DOI QR코드

DOI QR Code

Ligand Based CoMFA, CoMSIA and HQSAR Analysis of CCR5 Antagonists

  • Gadhe, Changdev G. (Department of Bio-New Drug Development, College of Medicine, Chosun University) ;
  • Lee, Sung-Haeng (Department of Bio-New Drug Development, College of Medicine, Chosun University) ;
  • Madhavan, Thirumurthy (Department of Bio-New Drug Development, College of Medicine, Chosun University) ;
  • Kothandan, Gugan (Department of Bio-New Drug Development, College of Medicine, Chosun University) ;
  • Choi, Du-Bok (Biotechnology Lab, BK Company R&D Center) ;
  • Cho, Seung-Joo (Department of Bio-New Drug Development, College of Medicine, Chosun University)
  • Received : 2010.06.24
  • Accepted : 2010.08.19
  • Published : 2010.10.20

Abstract

In this study, we have developed QSAR models for a series of 38 piperidine-4-carboxamide CCR5 antagonists using CoMFA, CoMSIA and HQSAR methods. Developed models showed good statistics in terms of $q^2$ and $r^2$ values. Best predictions obtained with standard CoMFA model ($r^2$ = 0.888, $q^2$ = 0.651) and combined CoMSIA model ($r^2$ = 0.892, $q^2$ = 0.665) with electrostatics and H-bond acceptor parameter. The validity of developed models was assessed by test set of 9 compounds, which showed good predictive correlation coefficient for CoMFA (0.804) and CoMSIA (0.844). Bootstrapped analysis showed statistically significant and robust CoMFA (0.968) and CoMSIA (0.936) models. Best HQSAR model was obtained with a $q^2$ of 0.662 and $r^2$ of 0.936 using atom, connection, hydrogen, donor and acceptor as parameters and fragment size (7-10) with optimum number of 6 components. Predictive power of developed HQSAR model was proved by test set and it was found to be 0.728.

