Journal of Computer-Aided Molecular Design

, Volume 14, Issue 6, pp 573–591 | Cite as

A novel method of aligning molecules by local surface shape similarity

  • D.A. Cosgrove
  • D.M. Bayada
  • A.P. Johnson
Article

Abstract

A novel shape-based method has been developed for overlaying a series of molecule surfaces into a common reference frame. The surfaces are represented by a set of circular patches of approximately constant curvature. Two molecules are overlaid using a clique-detection algorithm to find a set of patches in the two surfaces that correspond, and overlaying the molecules so that the similar patches on the two surfaces are coincident. The method is thus able to detect areas of local, rather than global, similarity. A consensus overlay for a group of molecules is performed by examining the scores of all pairwise overlays and performing a set of overlays with the highest scores. The utility of the method has been examined by comparing the overlaid and experimental configurations of 4 sets of molecules for which there are X-ray crystal structures of the molecules bound to a protein active site. Results for the overlays are generally encouraging. Of particular note is the correct prediction of the `reverse orientation' for ligands binding to human rhinovirus coat protein HRV14.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Good, A.C. and Richards, W.G., Perspect. Drug Discov. Design, 9–11 (1998) 321.Google Scholar
  2. 2.
    Bohacek, R.S. and Guida, W.C., J. Mol. Graph., 7 (1989) 113.Google Scholar
  3. 3.
    Stouch, T.R. and Jurs, P.C., J. Chem. Inf. Comput. Sci., 26 (1986) 4.Google Scholar
  4. 4.
    Marsili, M., Floersheim, P. and Dreiding, A.S., Comput. Chem., 7 (1983) 175.Google Scholar
  5. 5.
    Masek, B.B., Merchant, A. and Matthew, J.B., J. Med. Chem., 36 (1993) 1230.Google Scholar
  6. 6.
    Good, A.C., Hodgkin, E.E. and Richards, W.G., J. Chem. Inf. Comput. Sci., 32 (1992) 188.Google Scholar
  7. 7.
    Lemmen, C., Hiller, C. and Lengauer, T., J. Comput.-Aided Mol. Design, 12 (1998) 491.Google Scholar
  8. 8.
    Kearsley, S.K. and Smith, G.M., Tetrahedron Comput. Methodol., 3 (1990) 615.Google Scholar
  9. 9.
    Grant, J.A., Gallardo, M.A. and Pickup, B.T., J. Comput. Chem., 17 (1996) 1653.Google Scholar
  10. 10.
    Mestres, J., Rohrer, D.C. and Maggiora, G.M., J. Comput. Chem., 18 (1997) 934.Google Scholar
  11. 11.
    Carbo, R., Leyda, L. and Arnau, M., Int. J. Quantum Chem., 17 (1980) 1185.Google Scholar
  12. 12.
    Nissink, J.W.M., Verdonk, M.L., Kroon, J., Mietzner, T. and Klebe, G., J. Comput. Chem., 18 (1997) 638.Google Scholar
  13. 13.
    Klebe, G., In Kubinyi, H. (Ed.), 3D QSAR in Drug Design: Theory, Methods and Applications, ESCOM, Leiden, 1993, pp. 173–199.Google Scholar
  14. 14.
    Bode, W., Wei, A., Huber, R., Meyer, E., Travis, J. and Neumann, S., EMBO J., 5 (1986) 2453.Google Scholar
  15. 15.
    Takahashi, L.H., Radhakrishnan, R. and Rosenfield Jr., R.E., J. Am. Chem. Soc., 111 (1989) 3368.Google Scholar
