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Research Articles

In silico and in vitro assays reveal potential inhibitors against 3CLpro main protease of SARS-CoV-2

ORCID Icon, , , , , ORCID Icon & ORCID Icon show all
Pages 12800-12811 | Received 10 May 2021, Accepted 29 Aug 2021, Published online: 22 Sep 2021

References

  • Achilonu, I., Iwuchukwu, E. A., Achilonu, O. J., Fernandes, M. A., & Sayed, Y. (2020). Targeting the SARS-CoV-2 main protease using FDA-approved isavuconazonium, a P2-P3 α-ketoamide derivative and pentagastrin: An in-silico drug discovery approach. Journal of Molecular Graphics and Modelling, 101, 107730. https://doi.org/10.1016/j.jmgm.2020.107730
  • Alexpandi, R., De Mesquita, J. F., Pandian, S. K., & Ravi, A. V. (2020). Quinolines-based SARS-CoV-2 3CLpro and RdRp inhibitors and spike-RBD-ACE2 inhibitor for drug-repurposing against COVID-19: An in silico analysis. Frontiers in Microbiology, 11, 1796–1–1796–15.
  • Astuti, I. (2020). Ysrafil, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): An overview of viral structure and host response. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14, 407–412. https://doi.org/10.1016/j.dsx.2020.04.020
  • Baker, N. A., Sept, D., Joseph, S., Holst, M. J., & McCammon, J. A. (2001). Electrostatics of nanosystems: Application to microtubules and the ribosome. Proceedings of the National Academy of Sciences of the United States of America, 98(18), 10037–10041.
  • Bartók, A. P., Kondor, R., & Csányi, G. (2013). On representing chemical environments. Physical Review B, 87(18), 184115–1–184115–16. https://doi.org/10.1103/PhysRevB.87.184115
  • Berendsen, H., van der Spoel, D., & van Drunen, R. (1995). GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communications, 91(1–3), 43–56. https://doi.org/10.1016/0010-4655(95)00042-E. https://doi.org/10.1016/0010-4655(95)00042-E
  • Best, R. B., Zhu, X., Shim, J., Lopes, P. E. M., Mittal, J., Feig, M., & MacKerell, A. D. (2012). Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. Journal of Chemical Theory and Computation, 8(9), 3257–3273. https://doi.org/10.1021/ct300400x
  • Bharadwaj, S., Azhar, E. I., Kamal, M. A., Bajrai, L. H., Dubey, A., Jha, K., Yadava, U., Kang, S. G., & Dwivedi, V. D. (2020). SARS-CoV-2 Mpro inhibitors: Identification of anti-SARS-CoV-2 Mpro compounds from FDA approved drugs. Journal of Biomolecular Structure & Dynamics, 1–16. https://doi.org/10.1080/07391102.2020.1842807
  • Bussi, G., Donadio, D., & Parrinello, M. (2007). Canonical sampling through velocity rescaling. The Journal of Chemical Physics, 126(1), 014101.