Keywords

References

  1. Chermann, J. C.; Barre-Sinoussi, F.; Dauguet, C.; Brun-Vezinet, F.; Rouzioux, C.; Rozenbaum, W.; Montagnier, L. Antibiot. Chemother. 1983, 32, 48.
  2. Dorr, P.; Westby, M.; Dobbs, S.; Griffin, P.; Irvine, B.; Macartney, M.; Mori, J.; Rickett, G.; Smith-Burchnell, C.; Napier, C. Antimicrob. Agents Chemother. 2005, 49, 4721. https://doi.org/10.1128/AAC.49.11.4721-4732.2005
  3. Furtado, M. R.; Callaway, D. S.; Phair, J. P.; Kunstman, K. J.; Stanton, J. L.; Macken, C. A.; Perelson, A. S.; Wolinsky, S. M. New Engl. J. Med. 1999, 340, 1614. https://doi.org/10.1056/NEJM199905273402102
  4. Coffin, J.; Haase, A.; Levy, J. A.; Montagnier, L.; Oroszlan, S.; Teich, N.; Temin, H.; Toyoshima, K.; Varmus, H.; Vogt, P. Nature 1986, 321, 10.
  5. Koot, M.; van’t Wout, A. B.; Kootstra, N. A.; Goede, R. E.; Tersmette, M.; Schuitemaker, H. J. Infect. Dis. 1996, 173, 349. https://doi.org/10.1093/infdis/173.2.349
  6. Fackler, O. T.; Peterlin, B. M. Curr. Biol. 2000, 10, 1005. https://doi.org/10.1016/S0960-9822(00)00654-0
  7. Piot, P.; Bartos, M.; Ghys, P. D.; Walker, N.; Schwartlander, B. Nature 2001, 410, 968. https://doi.org/10.1038/35073639
  8. Strader, C. D.; Fong, T. M.; Tota, M. R.; Underwood, D.; Dixon, R. A. F. Annu. Rev. Biochem. 1994, 63, 101. https://doi.org/10.1146/annurev.bi.63.070194.000533
  9. Cocchi, F.; DeVico, A. L.; Garzino-Demo, A.; Arya, S. K.; Gallo, R. C.; Lusso, P. Science 1995, 270, 1811. https://doi.org/10.1126/science.270.5243.1811
  10. Cocchi, F.; DeVico, A. L.; Garzino-Demo, A.; Lusso, P.; Gallo, R. C. Science 1996, 274, 1393. https://doi.org/10.1126/science.274.5291.1393
  11. Alkhatib, G.; Combadiere, C.; Broder, C. C.; Feng, Y.; Kennedy, P. E.; Murphy, P. M.; Berger, E. A. Science 1996, 272, 1955. https://doi.org/10.1126/science.272.5270.1955
  12. Li, G.; Haney, K. M.; Kellogg, G. E.; Zhang, Y. J. Chem. Inf. Model. 2009, 49, 120. https://doi.org/10.1021/ci800356a
  13. Strizki, J. M.; Xu, S.; Wagner, N. E.; Wojcik, L.; Liu, J.; Hou, Y.; Endres, M.; Palani, A.; Shapiro, S.; Clader, J. W. Proc. Nat. Acad. Sci. USA 2001, 98, 12718. https://doi.org/10.1073/pnas.221375398
  14. Tagat, J. R.; McCombie, S. W.; Nazareno, D.; Labroli, M. A.; Xiao, Y.; Steensma, R. W.; Strizki, J. M.; Baroudy, B. M.; Cox, K.; Lachowicz, J. J. Med. Chem. 2004, 47, 2405. https://doi.org/10.1021/jm0304515
  15. Strizki, J. M.; Tremblay, C.; Xu, S.; Wojcik, L.; Wagner, N.; Gonsiorek, W.; Hipkin, R. W.; Chou, C. C.; Pugliese-Sivo, C.; Xiao Y. Antimicrob. Agents Chemother. 2005, 49, 4911. https://doi.org/10.1128/AAC.49.12.4911-4919.2005
  16. Maeda, K.; Nakata, H.; Koh, Y.; Miyakawa, T.; Ogata, H.; Takaoka, Y.; Shibayama, S.; Sagawa, K.; Fukushima, D.; Moravek, J. J. Virol. 2004, 78, 8654. https://doi.org/10.1128/JVI.78.16.8654-8662.2004
  17. Nichols, W. G.; Steel, H. M.; Bonny, T.; Adkison, K.; Curtis, L.; Millard, J.; Kabeya, K.; Clumeck, N. Antimicrob. Agents Chemother. 2008, 52, 858. https://doi.org/10.1128/AAC.00821-07
  18. Aher, Y. D.; Agrawal, A.; Bharatam, P. V.; Garg, P. J. Mol. Model. 2007, 13, 519. https://doi.org/10.1007/s00894-007-0173-z
  19. Afantitis, A.; Melagraki, G.; Sarimveis, H.; Koutentis, P. A.; Markopoulos, J.; Igglessi-Markopoulou, O. J. Comput.-Aided Mol. Des. 2006, 20, 83. https://doi.org/10.1007/s10822-006-9038-2
  20. Song, M.; Breneman, C. M.; Sukumar, N. Bioorg. Med. Chem. 2004, 12, 489. https://doi.org/10.1016/j.bmc.2003.10.019
  21. Zhuo, Y.; Kong, R.; Cong, X.; Chen, W.; Wang, C. Eur. J. Med. Chem. 2008, 43, 2724. https://doi.org/10.1016/j.ejmech.2008.01.040
  22. Xu, Y.; Liu, H.; Niu, C.; Luo, C.; Luo, X.; Shen, J.; Chen, K.; Jiang, H. Bioorg. Med. Chem. 2004, 12, 6193. https://doi.org/10.1016/j.bmc.2004.08.045
  23. Imamura, S.; Nishikawa, Y.; Ichikawa, T.; Hattori, T.; Matsushita, Y.; Hashiguchi, S.; Kanzaki, N.; Iizawa, Y.; Baba, M.; Sugihara, Y. Bioorg. Med. Chem. 2005, 13, 397. https://doi.org/10.1016/j.bmc.2004.10.013
  24. Cramer, R. D.; Patterson, D. E.; Bunce, J. D. J. Am. Chem. Soc. 1988, 110, 5959. https://doi.org/10.1021/ja00226a005
  25. Klebe, G.; Abraham, U.; Mietzner, T. J. Med. Chem. 1994, 37, 4130. https://doi.org/10.1021/jm00050a010
  26. Hurst T, Heritage T. 213th ACS Natl. Meeting, San Francisco, CA, 1997, CINF 019.
  27. S. H. R. SYBYL8.1; Tripos Inc., St. Louis, MO 63144 USA.
  28. Ash, S.; Cline, M. A.; Homer, R. W.; Hurst, T.; Smith, G. B. J. Chem. Inf. Comput. Sci. 1997, 37, 71. https://doi.org/10.1021/ci960109j
  29. Dunn, W. J.; Wold, S.; Edlund, V.; Hellherg, S.; Gasteiger, J. Quant. Struct.-Act. Relat. 1984, 3, 131. https://doi.org/10.1002/qsar.19840030402
  30. Wold, S.; Sjostrom, M.; Eriksson, L. Chemom. Intell. Lab. Syst. 2001, 58, 109. https://doi.org/10.1016/S0169-7439(01)00155-1
  31. Cramer, R. D. Perspect. Drug Discovery Des. 1993, 1, 269. https://doi.org/10.1007/BF02174528

