Influenza A virus (IAV) encodes a polymerase composed of three subunits: PA, with endonuclease activity, PB1 with polymerase activity and PB2 with host RNA five-prime cap binding site. Their cooperation and stepwise activation include a process called cap-snatching, which is a crucial step in the IAV life cycle. Reproduction of IAV can be blocked by disrupting the interaction between the PB2 domain and the five-prime cap. An inhibitor of this interaction called pimodivir (VX-787) recently entered the third phase of clinical trial; however, several mutations in PB2 that cause resistance to pimodivir were observed. First major mutation, F404Y, causing resistance was identified during preclinical testing, next the mutation M431I was identified in patients during the second phase of clinical trials. The mutation H357N was identified during testing of IAV strains at Centers for Disease Control and Prevention. We set out to provide a structural and thermodynamic analysis of the interactions between cap-binding domain of PB2 wild-type and PB2 variants bearing these mutations and pimodivir. Here we present four crystal structures of PB2-WT, PB2-F404Y, PB2-M431I and PB2-H357N in complex with pimodivir. We have thermodynamically analysed all PB2 variants and proposed the effect of these mutations on thermodynamic parameters of these interactions and pimodivir resistance development. These data will contribute to understanding the effect of these missense mutations to the resistance development and help to design next generation inhibitors.

1st Faculty of Medicine Charles University Kateřinská 1660 32 12108 Prague 2 Czech Republic

Department of Biochemistry Faculty of Science Charles University Hlavova 8 12800 Prague 2 Czech Republic

Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Gilead Sciences and IOCB Research Center Flemingovo n 2 16610 Prague 6 Czech Republic

Zobrazit více v PubMed

Taubenberger J.K., Kash J.C. Influenza Virus Evolution, Host Adaptation, and Pandemic Formation. Cell Host Microbe. 2010;7:440–451. doi: 10.1016/j.chom.2010.05.009. PubMed DOI PMC

Mertz D., Kim T.H., Johnstone J., Lam P.-P., Science M., Kuster S.P., Fadel S., Tran D., Fernandez E., Bhatnagar N., et al. Populations at risk for severe or complicated influenza illness: Systematic review and meta-analysis. BMJ. 2013;347:f5061. doi: 10.1136/bmj.f5061. PubMed DOI PMC

Shi F., Xie Y., Shi L., Xu W. Viral RNA polymerase: A promising antiviral target for influenza A virus. Curr. Med. Chem. 2013;20:3923–3934. doi: 10.2174/09298673113209990208. PubMed DOI

Peng Q., Liu Y., Peng R., Wang M., Yang W., Song H., Chen Y., Liu S., Han M., Zhang X., et al. Structural insight into RNA synthesis by influenza D polymerase. Nat. Microbiol. 2019;4:1750–1759. doi: 10.1038/s41564-019-0487-5. PubMed DOI

Pflug A., Guilligay D., Reich S., Cusack S. Structure of influenza A polymerase bound to the viral RNA promoter. Nat. Cell Biol. 2014;516:355–360. doi: 10.1038/nature14008. PubMed DOI

Dias A.T., Bouvier D., Crépin T., McCarthy A.A., Hart D.J., Baudin F., Cusack S., Ruigrok R.W.H. The cap-snatching endonuclease of influenza virus polymerase resides in the PA subunit. Nat. Cell Biol. 2009;458:914–918. doi: 10.1038/nature07745. PubMed DOI

Reich S., Guilligay D., Pflug A., Malet H., Berger I., Crépin T., Hart D.J., Lunardi T., Nanao M., Ruigrok R.W.H., et al. Structural insight into cap-snatching and RNA synthesis by influenza polymerase. Nat. Cell Biol. 2014;516:361–366. doi: 10.1038/nature14009. PubMed DOI

Hayden F.G., Shindo N. Influenza virus polymerase inhibitors in clinical development. Curr. Opin. Infect. Dis. 2019;32:176–186. doi: 10.1097/QCO.0000000000000532. PubMed DOI PMC

Clark M.P., Ledeboer M.W., Davies I., Byrn R.A., Jones S.M., Perola E., Tsai A., Jacobs M., Nti-Addae K., Bandarage U.K., et al. Discovery of a Novel, First-in-Class, Orally Bioavailable Azaindole Inhibitor (VX-787) of Influenza PB2. J. Med. Chem. 2014;57:6668–6678. doi: 10.1021/jm5007275. PubMed DOI

Byrn R.A., Jones S.M., Bennett H.B., Bral C., Clark M.P., Jacobs M.D., Kwong A.D., Ledeboer M.W., Leeman J.R., McNeil C.F., et al. Preclinical Activity of VX-787, a First-in-Class, Orally Bioavailable Inhibitor of the Influenza Virus Polymerase PB2 Subunit. Antimicrob. Agents Chemother. 2015;59:1569–1582. doi: 10.1128/AAC.04623-14. PubMed DOI PMC

