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大学・研究所にある論文を検索できる 「Promising SARS-CoV-2 main protease inhibitor ligand-binding modes evaluated using LB-PaCS-MD/FMO」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。
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Promising SARS-CoV-2 main protease inhibitor ligand-binding modes evaluated using LB-PaCS-MD/FMO

ヘンパサトポン, コビット 原田, 隆平 Wilasluck, Patcharin Deetanya, Peerapon Sukandar, Edwin R. Chavasiri, Warinthorn Suroengrit, Aphinya Boonyasuppayakorn, Siwaporn Rungrotmongkol, Thanyada Wangkanont, Kittikhun 重田, 育照 筑波大学 DOI:36289271

2023.01.23

概要

Parallel cascade selection molecular dynamics‑based ligand binding‑path sampling (LB‑PaCS‑MD) was combined with fragment molecular orbital (FMO) calculations to reveal the ligand path from an aqueous solution to the SARS‑CoV‑2 main protease (Mpro) active site and to customise a ligand‑ binding pocket suitable for delivering a potent inhibitor. Rubraxanthone exhibited mixed‑inhibition antiviral activity against SARS‑CoV‑2 Mpro, relatively low cytotoxicity, and high cellular inhibition.

However, the atomic inhibition mechanism remains ambiguous. LB‑PaCS‑MD/FMO is a hybrid ligand‑binding evaluation method elucidating how rubraxanthone interacts with SARS‑CoV‑2 Mpro.

In the first step, LB‑PaCS‑MD, which is regarded as a flexible docking, efficiently samples a set of ligand‑binding pathways. After that, a reasonable docking pose of LB‑PaCS‑MD is evaluated by the FMO calculation to elucidate a set of protein–ligand interactions, enabling one to know the binding affinity of a specified ligand with respect to a target protein. A possible conformation was proposed for rubraxanthone binding to the SARS‑CoV‑2 Mpro active site, and allosteric inhibition was elucidated by combining blind docking with k‑means clustering. The interaction profile, key binding residues, and considerable interaction were elucidated for rubraxanthone binding to both Mpro sites. Integrated LB‑PaCS‑MD/FMO provided a more reasonable complex structure for ligand binding at the SARS‑ CoV‑2 Mpro active site, which is vital for discovering and designing antiviral drugs.

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参考文献

1. Du Toit, A. Outbreak of a novel coronavirus. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-0332-0 (2020).

2. Guan, W.-J. et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 382, 1708–1720. https://doi.org/ 10.1056/NEJMoa2002032 (2020).

3. National Center for Immunization and Respiratory Diseases (NCIRD), D. o. V. D. Risk for COVID‑19 Infection, Hospitalization, and Death By Age Group. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death- by-age.html (2022).

4. Silveira, D. et al. COVID-19: Is there evidence for the use of herbal medicines as adjuvant symptomatic therapy?. Front. Pharmacol. 11, 1479 (2020).

5. Jiménez-Avalos, G. et al. Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 MPRO. Sci. Rep. 11, 15452. https://doi.org/10.1038/s41598-021-94951-6 (2021).

6. Hengphasatporn, K. et al. Halogenated baicalein as a promising antiviral agent toward SARS-CoV-2 main protease. J. Chem. Inf. Model. 62, 1498–1509 (2022).

7. Kowalczyk, M. et al. Drug design strategies for the treatment of viral disease. Plant phenolic compounds and their derivatives. Front. Pharmacol. 12, 709104. https://doi.org/10.3389/fphar.2021.709104 (2021).

8. Ma, Y. et al. Exploration of plant-derived natural polyphenols toward COVID-19 main protease inhibitors: DFT, molecular docking approach, and molecular dynamics simulations. RSC Adv. 12, 5357–5368. https://doi.org/10.1039/D1RA07364H (2022).

9. Yuanita, E. et al. Molecular docking of xanthone derivatives as therapeutic agent for Covid-19. Molekul 17(1), 1. https://doi.org/ 10.20884/1.jm.2022.17.1.5600 (2022).

10. Liang, Y., Luo, D., Gao, X. & Wu, H. Inhibitory effects of garcinone E on fatty acid synthase. RSC Adv. 8, 8112–8117. https://doi. org/10.1039/C7RA13246H (2018).

