Skip to main content
Log in

Selectivity analysis of 5-(arylthio)-2,4-diaminoquinazolines as inhibitors of Candida albicans dihydrofolate reductase by molecular dynamics simulations

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

A series of 5-(arylthio)-2,4-diaminoquinazolines are known as selective inhibitors of dihydrofolate reductase (DHFR) from Candida albicans. We have performed docking and molecular dynamics simulations of these inhibitors with C. albicans and human DHFR to understand the basis for selectivity of these agents. Study was performed on a selected set of 10 compounds with variation in structure and activity. Molecular dynamics simulations were performed at 300 K for 45 ps with equilibration for 10 ps. Trajectory data was analyzed on the basis of hydrogen bond interactions, energy of binding and conformational energy difference. The results indicate that hydrogen bonds formed between the compound and the active site residues are responsible for inhibition and higher potency. The selectivity index, i.e the ratio of I50 against human DHFR to I50 against fungal DHFR, is mainly determined by the conformation adapted by the compounds within the active site of two enzymes. Since the human DHFR active site is rigid, the compound is trapped in a higher energy conformation. This energy difference between the two conformations ΔE mainly governs the selectivity against fungal DHFR. The information generated from this analysis of potency and selectivity should be useful for further work in the area of antifungal research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Kuyper, L.F., In Perun, T.J. and Propst, C.L. (Eds.) Computer-Aided Drug Design Methods and Applications, Marcel Dekker Inc., New York, NY and Basel, 1989, pp. 327-369.

    Google Scholar 

  2. McCormack, J., In Hansch, C. and Sammes, P.G. (Eds.) Comprehensive Medicinal Chemistry, Vol. 2., Pergamon Press, London, 1990, pp. 271-298.

    Google Scholar 

  3. Volz, K.W., Matthews, D.A., Alden, R.A., Freer, S.T., Hansch, C., Kaufman, B.T. and Kraut, J., J. Biol. Chem., 257 (1982) 2528.

    Google Scholar 

  4. Matthews, D.A., Alden, R.A., Bolin, J.T., Filman, D.J., Freer, S.T., Hamlin, R., Hol, W.G.J., Kisluik, R.L., Pastore, E.J., Plante, L.T., Xuong, N. and Kraut, J., J. Biol. Chem., 253 (1978) 6946.

    Google Scholar 

  5. Bolin, J.T., Filman, D.J., Matthews, D.A., Hamlin, R.C. and Kraut, J., J. Biol. Chem., 257 (1982) 13650.

    Google Scholar 

  6. Whitlow, M., Howard, A.J., Stewart, D., Hardman, K.D., Kuyper, L.F., Baccanari, D.P., Fling, M.E. and Tansik, R.L., J. Biol. Chem., 272 (1997) 30289.

    Google Scholar 

  7. Chan, J.H., Hong, J.S., Kuyper, L.F., Baccanari, D.P., Joyner, S.S., Tansik, R.L., Boytos, C.M. and Rudolph, S.K., J. Med. Chem., 38 (1995) 3608.

    Google Scholar 

  8. Kuyper, L.F., Baccanari, D.P., Jones, M.L., Hunter, R.N., Tansik, R.L., Joyner, S.S., Boytos, C.M., Rudolph, S.K., Knick, V., Robert Wilson, H., Marc Caddell, J., Friedman, H.S., Comley, J.C.W. and Stables, J.N., J. Med. Chem., 39 (1996) 892.

    Google Scholar 

  9. Inoue, A., Kawai, T., Wakita, M., Iimura, Y., Sugimoto, H. and Kawakami, Y., J. Med. Chem., 39 (1996) 4460.

    Google Scholar 

  10. Yamamoto, Y., Ishihara, Y. and Kuntz, I.D., J. Med. Chem., 37 (1994) 3141.

    Google Scholar 

  11. Hariprasad, V. and Kulkarni, V.M., J. Mol. Model., 3 (1997) 443.

    Google Scholar 

  12. Rastelli, G. and Costantino, L., Bioorg. Med. Chem. Lett., 8 (1998) 641.

    Google Scholar 

  13. Kyle, D.J., Chakravarty, S., Sinsko, J.A. and Stormann, T.M., J. Med. Chem., 37 (1994) 1347.

    Google Scholar 

  14. Furet, P., Caravatti, G., Lydon, N., Priestle, J.P., Sowadski, J.M., Trinks, U. and Traxler, P., J. Comput.-Aided Mol. Design, 9 (1995) 465.

    Google Scholar 

  15. Winter, H.D. and Herdewijn, P., J. Med. Chem., 39 (1996) 4727.

    Google Scholar 

  16. Wang, S., Karanietz, M.G., Blumberg, P.M., Marquez, V.E. and Milne, G.W.A., J. Med. Chem., 39 (1996) 2541.

    Google Scholar 

  17. Insight II 97.0 Molecular Modeling software is available from Molecular Simulations Inc., San Diego, CA.

  18. Discover 3.0.0 User Guide, October 1995, Molecular Simulations Inc. San Diego, CA.

  19. Gilson, M.K. and Honig, B., Proteins, 4 (1988) 7.

    Google Scholar 

  20. Kulkarni, S.S. and Kulkarni, V.M., J. Chem. Inf. Comput. Sci., 39 (1999) 1128.

    Google Scholar 

  21. Hariprasad, V. and Kulkarni, V.M., J. Mol. Recogn., 9 (1996) 95.

    Google Scholar 

  22. Selassie, C.D., Gan, W., Kallander, L.S. and Klein, T.E., J. Med. Chem., 41 (1998) 4261.

    Google Scholar 

  23. Birdsall, B., Feeney, J., Tendler, S.J.B., Hammond, S.J. and Roberts, G.C.K., Biochemistry, 28 (1989) 2297.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gokhale, V.M., Kulkarni, V.M. Selectivity analysis of 5-(arylthio)-2,4-diaminoquinazolines as inhibitors of Candida albicans dihydrofolate reductase by molecular dynamics simulations. J Comput Aided Mol Des 14, 495–506 (2000). https://doi.org/10.1023/A:1008189724803

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008189724803

Navigation