  • Cornpropst, M., Collis, P., Collier, J., Babu, Y. S., Wilson, R., Zhang, J., Fang, L., Zong, J., & Sheridan, W. P. (2016). Safety, pharmacokinetics, and pharmacodynamics of avoralstat, an oral plasma kallikrein inhibitor: Phase 1 study. Allergy, 71(12), 1676–1683. https://doi.org/10.1111/all.12930
  • De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016). Comparing molecules and solids across structural and alchemical space. Physical Chemistry Chemical Physics: PCCP, 18(20), 13754–13769. https://doi.org/10.1039/c6cp00415f
  • Dutta, M., & Iype, E. (2021). Peptide inhibitors against SARS-CoV-2 2'-O-methyltransferase involved in RNA capping: A computational approach. Biochemistry and Biophysics Reports, 27, 101069. https://doi.org/10.1016/j.bbrep.2021.101069
  • Faber, F. A., Christensen, A. S., Huang, B., & Von Lilienfeld, O. A. (2018). Alchemical and structural distribution based representation for universal quantum machine learning. The Journal of Chemical Physics, 148(24), 241717. https://doi.org/10.1063/1.5020710
  • Fehr, A. R., & Perlman, S. (2015). Coronaviruses: An overview of their replication and pathogenesis. Methods in Molecular Biology (Clifton, N.J.), 1282, 1–23. https://doi.org/10.1007/978-1-4939-2438-7_1
  • Fu, L., Ye, F., Feng, Y., Yu, F., Wang, Q., Wu, Y., Zhao, C., Sun, H., Huang, B., Niu, P., Song, H., Shi, Y., Li, X., Tan, W., Qi, J., & Gao, G. F. (2020). Both boceprevir and GC376 efficaciously inhibit SARS-CoV-2 by targeting its main protease. Nature Communications, 11(1), 4417. https://doi.org/10.1038/s41467-020-18233-x
  • Gallicchio, E., Deng, N., He, P., Wickstrom, L., Perryman, A. L., Santiago, D. N., Forli, S., Olson, A. J., & Levy, R. M. (2014). Virtual screening of integrase inhibitors by large scale binding free energy calculations: The SAMPL4 challenge. Journal of Computer-Aided Molecular Design, 28(4), 475–490. https://doi.org/10.1007/s10822-014-9711-9
  • Gentile, F., Agrawal, V., Hsing, M., Ton, A.-T., Ban, F., Norinder, U., Gleave, M. E., & Cherkasov, A. (2020). Deep docking: A deep learning platform for augmentation of structure based drug discovery. ACS Central Science, 6(6), 939–949. https://doi.org/10.1021/acscentsci.0c00229
  • Ghahremanpour, M. M., Tirado-Rives, J., Deshmukh, M., Ippolito, J. A., Zhang, C.-H., Cabeza de Vaca, I., Liosi, M.-E., Anderson, K. S., & Jorgensen, W. L. (2020). Identification of 14 known drugs as inhibitors of the main protease of SARS-CoV-2. ACS Medicinal Chemistry Letters, 11(12), 2526–2533. https://doi.org/10.1021/acsmedchemlett.0c00521
  • Ghanbari, R., Teimoori, A., Sadeghi, A., Mohamadkhani, A., Rezasoltani, S., Asadi, E., Jouyban, A., & Sumner, S. C. (2020). Existing antiviral options against SARS-CoV-2 replication in COVID-19 patients. Future Microbiology, 15, 1747–1758. https://doi.org/10.2217/fmb-2020-0120
  • Gorbalenya, A. E., & Snijder, E. J. (1996). Viral cysteine proteinases. Perspectives in Drug Discovery and Design: PD3, 6(1), 64–86. https://doi.org/10.1007/BF02174046
  • Goyal, B., & Goyal, D. (2020). Targeting the dimerization of the main protease of coronaviruses: A potential broad-spectrum therapeutic strategy. ACS Combinatorial Science, 22(6), 297–305. https://doi.org/10.1021/acscombsci.0c00058
  • Grum-Tokars, V., Ratia, K., Begaye, A., Baker, S. C., & Mesecar, A. D. (2008). Evaluating the 3C-like protease activity of SARS-Coronavirus: Recommendations for standardized assays for drug discovery. Virus Research, 133(1), 63–73. https://doi.org/10.1016/j.virusres.2007.02.015
  • Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., Von Lilienfeld, O. A., Müller, K. R., & Tkatchenko, A. (2015). Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. The Journal of Physical Chemistry Letters, 6(12), 2326–2331. https://doi.org/10.1021/acs.jpclett.5b00831
  • He, J., Hu, L., Huang, X., Wang, C., Zhang, Z., Wang, Y., Zhang, D., & Ye, W. (2020). Potential of coronavirus 3C-like protease inhibitors for the development of new anti-SARS-CoV-2 drugs: Insights from structures of protease and inhibitors. International Journal of Antimicrobial Agents, 56(2), 106055. https://doi.org/10.1016/j.ijantimicag.2020.106055
  • Huang, B., & Von Lilienfeld, O. A. (2016). Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. The Journal of Chemical Physics, 145(16), 161102. https://doi.org/10.1063/1.4964627
  • Hung, H.-C., Ke, Y.-Y., Huang, S. Y., Huang, P.-N., Kung, Y.-A., Chang, T.-Y., Yen, K.-J., Peng, T.-T., Chang, S.-E., Huang, C.-T., Tsai, Y.-R., Wu, S.-H., Lee, S.-J., Lin, J.-H., Liu, B.-S., Sung, W.-C., Shih, S.-R., Chen, C.-T., & Hsu, J. T.-A. (2020). Discovery of M protease inhibitors encoded by SARS-CoV-2. Antimicrobial Agents and Chemotherapy, 64(9), e00872–20. https://doi.org/10.1128/AAC.00872-20
  • Huo, H., & Rupp, M. (2017). Unified representation of molecules and crystals for machine learning. 1–6. http://arxiv.org/abs/1704.06439.
