Development of the Novel Nsp16 Inhibitors as Potential Anti-SARS-CoV-2 Agents


  • Kuojun Zhang School of Pharmacy, China Pharmaceutical University, Nanjing, China
  • Alexey V. Sulimov Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
  • Ivan S. Ilin Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
  • Danil C. Kutov Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
  • Anna S. Taschilova Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
  • Sheng Jiang School of Pharmacy, China Pharmaceutical University, Nanjing, China
  • Tianyu Wang School of Pharmacy, China Pharmaceutical University, Nanjing, China
  • Vladimir B. Sulimov Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
  • Yibei Xiao School of Pharmacy, China Pharmaceutical University, Nanjing, China



nsp16, methyltransferase, virtual screening, protein-ligand binding, enzyme inhibitors, recombinant protein expression, SARS-CoV-2


Computer aided structural based approach was used to find inhibitors of SARS-CoV-2 nsp16 (2’-O-methyltransferase). Docking based virtual screening of three libraries, Enamine Coronavirus Library, Enamine Nucleoside Mimetics Library, and Chemdiv Nucleoside Analogue Library, was performed. In total, 39350 3D-structures of low molecular weight ligands were docked into a model of nsp16 prepared using the structure of 6WKQ complex from the Protein Data Bank. Docking was performed by the SOL docking program. For the best SOL scored ligands, the protein-ligand binding enthalpy was calculated using the PM7 semiempirical quantum-chemical method with the COSMO implicit solvent model. The most promising eleven compounds were purchased and their inhibitory activity against the recombinant viral nsp16 protein was measured using MST assay with Monolith NT.115. As a result, two compounds, Z195979162 and Z1333277068, from Enamine Coronavirus Library demonstrated dissociation constants Kd for nsp16/nsp10 complex equal to 2.0 and 5.0 μM. The relative stability of these ligands in their docked positions in the nsp16 S-adenosylmethionine (SAM) binding site was confirmed in the molecular dynamics simulations along 70 ns trajectories. Z195979162 and Z1333277068 compounds belong to two chemical classes: 1,4-disubstituted tetrahydropyridines and derivatives of pyrazole-5-carboxamide, respectively, and can be good starting points for further hit optimization in the field of nsp16 inhibitors design.


Chemdiv, nucleoside mimetics library,

Enamine, targeted libraries,

VMD: Visual Molecular Dynamics,

Stewart Computational Chemistry. MOPAC2016 (2016),

Schrodinger Release 2019-3: LigPrep (2019),

Baell, J.B., Holloway, G.A.: New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays. Journal of Medicinal Chemistry 53(7), 2719–2740 (2010).

Berman, H.M., Westbrook, J., Feng, Z., et al.: The Protein Data Bank. Nucleic Acids Research 28(1), 235–242 (01 2000).

Bobieva, O., Bobrovs, R., Kaepe, I., et al.: Potent SARS-CoV-2 mRNA Cap Methyltransferase Inhibitors by Bioisosteric Replacement of Methionine in SAM Cosubstrate. ACS Medicinal Chemistry Letters 12(7), 1102–1107 (2021).

Bobrovs, R., Kanepe, I., Narvaiss, N., et al.: Discovery of SARS-CoV-2 Nsp14 and Nsp16 Methyltransferase Inhibitors by High-Throughput Virtual Screening. Pharmaceuticals 14(12) (2021).

Chang, L.J., Chen, T.H.: NSP16 2’-O-MTase in Coronavirus Pathogenesis: Possible Prevention and Treatments Strategies. Viruses 13(4) (2021).

Citarella, A., Dimasi, A., Moi, D., et al.: Recent Advances in SARS-CoV-2 Main Protease Inhibitors: From Nirmatrelvir to Future Perspectives. Biomolecules 13(9) (2023).

Decroly, E., Debarnot, C., Ferron, F., et al.: Crystal Structure and Functional Analysis of the SARS-Coronavirus RNA Cap 2’-O-Methyltransferase nsp10/nsp16 Complex. PLOS Pathogens 7(5), 1–14 (05 2011).

Decroly, E., Imbert, I., Coutard, B., et al.: Coronavirus Nonstructural Protein 16 Is a Cap-0 Binding Enzyme Possessing (Nucleoside-2’O)-Methyltransferase Activity. Journal of Virology 82(16), 8071–8084 (2008).