  16. 16.
    Hermann, R.B. and Herron, D.K., J. Comput.-Aided Mol. Design, 5 (1991) 511.Google Scholar
  17. 17.
    Masek, B.B., Merchant, A. and Matthew, J.B., Proteins, 17 (1993) 193.Google Scholar
  18. 18.
    Perkins, T.D.J., Mills, J.E.J. and Dean, P.M., J. Comput.-Aided Mol. Design, 9 (1995) 479.Google Scholar
  19. 19.
    Grant, J.A. and Pickup, B.T., In van Gunsteren, W.G., Weiner, P.K. and Wilkinson, A.J. (Eds), Computer Simulation of Biomolecular Systems. Theoretical and Experimental Applications, Vol. 3, Ch. 5, Kluwer/ESCOM, Dordrecht, 1997, pp. 150–176.Google Scholar
  20. 20.
    Dean, P.M. and Chau, P.-L., J. Mol. Graph., 5 (1987) 97.Google Scholar
  21. 21.
    Dean, P.M. and Chau, P.-L., J. Mol. Graph., 5 (1987) 153.Google Scholar
  22. 22.
    Dean, P.M. and Callow, P., J. Mol. Graph., 5 (1987) 159.Google Scholar
  23. 23.
    Poirrette, A.R., Artymiuk, P.J., Rice, D.W. and Willett, P., J. Comput.-Aided Mol. Design, 11 (1997) 557.Google Scholar
  24. 24.
    Rosen, M., Lin, S.L., Wolfson, H.J. and Nussinov, R., Protein Eng., 11 (1998) 263.Google Scholar
  25. 25.
    Norel, R., Petrey, D., Wolfson, H.J. and Nussinov, R., Proteins, 36 (1999) 307.Google Scholar
  26. 26.
    Walker, P.D., Ateca, G.A. and Mezey, P.G., J. Comput. Chem., 12 (1991) 220.Google Scholar
  27. 27.
    Lee, B. and Richards, F.M., J. Mol. Biol., 55 (1971) 379.Google Scholar
  28. 28.
    McHugh, J., Algorithmic Graph Theory, Prentice-Hall International Editions, New York, NY, 1990, pp. 90–114.Google Scholar
  29. 29.
    Kerbosch, J. and Bron, C., Commun. ACM, 16 (1973) 575.Google Scholar
  30. 30.
    Brint, A.T. and Willett, P., J. Chem. Inf. Comput. Sci., 27 (1987) 152.Google Scholar
  31. 31.
    Diamond, R., Acta. Crystallogr., A44 (1988) 211.Google Scholar
  32. 32.
    Cramer III, R.D., Patterson, D.E. and Bunce, J.D., J. Am. Chem. Soc., 110 (1993) 5959.Google Scholar
  33. 33.
    Cramer III, R.D., DePriest, S.A., Patterson, D.E. and Hecht, P., In Kubinyi, H. (Ed.), 3D QSAR in Drug Design. Theory, Methods and Applications, ESCOM, Leiden, 1993, pp. 443–z485.Google Scholar
  34. 34.
    Kearsley, S.K., J. Comput. Chem., 11 (1990) 1187.Google Scholar
  35. 35.
    Diamond, R., Protein Sci., 1 (1992) 1279.Google Scholar
  36. 36.
    Mestres, J., Maggiora, G.M. and Rohrer, D.C., J. Mol. Graph., 15 (1997) 114.Google Scholar
  37. 37.
    Purisima, E.O. and Chan, S.L., Comput. Graph., 22 (1998) 83.Google Scholar
  38. 38.
    Masek, B.B., Merchant, A. and Matthew, J.B., J. Med. Chem., 36 (1993) 1230.Google Scholar
  39. 39.
    Böhm, H.-J. and Klebe, G., Angew. Chem. Int. Ed. Engl., 35 (1996) 2588.Google Scholar
  40. 40.
    Jewbury, P.J., Private communication.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • D.A. Cosgrove
    • 1
  • D.M. Bayada
    • 2
  • A.P. Johnson
    • 2
  1. 1.AstraZenecaCheshireU.K.
  2. 2.School of ChemistryUniversity of LeedsLeedsU.K.

Personalised recommendations