Skip to main content
Log in

3D QSAR (COMFA) of a series of potent and highly selective VLA-4 antagonists

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

Abstract

The integrin VLA-4 (α4β1) is involved in the migration of white blood cells to sites of inflammation, and is implicated in the pathology of a variety of diseases including asthma and multiple sclerosis. We report the structure-activity relationships of a series of VLA-4 antagonists that were based upon the integrin-binding sequence of the connecting segment peptide of fibronectin (Leu-Asp-Val), and of VCAM-1 (Ile-Asp-Ser), both natural ligands of VLA-4. We explore variation in the ligand derived peptide portion of these antagonists and also in the novel N-terminal cap, which have discovered through chemical optimization, and which confers high affinity and selectivity. Using the X-ray derived conformation of the Ile-Asp-Ser region of VCAM-1, we rationalize the structure-activity relationships of these antagonists using 3D QSAR (COMFA). The COMFA model was found to be highly predictive with a cross-validated R2 CVof 0.7 and a PRESS of 0.49. The robustness of the model was confirmed by testing the influence of various parameters, including grid size, column filtering, as well as the role of orientation of the aligned molecules. Our results suggest that the VCAM-1 structure is useful in generating highly predictive models of our VLA-4 antagonists. The COMFA model coupled with the knowledge that the peptide amides are tolerant to methylation should prove useful in future peptidomimetic design studies.

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. Leger, O.J., et al., Hum. Antibodies, 8(1997) 3–16.

    Google Scholar 

  2. Molossi, S., et al., J. Clin. Invest., 95(1995) 2601–2610.

    Google Scholar 

  3. Elices, M.J., Ciba Found. Symp., 189(1995) 79–85.

    Google Scholar 

  4. Abraham, W.M., et al., J. Clin. Invest, 93(1994) 776–787.

    Google Scholar 

  5. Sagara, H., et al., Int. Arch. Allergy Immunol., 112(1997) 287-294.

    Google Scholar 

  6. Podolsky, D.K., et al., J. Clin. Invest., 92(1993) 372–380.

    Google Scholar 

  7. Burkly, L.C., A. Jakubowski, and M. Hattori. Diabetes, 43(1994) 529–534.

    Google Scholar 

  8. Yang, X.D., et al., Proc. Natl. Acad. Sci., 90(1993) 10494–10498.

    Google Scholar 

  9. Chabot, S., G. Williams, and V.W. Yong, J. Clin. Invest, 100(1997) 604–612.

    Google Scholar 

  10. Seiffge, J. Rheumatol., 23(1997) 287–294.

    Google Scholar 

  11. Komoriya, A., et al., J. Biol. Chem., 266(1991) 15075–15079.

    Google Scholar 

  12. Humphries, M.J., et al., 189(1995) 177–191.

  13. Makarem, R., et al., J. Biol. Chem., 269(1994) 4005–4011.

    Google Scholar 

  14. Jones, E.Y., et al., Nature, 373(1995) 539–544.

    Google Scholar 

  15. Wang, J.H., et al., Proc. Natl. Acad. Sci. (USA), 92(1995) 5714–5718.

    Google Scholar 

  16. Ling, L., et al., Biochemistry, 37(1998) 8743–8753.

    Google Scholar 

  17. Kubinyi, H., QSAR and 3D QSAR in drug design. Part 1: methodology. Kluwer, Dordrecht, 2(1997) 457–466.

    Google Scholar 

  18. Cramer, R.D., D.E. Patterson, and J.D. Bunce, J. Am. Chem. Soc., 110(1988) 5959–5967.

    Google Scholar 

  19. Lin, K.C., et al., J. Med. Chem., 42(1999) 920–934.

    Google Scholar 

  20. Cho, S.J. and A. Tropsha, J. Med. Chem., 38(1995) 1060–1066.

    Google Scholar 

  21. Lobb, R.R., et al., Cell Adhes. Commun., 3(1995) 385–397.

    Google Scholar 

  22. Cambridge Crystallographic Database: 12 Union Road, Cambridge CB2 1EZ, UK.

  23. MOPAC, Quantum Chemistry Program Exchange: Creative Arts Building 181, Indiana University, Bloomington, Indiana 47405, USA.

  24. Agarwal, A., et al., J. Med. Chem., 36(1993) 4006–4014.

    Google Scholar 

  25. Renz, M.E., et al., J. Cell. Biol., 125(1994) 1395–1406.

    Google Scholar 

  26. Ku, J. Am. Chem. Soc., 2(1993) 897.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Singh, J., Vlijmen, H.v., Lee, WC. et al. 3D QSAR (COMFA) of a series of potent and highly selective VLA-4 antagonists. J Comput Aided Mol Des 16, 201–211 (2002). https://doi.org/10.1023/A:1020130418084

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1020130418084

Navigation