Finberg R.W., Lanno R., Anderson D., Fleischhackl R., Van Duijnhoven W., Kauffman R.S., Kosoglou T., Vingerhoets J., Leopold L. Phase 2b Study of Pimodivir (JNJ-63623872) as Monotherapy or in Combination with Oseltamivir for Treatment of Acute Uncomplicated Seasonal Influenza A: TOPAZ Trial. J. Infect. Dis. 2018;219:1026–1034. doi: 10.1093/infdis/jiy547. PubMed DOI

Gubareva L.V., Bethell R., Hart G.J., Murti K.G., Penn C.R., Webster R.G. Characterization of mutants of influenza A virus selected with the neuraminidase inhibitor 4-guanidino-Neu5Ac2en. J. Virol. Mar. 1996;70:1818–1827. doi: 10.1128/JVI.70.3.1818-1827.1996. PubMed DOI PMC

Gubareva L., Mishin V., Patel M., Chesnokov A., Cruz J.D.L., Nguyen H., Lollis L., Hodges E., Jang Y., Barnes J., et al. Seasonal and other influenza viruses with reduced susceptibility to Baloxavir and Pimodivir; Proceedings of the OPTIONS X for the Control of Influenza; Suntec City, Singapore. 28 August–1 September 2019; No. 10750.

Zhu W., Zhu Y., Qin K., Yu Z., Gao R., Yu H., Zhou J., Shu Y. Mutations in Polymerase Genes Enhanced the Virulence of 2009 Pandemic H1N1 Influenza Virus in Mice. PLoS ONE. 2012;7:e33383. doi: 10.1371/journal.pone.0033383. PubMed DOI PMC

Fechter P., Mingay L., Sharps J., Chambers A., Fodor E., Brownlee G.G. Two Aromatic Residues in the PB2 Subunit of Influenza a RNA Polymerase Are Crucial for Cap Binding. J. Biol. Chem. 2003;278:20381–20388. doi: 10.1074/jbc.M300130200. PubMed DOI

Trevejo J.M., Asmal M., Vingerhoets J., Polo R., Robertson S., Jiang Y., Kieffer T.L., Leopold L. Pimodivir treatment in adult volunteers experimentally inoculated with live influenza virus: A Phase IIa, randomized, double-blind, placebo-controlled study. Antivir Ther. 2018;23:335–344. doi: 10.3851/IMP3212. PubMed DOI

Lee S., Jacobson I., Xiao H., Sanchez E., Feese M., Lin B., Adolphson J., Uher L. Development of a new class of broad spectrum influenza PB2 inhibitors; Proceedings of the OPTIONS X for the Control of Influenza; Suntec City, Singapore. 28 August–1 September 2019; No. 11025.

Darby J.F., Hopkins A.P., Shimizu S., Roberts S.M., Brannigan J.A., Turkenburg J.P., Thomas G.H., Hubbard R.E., Fischer M. Water Networks Can Determine the Affinity of Ligand Binding to Proteins. J. Am. Chem. Soc. 2019;141:15818–15826. doi: 10.1021/jacs.9b06275. PubMed DOI

Fox J.M., Kang K., Sastry M., Sherman W., Sankaran B., Zwart P.H., Whitesides G.M. Water-Restructuring Mutations Can Reverse the Thermodynamic Signature of Ligand Binding to Human Carbonic Anhydrase. Angew. Chem. Int. Ed. 2017;56:3833–3837. doi: 10.1002/anie.201609409. PubMed DOI

Pokorná J., Pachl P., Karlukova E., Hejdánek J., Řezáčová P., Machara A., Hudlický J., Konvalinka J., Kožíšek M. Kinetic, Thermodynamic, and Structural Analysis of Drug Resistance Mutations in Neuraminidase from the 2009 Pandemic Influenza Virus. Viruses. 2018;10:339. doi: 10.3390/v10070339. PubMed DOI PMC

Krimmer S.G., Klebe G. Thermodynamics of protein–ligand interactions as a reference for computational analysis: How to assess accuracy, reliability and relevance of experimental data. J. Comput. Mol. Des. 2015;29:867–883. doi: 10.1007/s10822-015-9867-y. PubMed DOI

Kabsch W. XDS. Acta Crystallogr. D Biol. Crystallogr. 2010;66:125–132. doi: 10.1107/S0907444909047337. PubMed DOI PMC

Kabsch W. Integration, scaling, space-group assignment and post-refinement. Acta Crystallogr. Sect. D Biol. Crystallogr. 2010;66:133–144. doi: 10.1107/S0907444909047374. PubMed DOI PMC

