein, and so on, were the potentially active compounds against COVID-19. The binding affinity of these compounds with the potential targets was as high as the positive controls. Among them, baicalein could interfere with multiple targets simultaneously, and it also interfered with the interaction between spike protein and angiotensin-converting enzyme 2 receptor. Additionally, almost all the systems reached stability during dynamics simulation. The combination of A. communis, Ephedra Herba and S. baicalensis was the most important herbal combination for the treatment of COVID-19. Baicalein may be a potential candidate against SARS-CoV-2 and its variants. Please cite this article as: Song JB, Zhao LQ, Wen HP, Li YP. Herbal combinations against COVID-19: A network pharmacology, molecular docking and dynamics study. J Integr Med. 2023;21(6):593–604.
科研通智能强力驱动
Strongly Powered by AbleSci AI