11. Abdallah, H. M. et al. Mangostanaxanthones III and IV: Advanced glycation end-product inhibitors from the pericarp of Garcinia mangostana. J. Nat. Med. 71, 216–226. https://doi.org/10.1007/s11418-016-1051-8 (2017).

12. Ying, Y.-M. et al. Antiproliferative prenylated xanthones from the pericarps of Garcinia mangostana. Chem. Nat. Compd. 53, 555–556. https://doi.org/10.1007/s10600-017-2047-7 (2017).

13. Chusri, S. et al. Antibacterial and anti-biofilm effects of a polyherbal formula and its constituents against coagulase-negative and -positive staphylococci isolated from bovine mastitis. J. Appl. Anim. Res. 45, 364–372. https://doi.org/10.1080/09712119.2016. 1193021 (2017).

14. Tjahjani, S. Antimalarial activity of Garcinia mangostana L. rind and its synergistic effect with artemisinin in vitro. BMC Comple‑ ment Altern. Med. 17, 131. https://doi.org/10.1186/s12906-017-1649-8 (2017).

15. Patil, P. et al. In vitro and in vivo studies reveal α-Mangostin, a xanthonoid from Garcinia mangostana, as a promising natural antiviral compound against chikungunya virus. Virol. J. 18, 47. https://doi.org/10.1186/s12985-021-01517-z (2021).

16. Tarasuk, M. et al. Alpha-mangostin inhibits both dengue virus production and cytokine/chemokine expression. Virus Res. 240, 180–189. https://doi.org/10.1016/j.virusres.2017.08.011 (2017).

17. Sanachai, K., Mahalapbutr, P., Sanghiran Lee, V., Rungrotmongkol, T. & Hannongbua, S. In silico elucidation of potent inhibitors and rational drug design against SARS-CoV-2 papain-like protease. J. Phys. Chem. B 125, 13644–13656. https://doi.org/10.1021/ acs.jpcb.1c07060 (2021).

18. Mahase, E. Covid-19: Pfizer’s paxlovid is 89% effective in patients at risk of serious illness, company reports. BMJ 375, n2713. https://doi.org/10.1136/bmj.n2713 (2021).

19. Unoh, Y. et al. Discovery of S-217622, a noncovalent oral SARS-CoV-2 3CL protease inhibitor clinical candidate for treating COVID-19. J. Med. Chem. https://doi.org/10.1021/acs.jmedchem.2c00117 (2022).

20. Cui, W., Yang, K. & Yang, H. Recent progress in the drug development targeting SARS-CoV-2 main protease as treatment for COVID-19. Front. Mol. Biosci. https://doi.org/10.3389/fmolb.2020.616341 (2020).

21. Deetanya, P. et al. Interaction of 8-anilinonaphthalene-1-sulfonate with SARS-CoV-2 main protease and its application as a fluo- rescent probe for inhibitor identification. Comput. Struct. Biotechnol. J. 19, 3364–3371. https://doi.org/10.1016/j.csbj.2021.05.053 (2021).

22. Nutho, B. et al. Why are lopinavir and ritonavir effective against the newly emerged coronavirus 2019? Atomistic insights into the inhibitory mechanisms. Biochemistry https://doi.org/10.1021/acs.biochem.0c00160 (2020).

23. Kidera, A., Moritsugu, K., Ekimoto, T. & Ikeguchi, M. Allosteric regulation of 3CL protease of SARS-CoV-2 and SARS-CoV observed in the crystal structure ensemble. J. Mol. Biol. 433, 167324. https://doi.org/10.1016/j.jmb.2021.167324 (2021).

24. Carli, M., Sormani, G., Rodriguez, A. & Laio, A. Candidate binding sites for allosteric inhibition of the SARS-CoV-2 main protease from the analysis of large-scale molecular dynamics simulations. J. Phys. Chem. Lett. 12, 65–72. https://doi.org/10.1021/acs.jpcle tt.0c03182 (2021).

25. Sherman, W., Day, T., Jacobson, M. P., Friesner, R. A. & Farid, R. Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem. 49, 534–553. https://doi.org/10.1021/jm050540c (2006).

26. Bolia, A., Gerek, Z. N. & Ozkan, S. B. BP-Dock: A flexible docking scheme for exploring protein-ligand interactions based on unbound structures. J. Chem. Inf. Model. 54, 913–925. https://doi.org/10.1021/ci4004927 (2014).