A Vagin A., Teplyakov A. MOLREP: An Automated Program for Molecular Replacement. J. Appl. Crystallogr. 1997;30:1022–1025. doi: 10.1107/S0021889897006766. DOI

Winn M.D., Ballard C.C., Cowtan K.D., Dodson E.J., Emsley P., Evans P.R., Keegan R.M., Krissinel E.B., Leslie A.G.W., McCoy A., et al. Overview of theCCP4 suite and current developments. Acta Crystallogr. Sect. D Biol. Crystallogr. 2011;67:235–242. doi: 10.1107/S0907444910045749. PubMed DOI PMC

Emsley P., Cowtan K. Coot: Model-building tools for molecular graphics. Acta Crystallogr. Sect. D Biol. Crystallogr. 2004;60:2126–2132. doi: 10.1107/S0907444904019158. PubMed DOI

Murshudov G.N., Skubák P., Lebedev A.A., Pannu N.S., Steiner R.A., Nicholls R.A., Winn M.D., Long F., Vagin A.A. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr. Sect. D Biol. Crystallogr. 2011;67:355–367. doi: 10.1107/S0907444911001314. PubMed DOI PMC

Chen V.B., Arendall W.B., Headd J.J., Keedy D.A., Immormino R.M., Kapral G.J., Murray L.W., Richardson J.S., Richardson D.C. MolProbity: All-atom structure validation for macromolecular crystallography. Acta Crystallogr. Sect. D Biol. Crystallogr. 2009;66:12–21. doi: 10.1107/S0907444909042073. PubMed DOI PMC

Delano W.L. The PyMOL Molecular Graphics System. [(accessed on 10 March 2020)]; Available online: http://www.pymol.org.

Case D., Babin V., Berryman J.T., Betz R.M. Amber 2014. [(accessed on 10 June 2020)];2014 Available online: http://ambermd.org.

Olsson M.H.M., Søndergaard C.R., Rostkowski M., Jensen J.H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions. J. Chem. Theory Comput. 2010;7:525–537. doi: 10.1021/ct100578z. PubMed DOI

Pettersen E.F., Goddard T.D., Huang C.C., Couch G.S., Greenblatt D.M., Meng E.C., Ferrin T.E. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 2004;25:1605–1612. doi: 10.1002/jcc.20084. PubMed DOI

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. 2004;25:1157–1174. doi: 10.1002/jcc.20035. PubMed DOI

Fanfrlík J., Bronowska A.K., ŘezáčJ P., Konvalinka J., Hobza P. A Reliable Docking/Scoring Scheme Based on the Semiempirical Quantum Mechanical PM6-DH2 Method Accurately Covering Dispersion and H-Bonding: HIV-1 Protease with 22 Ligands. J. Phys. Chem. B. 2010;114:12666–12678. doi: 10.1021/jp1032965. PubMed DOI

Lepšík M., Řezáč J., Kolář M.H., Pecina A., Hobza P., Fanfrlík J. The Semiempirical Quantum Mechanical Scoring Function for In Silico Drug Design. ChemPlusChem. 2013;78:921–931. doi: 10.1002/cplu.201300199. PubMed DOI

Pecina A., Eyrilmez S.M., Köprülüoğlu C., Miriyala V.M., Lepšík M., Fanfrlík J., Řezáč J., Hobza P. SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design. ChemPlusChem. 2020;85:2362–2371. doi: 10.1002/cplu.202000120. PubMed DOI

Stewart J.J.P. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. J. Mol. Model. 2007;13:1173–1213. doi: 10.1007/s00894-007-0233-4. PubMed DOI PMC

Řezáč J., Hobza P. Advanced Corrections of Hydrogen Bonding and Dispersion for Semiempirical Quantum Mechanical Methods. J. Chem. Theory Comput. 2012;8:141–151. doi: 10.1021/ct200751e. PubMed DOI

Stewart J.J.P. Optimization of parameters for semiempirical methods IV: Extension of MNDO, AM1, and PM3 to more main group elements. J. Mol. Model. 2004;10:155–164. doi: 10.1007/s00894-004-0183-z. PubMed DOI

Řezáč J. Cuby: An integrative framework for computational chemistry. J. Comput. Chem. 2016;37:1230–1237. doi: 10.1002/jcc.24312. PubMed DOI

Řezáč J. Cuby—Ruby Framework for Computational Chemistry, Version 4. [(accessed on 15 November 2020)]; Available online: http://cuby4.molecular.cz.

Kříž K., Řezáč J. Reparametrization of the COSMO Solvent Model for Semiempirical Methods PM6 and PM7. J. Chem. Inf. Model. 2018;59:229–235. doi: 10.1021/acs.jcim.8b00681. PubMed DOI