Virtual screening of active ingredients of traditional Chinese medicine in treating COVID-19 based on molecular docking and molecular dynamic simulation_Journal of Biomedical Engineering

Authors：

LIU Minghao ^1,2 , Faez Iqbal Khan ^3,1 , XIAO Yuqing ⁴ , WANG Xu ⁵ , HU Ziran ⁶ ,  LAI Dakun ¹

1. School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
2. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
3. Department of Biological Sciences, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215000, P. R. China;
4. School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
5. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
6. First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin 150006, P. R. China;

Corresponding author：

LAI Dakun, Email: dklai@uestc.edu.cn

Keywords：

COVID-19; Traditional Chinese medicines; Virtual screening; Molecular docking; Molecular dynamics simulations; Xambioona

DOI：

10.7507/1001-5515.202205021

Video：

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We aim to screen out the active components that may have therapeutic effect on coronavirus disease 2019 (COVID-19) from the severe and critical cases’ prescriptions in the “Coronavirus Disease 2019 Diagnosis and Treatment Plan (Trial Ninth Edition)” issued by the National Health Commission of the People’s Republic of China and explain its mechanism through the interactions with proteins. The ETCM database and SwissADME database were used to screen the active components contained in 25 traditional Chinese medicines in 3 prescriptions, and the PDB database was used to obtain the crystal structures of 4 proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Molecular docking was performed using Autodock Vina and molecular dynamics simulations were performed using GROMACS. Binding energy results showed that 44 active ingredients including xambioona, gancaonin L, cynaroside, and baicalin showed good binding affinity with multiple targets of SARS-CoV-2, while molecular dynamics simulations analysis showed that xambioona bound more tightly to the nucleocapsid protein of SARS-CoV-2 and exerted a potent inhibitory effect. Modern technical methods are used to study the active components of traditional Chinese medicine and show that xambioona is an effective inhibitor of SARS-CoV-2 nucleocapsid protein, which provides a theoretical basis for the development of new anti-SARS-CoV-2 drugs and their treatment methods.

Citation： LIU Minghao, Faez Iqbal Khan, XIAO Yuqing, WANG Xu, HU Ziran, LAI Dakun. Virtual screening of active ingredients of traditional Chinese medicine in treating COVID-19 based on molecular docking and molecular dynamic simulation. Journal of Biomedical Engineering, 2022, 39(5): 1005-1014. doi: 10.7507/1001-5515.202205021 Copy