27. Sabbadin, D. & Moro, S. Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR–ligand recognition pathway in a nanosecond time scale. J. Chem. Inf. Model. 54, 372–376. https://doi.org/10.1021/ci400766b (2014).

28. Harada, R. & Kitao, A. Parallel cascade selection molecular dynamics (PaCS-MD) to generate conformational transition pathway. J. Chem. Phys. 139, 035103. https://doi.org/10.1063/1.4813023 (2013).

29. Harada, R. & Shigeta, Y. Temperature-shuffied parallel cascade selection molecular dynamics accelerates the structural transitions of proteins. J. Comput. Chem. 38, 2671–2674. https://doi.org/10.1002/jcc.25060 (2017).

30. Harada, R. & Shigeta, Y. Dynamic specification of initial structures in parallel cascade selection molecular dynamics (PaCS-MD) efficiently promotes biologically relevant rare events. Bull. Chem. Soc. Jpn. 90, 1236–1243. https://doi.org/10.1246/bcsj.20170177 (2017).

31. Kitao, A., Harada, R., Nishihara, Y. & Tran, D. P. Parallel cascade selection molecular dynamics for efficient conformational sam- pling and free energy calculation of proteins. AIP Conf. Proc. https://doi.org/10.1063/1.4968639 (2016).

32. Fujita, J. et al. Identification of the key interactions in structural transition pathway of FtsZ from Staphylococcus aureus. J. Struct. Biol. 198, 65–73. https://doi.org/10.1016/j.jsb.2017.04.008 (2017).

33. Aida, H., Shigeta, Y. & Harada, R. Ligand binding path sampling based on parallel cascade selection molecular dynamics: LB- PaCS-MD. Materials 15, 1490. https://doi.org/10.3390/ma15041490 (2022).

34. Hengphasatporn, K., Kungwan, N. & Rungrotmongkol, T. Binding pattern and susceptibility of epigallocatechin gallate against envelope protein homodimer of Zika virus: A molecular dynamics study. J. Mol. Liq. 274, 140–147. https://doi.org/10.1016/j.mol- liq.2018.10.111 (2019).

35. Günther, S. et al. X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease. Science 372, 642–646. https://doi.org/10.1126/science.abf7945 (2021).

36. Sun, Z. et al. An extended conformation of SARS-CoV-2 main protease reveals allosteric targets. Proc. Natl. Acad. Sci. 119, e2120913119. https://doi.org/10.1073/pnas.2120913119 (2022).

37. Dubanevics, I. & McLeish, T. C. B. Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease. J. R. Soc. Interface 18, 20200591. https://doi.org/10.1098/rsif.2020.0591 (2021).

38. Menéndez Cintia, A., Byléhn, F., Perez-Lemus Gustavo, R., Alvarado, W. & de Pablo Juan, J. Molecular characterization of ebselen binding activity to SARS-CoV-2 main protease. Sci. Adv. 6, eabd0345. https://doi.org/10.1126/sciadv.abd0345 (2020).

39. Holm, L. Using Dali for protein structure comparison. Methods Mol. Biol. 2112, 29–42. https://doi.org/10.1007/978-1-0716-0270- 6_3 (2020).

40. Yu, J., Zhou, Y., Tanaka, I. & Yao, M. Roll: A new algorithm for the detection of protein pockets and cavities with a rolling probe sphere. Bioinformatics 26, 46–52. https://doi.org/10.1093/bioinformatics/btp599 (2010).

41. Forbes, C. R. et al. Insights into thiol–aromatic interactions: A stereoelectronic basis for S-H/π interactions. J. Am. Chem. Soc. 139, 1842–1855. https://doi.org/10.1021/jacs.6b08415 (2017).

42. Nutho, B. et al. Why are lopinavir and ritonavir effective against the newly emerged coronavirus 2019? Atomistic insights into the inhibitory mechanisms. Biochemistry 59, 1769–1779. https://doi.org/10.1021/acs.biochem.0c00160 (2020).

43. Mengist, H. M., Dilnessa, T. & Jin, T. Structural basis of potential inhibitors targeting SARS-CoV-2 main protease. Front. Chem. 9, 922898. https://doi.org/10.3389/fchem.2021.622898 (2021).

44. Jin, Z. et al. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature 582, 289–293. https://doi.org/10.1038/ s41586-020-2223-y (2020).