  • Iype, E., & Gulati, S. (2020). Understanding the asymmetric spread and case fatality rate (CFR) for COVID-19 among countries. medRxiv, 1–9. https://doi.org/10.1101/2020.04.21.20073791
  • Jena, N. (2021). Drug targets, mechanisms of drug action, and therapeutics against SARS-CoV-2. Chemical Physics Impact, 2, 100011. https://doi.org/10.1016/j.chphi.2021.100011
  • Jena, N. R. (2020). Role of different tautomers in the base-pairing abilities of some of the vital antiviral drugs used against COVID-19. Physical Chemistry Chemical Physics, 22(48), 28115–28122. https://doi.org/10.1039/D0CP05297C
  • Jena, N. R., Pant, S., & Srivastava, H. K. (2021). Artificially expanded genetic information systems (AEGISs) as potent inhibitors of the RNA-dependent RNA polymerase of the SARS-CoV-2. Journal of Biomolecular Structure and Dynamics, 1–17. DOI: 10.1080/07391102.2021.1883112, PMID: 33565387.
  • Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., Duan, Y., Yu, J., Wang, L., Yang, K., Liu, F., Jiang, R., Yang, X., You, T., Liu, X., … Yang, H. (2020). Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature, 582(7811), 289–293. https://doi.org/10.1038/s41586-020-2223-y
  • Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. Journal of Chemical Physics, 79(2), 926–935. https://doi.org/10.1063/1.445869
  • Katritzky, A. R., Hall, C. D., El-Gendy, B. E.-D M., & Draghici, B. (2010). Tautomerism in drug discovery. Journal of Computer-Aided Molecular Design, 24(6–7), 475–484. https://doi.org/10.1007/s10822-010-9359-z
  • Khanjiwala, Z., Khale, A., & Prabhu, A. (2019). Docking structurally similar analogues: Dealing with the false-positive. Journal of Molecular Graphics & Modelling, 93, 107451. https://doi.org/10.1016/j.jmgm.2019.107451
  • Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews. Drug Discovery, 3(11), 935–949. https://doi.org/10.1038/nrd1549
  • Kneller, D. W., Phillips, G., O'Neill, H. M., Jedrzejczak, R., Stols, L., Langan, P., Joachimiak, A., Coates, L., & Kovalevsky, A. (2020). Structural plasticity of SARS-CoV-2 3CL Mpro active site cavity revealed by room temperature X-ray crystallography. Nature Communications, 11(1), 3202–3201. DOI: 0.1038/s41467-020-16954-7. https://doi.org/10.1038/s41467-020-16954-7
  • Koulgi, S., Jani, V., Uppuladinne, M., Sonavane, U., Nath, A. K., Darbari, H., & Joshi, R. (2021). Drug repurposing studies targeting SARS-CoV-2: An ensemble docking approach on drug target 3C-like protease (3CLpro). Journal of Biomolecular Structure & Dynamics, 39(15), 5735–5721.
  • Kumar, S., Sharma, P. P., Shankar, U., Kumar, D., Joshi, S. K., Pena, L., Durvasula, R., Kumar, A., Kempaiah, P., Poonam., & Rathi, B. (2020). Discovery of new hydroxyethylamine analogs against 3CLpro protein target of SARS-CoV-2: Molecular docking, molecular dynamics simulation, and structure–activity relationship studies. Journal of Chemical Information and Modeling, 60(12), 5754–5770. https://doi.org/10.1021/acs.jcim.0c00326
  • Kumari, R., Kumar, R., Consortium, O. S. D. D., & Lynn, A, (2014). g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54(7), 1951–1962. https://doi.org/10.1021/ci500020m
  • Le, T. T., Andreadakis, Z., Kumar, A., Roman, R. G., Tollefsen, S., Saville, M., & Mayhew, S. (2020). The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery, 19, 305–306.
