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A Generalized Prediction Model of Inhibition of Neuraminidase of Influenza Virus of Various Strains

  • A. V. Mikurova
  • V. S. Skvortsov
Article
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Abstract

Preliminary results of construction of an overall model for prediction of IC50 values of inhibitors of neuraminidase from any influenza virus strains are presented. We used MM-PBSA (MM-GBSA) energy terms calculated for the complexes obtained after modeling of 30 variants of neuraminidase structures, subsequent docking and molecular dynamics simulation as independent variables in prediction equations. The structures of known neuraminidase-inhibiting drugs (oseltamivir, zanamivir and peramivir) and a neuraminidase substrate (MUNANA) were used as ligands. Use of calculation parameters of neuraminidase-inhibitor complexes did not result in the correlation equation with acceptable parameters (R2 ≤ 0.3). However, if information about binding energy of the substrate used for neuraminidase assay (and IC50 detection) is included the resultant IC50 prediction equations became significant (R2 ≥ 0.55). It is concluded that models joining not only various ligands but also numerous variants of the target protein involved in their binding should take into consideration not only the IC50 value as the target parameter but also binding of the neuraminidase substrate used for experimental determination of the IC50 value. In this case the use of modelled proteins is reasonable. The predictive power of such models depends critically on the quality of the modeling of the ligand-protein complexes.

Keywords:

influenza virus neuraminidase inhibitors computational methods QSAR 

Notes

REFERENCES

  1. 1.
    Air, G.M. and Laver, W.G., Proteins: Structure, Function, and Bioinformatics, 1989, vol. 6, no. 4, pp. 341–356.CrossRefGoogle Scholar
  2. 2.
    Mishin, V.P., Hayden, F.G., and Gubareva, L.V., Antimicrob. Agents Chemother., 2005, vol. 11, pp. 4515–4520.CrossRefGoogle Scholar
  3. 3.
    Breslav, N.V., Shevchenko, E.S., Abramov, D.D., Prilipov, A.G., Zhuravleva, M.M., Oskerko, T.A., Kolobukhina, L.V., Merkulova, L.N., Shchelkanov, M.Yu., Burtseva, E.I., and L’vov, D.K., Vopr. Virusol., 2013, vol. 58, no. 1, pp. 28–32.Google Scholar
  4. 4.
    Babu, Y.S., Chand, P., Bantia, S., Kotian, P., Dehghani, A., El-Kattan, Y., Lin, T.H., Hutchison, T.L., Elliott, A.J., Parker, C.D., Ananth, S.L., Horn, L.L., Laver, G.W., and Montgomery, J.A., J. Med. Chem., 2000, vol. 43, no. 19, pp. 3482–3486.CrossRefGoogle Scholar
  5. 5.
    Whittington, A. and Bethell, R., Exp. Opin. Ther. Patents, 1995, vol. 5, no. 8, pp. 793−803.CrossRefGoogle Scholar
  6. 6.
    Mikurova, A.V., Rybina, A.V., and Skvortsov, V.S., Biomed. Khim., 2016, vol. 62, pp. 691–703. doi 10.18097/PBMC20166206691CrossRefGoogle Scholar
  7. 7.
    Kollman, P.A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., Donini, O., Cieplak, P., Srinivasan, J., Case, D.A., and Cheatham, T.E., 3rd, Acc. Chem. Res., 2000, vol. 33, pp. 889–897.CrossRefGoogle Scholar
  8. 8.
    Allen, W.J., Balius, T.E., Mukherjee, S., Brozell, S.R., Moustakas, D.T., Lang, P.T., Case, D.A., Kuntz, I.D., and Rizzo, R.C., J. Comput. Chem., 2015, vol. 36, no. 15, pp. 1132–1156.CrossRefGoogle Scholar
  9. 9.
    Case, D.A., Darden, T., Cheatham, T.E., III, Simmerling, C., Wang, J., Duke, R.E., Luo, R., Merz, K.M., Pearlman, D.A., and Crowley, M., AMBER 9. University of California, San Francisco, 2006.Google Scholar
  10. 10.
    Shcherbakov, A.M., Levina, I.S., Kulikova, L.E., Fedyushkina, I.V., Skvortsov, V.S., Veselovsky, A.V., Kuznetsov, Yu.V., Zavarzin, I.V., Biomed. Khim., 2016, vol. 62, no. 3, pp. 290–294. doi 10.18097/ PBMC20166203290CrossRefGoogle Scholar
  11. 11.
    Govorkova, E.A., Leneva, I.A., Goloubeva, O.G., Bush, K., and Webster, R.G., Antimicrob. Agents Chemother., 2001, vol. 45, no. 10, pp. 2723–2732.CrossRefGoogle Scholar
  12. 12.
    Song, M.S., Marathe, B.M., Kumar, G., Wong, S.S., Rubrum, A., Zanin, M., Choi, Y.K., Webster, R.G., Govorkova, E.A., and Webby, R.J., J. Virology, 2015, vol. 89, 21, pp. 10891–10900.CrossRefGoogle Scholar
  13. 13.
    https://www.rcsb.org/.Google Scholar
  14. 14.
    SYBYL-X, Tripos, St. Louis, MO, USA.Google Scholar
  15. 15.
    The Federal Research Center “Informatics and Management” of the Russian Academy of Sciences, Retrieved May 11, 2018, from URL: http://frccsc.ru.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Institute of Biomedical ChemistryMoscowRussia

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