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27/04/2026
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Huynh, T. N. T., Kieu, N. H., & Kieu, M. N. (2026). Interaction mechanism of Ensitrelvir with SARS-CoV-2 main protease and its variants revealed by molecular dynamics simulation. Dong Thap University Journal of Science, 15(5), 20-31. https://doi.org/10.52714/dthu.ns.2177.1830
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Interaction mechanism of Ensitrelvir with SARS-CoV-2 main protease and its variants revealed by molecular dynamics simulation
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Abstract
SARS-CoV-2 continues to evolve, leading to new variants that may diminish the effectiveness of current treatments. It has undergone genetic mutations, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529). The main protease (Mpro) is a key target for antiviral drug development, with inhibitors being investigated as potential treatments for COVID-19. Docking and SMD analyses revealed that the binding energy, non-equilibrium work, and rupture force of Ensitrelvir exhibit strong interactions with Mpro, particularly in the K90R mutation, where the non-equilibrium work is 158.8 ± 17.7 kcal.mol⁻¹. This finding aligns well with experimental data, as indicated by IC50 value, showing a correlation coefficient (R ≈ -0.9). Additionally, docking results indicate that non-bonded interactions play a crucial role in Ensitrelvir's inhibition of SARS-CoV-2.
Keywords
Docking method, Ensitrelvir, Mpro, SARS-CoV-2, SMD method
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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Faria, N. R., Mellan, T. A., Whittaker, C., Claro, I. M., Candido, D. d. S., Mishra, S., Crispim, M. A., Sales, F. C., Hawryluk, I., & McCrone, J. T. (2021). Genomics and epidemiology of the P. 1 SARS-CoV-2 lineage in Manaus, Brazil. Science, 372(6544), 815-821. https://doi.org/10.1126/science.abh2644
Grubmüller, H., Heymann, B., & Tavan, P. (1996). Ligand binding: molecular mechanics calculation of the streptavidin-biotin rupture force. Science, 271(5251), 997-999. https://doi.org/10.1126/science.271.5251.997
Huynh, T. N. T., Kieu, M. N., Kieu, N. H., & Nguyen Quoc, T. (2024). Molecular mechanism of Ensitrelvir and its similarity inhibiting SARS-CoV-2 main protease by molecular dynamics simulation. Tạp chí Khoa học Đại học Đồng Tháp, 13(5), 37-44. https://doi.org/10.52714/dthu.13.5.2024.1286
Isralewitz, B., Gao, M., & Schulten, K. (2001). Steered molecular dynamics and mechanical functions of proteins. Current Opinion in Structural Biology, 11(2), 224-230. https://doi.org/10.1016/S0959-440X(00)00194-9
Kawashima, S., Matsui, Y., Adachi, T., Morikawa, Y., Inoue, K., Takebayashi, S., Nobori, H., Rokushima, M., Tachibana, Y., & Kato, T. (2023). Ensitrelvir is effective against SARS-CoV-2 3CL protease mutants circulating globally. Biochemical and Biophysical Research Communications, 645, 132-136. https://doi.org/10.1016/j.bbrc.2023.01.040
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2025). PubChem 2025 update. Nucleic Acids Res, 53(D1), D1516-d1525. https://doi.org/10.1093/nar/gkae1059
Kumar, S., & Li, M. S. (2010). Biomolecules under mechanical force. Physics Reports, 486(1-2), 1-74. https://doi.org/10.1016/j.physrep.2009.11.001
Lin, C., Jiang, H., Li, W., Zeng, P., Zhou, X., Zhang, J., & Li, J. (2023). Structural basis for the inhibition of coronaviral main proteases by ensitrelvir. Structure, 31(9), 1016-1024.e1013. https://doi.org/10.1016/j.str.2023.06.010