  • Mahdian, S., Ebrahim-Habibi, A., & Zarrabi, M. (2020). Drug repurposing using computational methods to identify therapeutic options for COVID-19. Journal of Diabetes and Metabolic Disorders, 19, 691–699. https://doi.org/10.1007/s40200-020-00546-9
  • Malvezzi, A., de Rezende, L., Izidoro, M. A., Cezari, M. H. S., Juliano, L., & do Amaral, A. T. (2008). Uncovering false positives on a virtual screening search for cruzain inhibitors. Bioorganic & Medicinal Chemistry Letters, 18(1), 350–354. https://doi.org/10.1016/j.bmcl.2007.10.068
  • Manandhar, A., Blass, B. E., Colussi, D. J., Almi, I., Abou-Gharbia, M., Klein, M. L., & Elokely, K. M. (2021). Targeting SARS-CoV-2 M3CLpro by HCV NS3/4a inhibitors: In silico modeling and in vitro screening. Journal of Chemical Information and Modeling, 61(2), 1020–1032. DOI: 10.1021/acs.jcim.0c01457, PMID: 33538596.
  • Milletti, F., & Vulpetti, A. (2010). Tautomer preference in PDB complexes and its impact on structure-based drug discovery. Journal of Chemical Information and Modeling, 50(6), 1062–1074. https://doi.org/10.1021/ci900501c
  • Mobley, D. L., Liu, S., Lim, N. M., Wymer, K. L., Perryman, A. L., Forli, S., Deng, N., Su, J., Branson, K., & Olson, A. J. (2014). Blind prediction of HIV integrase binding from the SAMPL4 challenge. Journal of Computer-Aided Molecular Design, 28(4), 327–345. https://doi.org/10.1007/s10822-014-9723-5
  • Mody, V., Ho, J., Wills, S., Mawri, A., Lawson, L., Ebert, M. C. C. J. C., Fortin, G. M., Rayalam, S., & Taval, S. (2021). Identification of 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents. Communications Biology, 4(1), 93. https://doi.org/10.1038/s42003-020-01577-x
  • 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
  • Muramatsu, T., Takemoto, C., Kim, Y.-T., Wang, H., Nishii, W., Terada, T., Shirouzu, M., & Yokoyama, S. (2016). SARS-CoV 3CL protease cleaves its C-terminal autoprocessing site by novel subsite Cooperativity. Proceedings of the National Academy of Sciences of the United States of America, 113(46), 12997–13002. https://doi.org/10.1073/pnas.1601327113
  • Naqvi, A. A. T., Fatima, K., Mohammad, T., Fatima, U., Singh, I. K., Singh, A., Atif, S. M., Hariprasad, G., Hasan, G. M., & Hassan, M. I. (2020). Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1866(10), 165878–165878. https://doi.org/10.1016/j.bbadis.2020.165878
  • O'Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3, 33. https://doi.org/10.1186/1758-2946-3-33
  • Paul, D., Basu, D., & Ghosh Dastidar, S. (2021). Multi-conformation representation of Mpro identifies promising candidates for drug repurposing against COVID-19. Journal of Molecular Modeling, 27(5), 128. https://doi.org/10.1007/s00894-021-04732-1
  • Peele, K. A., Potla Durthi, C., Srihansa, T., Krupanidhi, S., Ayyagari, V. S., Babu, D. J., Indira, M., Reddy, A. R., & Venkateswarulu, T. (2020). Molecular docking and dynamic simulations for antiviral compounds against SARS-CoV-2: A computational study. Informatics in Medicine Unlocked, 19, 100345. https://doi.org/10.1016/j.imu.2020.100345
  • Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera-A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084
  • Rathnayake, A. D., Zheng, J., Kim, Y., Perera, K. D., Mackin, S., Meyerholz, D. K., Kashipathy, M. M., Battaile, K. P., Lovell, S., Perlman, S., Groutas, W. C., & Chang, K.-O. (2020). 3C-Like protease inhibitors block coronavirus replication in vitro and improve survival in MERS-CoV–infected mice. Science Translational Medicine, 12, eabc5332–1–eabc5332–11. https://doi.org/10.1126/scitranslmed.abc5332