Cited by

  1. Structural Insights from Binding Poses of CCR2 and CCR5 with Clinically Important Antagonists: A Combined In Silico Study vol.7, pp.3, 2012, https://doi.org/10.1371/journal.pone.0032864
  2. Enhancement of P-gylcoprotein modulators of arylmethylamine-phenyl derivatives: an integrative modeling approach vol.22, pp.5, 2013, https://doi.org/10.1007/s00044-012-0246-0
  3. Computational modeling of human coreceptor CCR5 antagonist as a HIV-1 entry inhibitor: using an integrated homology modeling, docking, and membrane molecular dynamics simulation analysis approach vol.31, pp.11, 2013, https://doi.org/10.1080/07391102.2012.732342
  4. Investigation of the Binding Site of CCR2 using 4-Azetidinyl-1-aryl-cyclohexane Derivatives: A Membrane Modeling and Molecular Dynamics Study vol.34, pp.11, 2013, https://doi.org/10.5012/bkcs.2013.34.11.3429
  5. Characterization of Binding Mode of the Heterobiaryl gp120 Inhibitor in HIV-1 Entry: A Molecular Docking and Dynamics Simulation Study vol.34, pp.8, 2013, https://doi.org/10.5012/bkcs.2013.34.8.2466
  6. The nociceptin receptor (NOPR) and its interaction with clinically important agonist molecules: a membrane molecular dynamics simulation study vol.10, pp.12, 2014, https://doi.org/10.1039/C4MB00323C
  7. Discovery of a potential lead compound for treating leprosy with dapsone resistance mutation in M. leprae folP1 vol.12, pp.7, 2016, https://doi.org/10.1039/C6MB00225K
  8. Combined 3D-QSAR modeling and molecular docking study on azacycles CCR5 antagonists vol.1045, pp.None, 2010, https://doi.org/10.1016/j.molstruc.2013.03.062
  9. In silicocharacterization of binding mode of CCR8 inhibitor: homology modeling, docking and membrane based MD simulation study vol.33, pp.11, 2010, https://doi.org/10.1080/07391102.2014.1002006
  10. Ligand based virtual screening for identifying potent inhibitors against viral neuraminidase: An in silico approach vol.9, pp.1, 2010, https://doi.org/10.1016/j.jtusci.2014.04.007