Lin, M., Zeng, X., Duan, Y., Yang, Z., Ma, Y., Yang, H., Yang, X., & Liu, X. (2023). Molecular mechanism of ensitrelvir inhibiting SARS-CoV-2 main protease and its variants. Communications Biology, 6(1), 694. https://doi.org/10.1038/s42003-023-05071-y
Mondal, S., Chen, Y., Lockbaum, G. J., Sen, S., Chaudhuri, S., Reyes, A. C., Lee, J. M., Kaur, A. N., Sultana, N., & Cameron, M. D. (2022). Dual inhibitors of main protease (MPro) and Cathepsin L as potent antivirals against SARS-CoV2. Journal of the American Chemical Society, 144(46), 21035-21045. https://pubs.acs.org/doi/10.1021/jacs.2c04626
Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785-2791. https://doi.org/10.1002/jcc.21256
Ngo, S. T., Quynh Anh Pham, N., Thi Le, L., Pham, D.-H., & Vu, V. V. (2020). Computational determination of potential inhibitors of SARS-CoV-2 main protease. Journal of Chemical Information and Modeling, 60(12), 5771-5780. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00491
Owen, D. R., Allerton, C. M., Anderson, A. S., Aschenbrenner, L., Avery, M., Berritt, S., Boras, B., Cardin, R. D., Carlo, A., & Coffman, K. J. (2021). An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19. Science, 374(6575), 1586-1593. https://doi.org/10.1126/science.abl4784
Quan, P. M., Toan, T. Q., Tung, N. S., Dan, N. T., Thuy, T. T. T., Cuong, N. M., & Long, P. Q. (2020). Initial study on SARS-CoV-2 main protease inhibition mechanism of some potential drugs using molecular docking simulation. Vietnam Journal of Science and Technology, 58(6), 665-675. https://doi.org/10.15625/2525-2518/58/6/14914
Sayers, E. W., Beck, J., Bolton, E. E., Brister, J. R., Chan, J., Connor, R., Feldgarden, M., Fine, A. M., Funk, K., Hoffman, J., Kannan, S., Kelly, C., Klimke, W., Kim, S., Lathrop, S., Marchler-Bauer, A., Murphy, T. D., O'Sullivan, C., Schmieder, E., Skripchenko, Y., Stine, A., Thibaud-Nissen, F., Wang, J., Ye, J., Zellers, E., Schneider, V. A., & Pruitt, K. D. (2025). Database resources of the National Center for Biotechnology Information in 2025. Nucleic Acids Res, 53(D1), D20-d29. https://doi.org/10.1093/nar/gkae979
Thai, N. Q., Nguyen, H. L., Linh, H. Q., & Li, M. S. (2017). Protocol for fast screening of multi-target drug candidates: Application to Alzheimer’s disease. Journal of Molecular Graphics and Modelling, 77, 121-129. https://doi.org/10.1016/j.jmgm.2017.08.002
Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455-461. https://doi.org/10.1002/jcc.21334
Unoh, Y., Uehara, S., Nakahara, K., Nobori, H., Yamatsu, Y., Yamamoto, S., Maruyama, Y., Taoda, Y., Kasamatsu, K., & Suto, T. (2022). Discovery of S-217622, a noncovalent oral SARS-CoV-2 3CL protease inhibitor clinical candidate for treating COVID-19. Journal of Medicinal Chemistry, 65(9), 6499-6512. https://doi.org/10.1021/acs.jmedchem.2c00117
Vuong, Q. V., Nguyen, T. T., & Li, M. S. (2015). A new method for navigating optimal direction for pulling ligand from binding pocket: application to ranking binding affinity by steered molecular dynamics. Journal of Chemical Information and Modeling, 55(12), 2731-2738. https://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00386
Walensky, R. P., Walke, H. T., & Fauci, A. S. (2021). SARS-CoV-2 Variants of Concern in the United States—Challenges and Opportunities. Jama, 325(11), 1037-1038. https://doi.org/10.1001/jama.2021.2294
Wallace, A. C., Laskowski, R. A., & Thornton, J. M. (1995). LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng, 8(2), 127-134. https://doi.org/10.1093/protein/8.2.127
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