El Hassab, M.A., Ibrahim, T.M., Shoun, A.A., et al.: In silico identification of potential SARS COV-2 2’-O-methyltransferase inhibitor: fragment-based screening approach and MM-PBSA calculations. RSC Advances 11, 16026–16033 (2021).

Feikin, D., Higdon, M., Abu-Raddad, L., et al.: Duration of effectiveness of vaccines against SARS-CoV-2 infection and COVID-19 disease: results of a systematic review and meta-regression. The Lancet 399(10328), 924–944 (02 2022).

Ferron, F., Decroly, E., Selisko, B., Canard, B.: The viral RNA capping machinery as a target for antiviral drugs. Antiviral Research 96(1), 21–31 (2012).

Gao, K., Wang, R., Chen, J., et al.: Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chemical Reviews 122(13), 11287–11368 (2022).

Gil, C., Ginex, T., Maestro, I., et al.: COVID-19: Drug Targets and Potential Treatments. Journal of Medicinal Chemistry 63(21), 12359–12386 (2020).

Gomes, J.P.A., de Oliveira Rocha, L., Leal, C.E.Y., de Alencar Filho, E.B.: Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2. Journal of Molecular Structure 1261, 132951 (2022).

Gowers, R., Linke, M., Barnoud, J., et al.: MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations. In: SciPy. pp. 98–105 (01 2016).

Halgren, T.A.: MMFF VII. Characterization of MMFF94, MMFF94s, and other widely available force fields for conformational energies and for intermolecular-interaction energies and geometries. Journal of Computational Chemistry 20(7), 730–748 (1999).<730::AID-JCC8>3.0.CO;2-T

Hunter, J.: Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering 9, 90–95 (06 2007).

Klamt, A., Schuurmann, G.: COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. Journal of the Chemical Society, Perkin Transactions 2 pp. 799–805 (1993).

Klima, M., Khalili Yazdi, A., Li, F., et al.: Crystal structure of SARS-CoV-2 nsp10nsp16 in complex with small molecule inhibitors, SS148 and WZ16. Protein Science 31(9), e4395 (2022).

Krafcikova, P., Silhan, J., Nencka, R., Boura, E.: Structural analysis of the SARS-CoV-2 methyltransferase complex involved in RNA cap creation bound to sinefungin. Nature Communications 11(1), 3717 (07 2020).

Kutov, D.C., Katkova, E.V., Sulimov, A.V., et al : Influence of the method of hydrogen atoms incorporation into the target protein on the protein-ligand binding energy. Bulletin of the South Ural State University. Series “Mathematical Modelling, Programming and Computer Software” 10(3), 94–107 (2017).

Lin, S., Chen, H., Ye, F., et al.: Crystal structure of SARS-CoV-2 nsp10/nsp16 2’-Omethylase and its implication on antiviral drug design. Signal Transduction and Targeted Therapy 5(1), 131 (12 2020).

Lipsitch, M., Krammer, F., Regev-Yochay, G., et al.: SARS-CoV-2 breakthrough infections in vaccinated individuals: measurement, causes and impact. Nature Reviews Immunology 22(1), 57–65 (12 2021).

Malone, B., Urakova, N., Snijder, E., Campbell, E.: Structures and functions of coronavirus replication-transcription complexes and their relevance for SARS-CoV-2 drug design. Nature Reviews Molecular Cell Biology 23(1), 21–39 (11 2021).

Mohammad, A., Alshawaf, E., Marafie, S.K., et al.: Molecular Simulation-Based Investigation of Highly Potent Natural Products to Abrogate Formation of the nsp10nsp16 Complex of SARS-CoV-2. Biomolecules 11(4), 573 (2021).

Nguyen, H.L., Thai, N.Q., Li, M.S.: Identifying inhibitors of NSP16-NSP10 of SARS-CoV-2 from large databases. Journal of Biomolecular Structure and Dynamics 41(15), 7045–7054 (2023).

Owen, D.R., Allerton, C.M.N., Anderson, A.S., et al.: An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19. Science 374(6575), 1586–1593 (2021).

Phillips, J.C., Hardy, D.J., Maia, J.D.C., et. al.: Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of Chemical Physics 153(4), 044130 (2020).

Ramanathan, A., Robb, G.B., Chan, S.H.: mRNA capping: biological functions and applications. Nucleic Acids Research 44(16), 7511–7526 (06 2016).