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1. Zhou P, Yang X L, Wang X G, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 2020, 579(7798): 270-273.
2. Ahmed S F, Quadeer A A, McKay M R. Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies. Viruses, 2020, 12(3): 254.
3. Sahebnasagh A, Avan R, Saghafi F, et al. Pharmacological treatments of COVID-19. Pharmacol Rep, 2020, 72(6): 1446-1478.
4. Zou L, Ruan F, Huang M, et al. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N Engl J Med, 2020, 382(12): 1177-1179.
5. Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA, 2020, 323(11): 1061-1069.
6. WHO Team. WHO Expert Meeting on Evaluation of Traditional Chinese Medicine in the Treatment of COVID-19. Geneva: WHO, 2022: 4-17.
7. Song C M, Lim S J, Tong J C. Recent advances in computer-aided drug design. Brief Bioinform, 2009, 10(5): 579-591.
8. Chen Y, Liu Q, Guo D. Emerging coronaviruses: Genome structure, replication, and pathogenesis. J Med Virol, 2020, 92(4): 418-423.
9. Prajapat M, Sarma P, Shekhar N, et al. Drug targets for corona virus: A systematic review. Indian J Pharmacol, 2020, 52(1): 56-65.
10. 国家卫生健康委办公厅. 新型冠状病毒肺炎诊疗方案(试行第九版). 传染病信息, 2022, 35(2): 97-106.
11. Trott O, Olson A J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem, 2010, 31(2): 455-461.
12. Lindahl E R. Molecular dynamics simulations. Methods Mol Biol, 2008, 443: 3-23.
13. Fu L, Ye F, Feng Y, et al. Both Boceprevir and GC376 efficaciously inhibit SARS-CoV-2 by targeting its main protease. Nat Commun, 2020, 11(1): 4417.
14. Kang S, Yang M, Hong Z, et al. Crystal structure of SARS-CoV-2 nucleocapsid protein RNA binding domain reveals potential unique drug targeting sites. Acta Pharm Sin B, 2020, 10(7): 1228-1238.
15. Juraszek J, Rutten L, Blokland S, et al. Stabilizing the closed SARS-CoV-2 spike trimer. Nat Commun, 2021, 12(1): 244.
16. Yin W, Luan X, Li Z, et al. Structural basis for inhibition of the SARS-CoV-2 RNA polymerase by suramin. Nat Struct Mol Biol, 2021, 28(3): 319-325.
17. Pettersen E F, Goddard T D, Huang C C, et al. UCSF Chimera-a visualization system for exploratory research and analysis. J Comput Chem, 2004, 25(13): 1605-1612.
18. Van Der Spoel D, Lindahl E, Hess B, et al. GROMACS: fast, flexible, and free. J Comput Chem, 2005, 26(16): 1701-1718.
19. Khan F I, Lai D, Anwer R, et al. Identifying novel sphingosine kinase 1 inhibitors as therapeutics against breast cancer. J Enzyme Inhib Med Chem, 2020, 35(1): 172-186.
20. Hassan F, Khan F I, Song H, et al. Effects of reverse genetic mutations on the spectral and photochemical behavior of a photoactivatable fluorescent protein PAiRFP1. Spectrochim Acta A Mol Biomol Spectrosc, 2020, 228: 117807.
21. Durrani R, Khan F I, Ali S, et al. A thermolabile phospholipase B from Talaromyces marneffei GD-0079: biochemical characterization and structure dynamics study. Biomolecules, 2020, 10(2): 231.
22. Khan F I, Wei D Q, Gu K R, et al. Current updates on computer aided protein modeling and designing. Int J Biol Macromol, 2016, 85: 48-62.
23. Lipinski C A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol, 2004, 1(4): 337-341.
24. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep, 2017, 7: 42717.
25. Zhang L, Ai H, Chen W, et al. CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods. Sci Rep, 2017, 7(1): 2118.
26. Gahlawat A, Kumar N, Kumar R, et al. Structure-based virtual screening to discover potential lead molecules for the SARS-CoV-2 main protease. J Chem Inf Model, 2020, 60(12): 5781-5793.
27. Peng Y, Du N, Lei Y, et al. Structures of the SARS-CoV-2 nucleocapsid and their perspectives for drug design. EMBO J, 2020, 39(20): e105938.
28. Hu B, Guo H, Zhou P, et al. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol, 2021, 19(3): 141-154. Erratum in: Nat Rev Microbiol, 2022, 20(5): 315.
29. Yin W, Mao C, Luan X, et al. Structural basis for inhibition of the RNA-dependent RNA polymerase from SARS-CoV-2 by remdesivir. Science, 2020, 368(6498): 1499-1504.
30. Huang Y, Yang C, Xu X F, et al. Structural and functional properties of SARS-CoV-2 spike protein: potential antivirus drug development for COVID-19. Acta Pharmacol Sin, 2020, 41(9): 1141-1149.
31. Hatmal M M, Alshaer W, Al-Hatamleh M A I, et al. Comprehensive structural and molecular comparison of spike proteins of SARS-CoV-2, SARS-CoV and MERS-CoV, and their interactions with ACE2. Cells, 2020, 9(12): 2638.
32. Zhu W, Chen C Z, Gorshkov K, et al. RNA-dependent RNA polymerase as a target for COVID-19 drug discovery. SLAS Discov, 2020, 25(10): 1141-1151.

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