  • Rupp, M. (2015). Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry, 115(16), 1058–1073. https://doi.org/10.1002/qua.24954
  • Rupp, M., Ramakrishnan, R., & von Lilienfeld, O. A. (2015). Machine learning for quantum mechanical properties of atoms in molecules. The Journal of Physical Chemistry Letters, 6(16), 3309–3313. https://doi.org/10.1021/acs.jpclett.5b01456
  • Rupp, M., Tkatchenko, A., Müller, K.-R., Lilienfeld, V., & Anatole, O. (2012). Fast and accurate modeling of molecular atomization energies with machine learning. Physical Review Letters, 108(5), 058301. https://doi.org/10.1103/PhysRevLett.108.058301
  • Singh, R., Gautam, A., Chandel, S., Sharma, V., Ghosh, A., Dey, D., Roy, S., Ravichandiran, V., & Ghosh, D. (2021). Computational screening of FDA approved drugs of fungal origin that may interfere with SARS-CoV-2 spike protein activation, viral RNA replication, and post-translational modification: A multiple target approach. In Silico Pharmacology, 9(1), 27. https://doi.org/10.1007/s40203-021-00089-8
  • Sun, Y. J., Velez, G., Parsons, D. E., Li, K., Ortiz, M. E., Sharma, S., McCray, P. B., Bassuk, A. G., & Mahajan, V. B. (2021). Structure-based phylogeny identifies avoralstat as a TMPRSS2 inhibitor that prevents SARS-CoV-2 infection in mice. The Journal of Clinical Investigation, 131(10), e147973 (1-13). https://doi.org/10.1172/JCI147973
  • Tang, B., He, F., Liu, D., Fang, M., Wu, Z., & Xu, D. (2020). AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2. bioRxiv: The Preprint Server for Biology, 1–26. https://doi.org/10.1101/2020.03.03.972133
  • Ullrich, S., & Nitsche, C. (2020). The SARS-CoV-2 main protease as drug target. Bioorganic & Medicinal Chemistry Letters, 30(17), 127377. https://doi.org/10.1016/j.bmcl.2020.127377
  • Vanommeslaeghe, K., & MacKerell, A. D. Jr. (2012). Automation of the CHARMM General Force Field (CGenFF) I: Bond perception and atom typing. Journal of Chemical Information and Modeling, 52(12), 3144–3154.
  • Vanommeslaeghe, K., Raman, E. P., & MacKerell, A. D. Jr. (2012). Automation of the CHARMM General Force Field (CGenFF) II: Assignment of bonded parameters and partial atomic charges. Journal of Chemical Information and Modeling, 52(12), 3155–3168.
  • Von Lilienfeld, O. A., Ramakrishnan, R., Rupp, M., & Knoll, A. (2015). Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties. International Journal of Quantum Chemistry, 115(16), 1084–1093. https://doi.org/10.1002/qua.24912
  • Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z., & Woolsey, J. (2006). DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 34(Database Issue), D668–D672. https://doi.org/10.1093/nar/gkj067
  • Yuce, M., Cicek, E., Inan, T., Dag, A. B., Kurkcuoglu, O., & Sungur, F. A. (2021). Repurposing of FDA-approved drugs against active site and potential allosteric drug-binding sites of COVID-19 main protease. Proteins: Structure, Function, and Bioinformatics, 1–17. https://doi.org/10.1002/prot.26164.
  • Zhang, J., Xie, B., & Hashimoto, K. (2020). Current status of potential therapeutic candidates for the COVID-19 crisis. Brain, Behavior, and Immunity, 87, 59–73. https://doi.org/10.1016/j.bbi.2020.04.046.

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