45. Sukandar, E. R. et al. Tetrandraxanthones A-I, prenylated and geranylated xanthones from the stem bark of Garcinia tetrandra. J. Nat. Prod. 82, 1312–1318. https://doi.org/10.1021/acs.jnatprod.9b00046 (2019).

46. Sukandar, E. R. et al. Xanthones and biphenyls from the stems of Garcinia cylindrocarpa and their cytotoxicity. Fitoterapia 130, 112–117. https://doi.org/10.1016/j.fitote.2018.08.019 (2018).

47. Xue, X. et al. Production of authentic SARS-CoV M(pro) with enhanced activity: Application as a novel tag-cleavage endopeptidase for protein overproduction. J. Mol. Biol. 366, 965–975. https://doi.org/10.1016/j.jmb.2006.11.073 (2007).

48. Wansri, R. et al. Semi-synthesis of N-aryl amide analogs of piperine from Piper nigrum and evaluation of their antitrypanoso- mal, antimalarial, and anti-SARS-CoV-2 main protease activities. Molecules 27, 2841. https://doi.org/10.3390/molecules270928 41 (2022).

49. Gaussian 16 Rev. C.01 (Wallingford, 2016).

50. Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174. https://doi.org/10.1002/jcc.20035 (2004).

51. Case, D. A., Belfon, K., Ben-Shalom, I. Y., Brozell, S. R., Cerutti, D. S., Cheatham, III, T. E., Cruzeiro, V. W. D., Darden, T. A., Duke, R. E., Giambasu, G., Gilson, M. K., Gohlke, H., Goetz, A. W., Harris, R., Izadi, S., Izmailov, S. A., Kasavajhala, K., Kovalenko, A., Krasny, R., Kurtzman, T., Lee, T. S., LeGrand, S., Li, P., Lin, C., Liu, J., Luchko, T., Luo, R., Man, V., Merz, K. M., Miao, Y., Mikhailovskii, O., Monard, G., Nguyen, H., Onufriev, A., Pan, F., Pantano, S., Qi, R., Roe, D. R., Roitberg, A., Sagui, C., Schott- Verdugo, S., Shen, J., Simmerling, C. L., Skrynnikov, N. R., Smith, J., Swails, J., Walker, R. C., Wang, J., Wilson, L., Wolf, R. M., Wu, X., Xiong, Y., Xue, Y., York, D. M. & Kollman, P. A. AMBER 2020 (2020).

52. Dolinsky, T. J., Nielsen, J. E., McCammon, J. A. & Baker, N. A. PDB2PQR: An automated pipeline for the setup of Poisson–Boltz- mann electrostatics calculations. Nucleic Acids Res. 32, W665–W667. https://doi.org/10.1093/nar/gkh381 (2004).

53. Tian, C. et al. ff19SB: Amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J. Chem. Theory Comput. 16, 528–552. https://doi.org/10.1021/acs.jctc.9b00591 (2020).

54. Ravindranath, P. A., Forli, S., Goodsell, D. S., Olson, A. J. & Sanner, M. F. AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Comput. Biol. 11, e1004586. https://doi.org/10.1371/journal.pcbi.1004586 (2015).

55. Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and Python bindings. J. Chem. Inf. Model. 61, 3891–3898. https://doi.org/10.1021/acs.jcim.1c00203 (2021).

56. Aida, H., Shigeta, Y. & Harada, R. Ligand binding path sampling based on parallel cascade selection molecular dynamics: LB- PaCS-MD. Materials (Basel) 15, 1490. https://doi.org/10.3390/ma15041490 (2022).

57. Kitaura, K., Sugiki, S.-I., Nakano, T., Komeiji, Y. & Uebayasi, M. Fragment molecular orbital method: Analytical energy gradients. Chem. Phys. Lett. 336, 163–170. https://doi.org/10.1016/S0009-2614(01)00099-9 (2001).

58. Fedorov, D. G. The fragment molecular orbital method: Theoretical development, implementation in GAMESS, and applications. Wiley Interdiscip. Rev. Comput. Mol. Sci. 7, e1322. https://doi.org/10.1002/wcms.1322 (2017).

59. Virtanen, S. I., Niinivehmas, S. P. & Pentikäinen, O. T. Case-specific performance of MM-PBSA, MM-GBSA, and SIE in virtual screening. J. Mol. Graph. Model. 62, 303–318. https://doi.org/10.1016/j.jmgm.2015.10.012 (2015).

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