Romano, M., Ruggiero, A., Squeglia, F., et al.: A Structural View of SARS-CoV-2 RNA Replication Machinery: RNA Synthesis, Proofreading and Final Capping. Cells 9(5), 1267 (2020).

Romanov, A.N., Jabin, S.N., Martynov, Y.B., et al.: Surface Generalized Born method: a simple, fast and precise implicit solvent model beyond the Coulomb approximation. The Journal of Physical Chemistry A: Molecules, Clusters, and Aerosols 108(43), 9323–9327 (2004).

Rosas-Lemus, M., Minasov, G., Shuvalova, L., et al.: High-resolution structures of the SARS-CoV-2 2’O-methyltransferase reveal strategies for structure-based inhibitor design. Science Signaling 13(651), eabe1202 (2020).

Shi, L., Wen, Z., Song, Y., et al.: Computational investigation of potent inhibitors against SARS-CoV-2 2’-O-methyltransferase (nsp16): Structure-based pharmacophore modeling, molecular docking, molecular dynamics simulations and binding free energy calculations. Journal of Molecular Graphics and Modelling 117, 108306 (2022).

Silva, L.R., da Silva Santos-Junior, P.F., de Andrade Brandao, J., et al.: Druggable targets from coronaviruses for designing new antiviral drugs. Bioorganic & Medicinal Chemistry 28(22), 115745 (2020).

Stewart, J.: Optimization of parameters for semiempirical methods VI: More modifications to the NDDO approximations and re-optimization of parameters. Journal of molecular modeling 19(1), 1–32 (1 2013).

Stewart, J.J.: Application of localized molecular orbitals to the solution of semiempirical self-consistent field equations. International Journal of Quantum Chemistry 58(2), 133–146 (1996).;2-Z

Sulimov, A., Kutov, D., Ilin, I., et al.: Novel Inhibitors of 2’-O-Methyltransferase of the SARS-CoV-2 Coronavirus. Molecules 27(9), 2721 (2022).

Sulimov, A.V., Kutov, D.C., Oferkin, I.V., et al.: Application of the Docking Program SOL for CSAR Benchmark. Journal of Chemical Information and Modeling 53(8), 1946–1956 (2013).

Sulimov, V.B., Ilin, I.S., Kutov, D.C., Sulimov, A.V.: Development of docking programs for Lomonosov supercomputer. Journal of the Turkish Chemical Society Section A: Chemistry 7(1), 259–276 (Feb 2020).

Tazikeh-Lemeski, E., Moradi, S., Raoufi, R., et al.: Targeting SARS-COV-2 non-structural protein 16: a virtual drug repurposing study. Journal of Biomolecular Structure and Dynamics 39(13), 4633–4646 (2021).

Tiwari, V., Beer, J.C., Sankaranarayanan, N.V., et al.: Discovering small-molecule therapeutics against SARS-CoV-2. Drug Discovery Today 25(8), 1535–1544 (2020).

Vanommeslaeghe, K., Hatcher, E., Acharya, C., et al.: CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of Computational Chemistry 31(4), 671–690 (2010).

Viswanathan, T., Arya, S., Chan, S.H., et al.: Structural basis of RNA cap modification by SARS-CoV-2. Nature Communications 11(1), 3718 (07 2020).

Voevodin, V.V., Antonov, A.S., Nikitenko, D.A., et al.: Supercomputer lomonosov-2: Large scale, deep monitoring and fine analytics for the user community. Supercomputing Frontiers and Innovations 6(2), 4–11 (Jun 2019).

Walls, A.C., Park, Y.J., Tortorici, M.A., et al.: Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 181(2), 1735 (2020).

Wrapp, D., Wang, N., Corbett, K., et al.: Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 367(6483), 1260–1263 (02 2020).

Wu, C., Liu, Y., Yang, Y., et al.: Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharmaceutica Sinica B 10(5), 766–788 (2020).

Wu, Y., Li, Z., Zhao, Y.S., et al.: Therapeutic targets and potential agents for the treatment of COVID-19. Medicinal Research Reviews 41(3), 1775–1797 (2021).




How to Cite

Zhang, K., Sulimov, A. V., Ilin, I. S., Kutov, D. C., Taschilova, A. S., Jiang, S., Wang, T., Sulimov, V. B., & Xiao, Y. (2024). Development of the Novel Nsp16 Inhibitors as Potential Anti-SARS-CoV-2 Agents. Supercomputing Frontiers and Innovations, 11(1), 51–66.