Advertisement

Journal of Computer-Aided Molecular Design

, Volume 16, Issue 1, pp 11–26 | Cite as

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

  • Renxiao Wang
  • Luhua Lai
  • Shaomeng Wang
Article

Abstract

New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes, X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.

binding affinity prediction consensus scoring empirical scoring molecular docking structure-based drug design 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kuntz, I.D., Science, 257 (1992) 1078.Google Scholar
  2. 2.
    Greer, J., Erickson, J.W., Baldwin, J.J. and Varney, M.D., J. Med. Chem., 37 (1994) 1035.Google Scholar
  3. 3.
    Verlinde C.L.M.J. and Hol W.G.J., Structure, 2 (1994) 577.Google Scholar
  4. 4.
    Babine, R.E. and Bender, S.L., Chem. Rev., 97 (1997) 1359.Google Scholar
  5. 5.
    Gane, P.J. and Dean, P.M., Curr. Opin. Struct. Biol., 10 (2000) 401.Google Scholar
  6. 6.
    Walters, W.P., Stahl, M.T. and Murcko, M.A., Drug Discovery Today, 3 (1998) 160.Google Scholar
  7. 7.
    Makino, S. and Kuntz, I.D., J. Comp. Chem., 18 (1997) 1812.Google Scholar
  8. 8.
    Morris, G.M., Goodsell, D.S., Halliday, R., Huey, R., Hart, W.E., Belew, R.K. and Olson, A.J., J. Comput. Chem., 19 (1998) 1639.Google Scholar
  9. 9.
    Jones, G., Wilett, P., Glen, R.C., Leach, A.R. and Taylor, R., J. Mol. Biol., 267 (1997) 727.Google Scholar
  10. 10.
    Rarey, M., Kramer, B., Lengauer, T. and Klebe, G., J. Mol. Biol., 261 (1996) 470.Google Scholar
  11. 11.
    Böhm, H.J., Curr. Opin. Biotech., 7 (1996) 433.Google Scholar
  12. 12.
    Miranker, A. and Karplus, M., Proteins, 11 (1991) 29.Google Scholar
  13. 13.
    Böhm, H.J., J. Comput. Aid. Mol. Des., 6 (1992) 61.Google Scholar
  14. 14.
    Gillet, V., Johnson, P. and Mata, P., J. Comput. Aid. Mol. Des., 7 (1993) 127.Google Scholar
  15. 15.
    Clark, D.E., Frenkel, D. and Levy, S.A., J. Comput. Aid. Mol. Des., 5 (1995) 13.Google Scholar
  16. 16.
    Pearlman, D.A. and Murcko, M.A., J. Med. Chem., 39 (1996) 1651.Google Scholar
  17. 17.
    Wang, R., Gao, Y., Lai, L., J. Mol. Model., 6(2000) 498-516.Google Scholar
  18. 18.
    Schneider, G., Lee, M.L., Stahl, M. and Schneider, P., J. Comput. Aid. Mol. Des., 14 (2000) 487.Google Scholar
  19. 19.
    Kollman, P.A., Curr. Opin. Struct. Biol., 4 (1994) 240.Google Scholar
  20. 20.
    Ajay and Murcko, M.A., J. Med. Chem., 38 (1995) 4953.Google Scholar
  21. 21.
    Tame, J.R.H., J. Comput. Aid. Mol. Des., 13 (1999) 99.Google Scholar
  22. 22.
    Goodford, P.J.A., J. Med. Chem., 28 (1985) 849.Google Scholar
  23. 23.
    Massova, I. and Kollman, P., Perspect. Drug Disc. Des., 18 (2000) 113.Google Scholar
  24. 24.
    Kollman, P., Chem. Rev., 7 (1993) 2395.Google Scholar
  25. 25.
    Aqvist, J., Medina, C. and Samuelsson, J.E., Protein Eng., 7 (1994) 385.Google Scholar
  26. 26.
    Carlson, H.A. and Jorgensen, W.L., J. Phys. Chem., 99 (1995) 10667.Google Scholar
  27. 27.
    Böhm, H.J., J. Comput. Aid. Mol. Des., 8 (1994) 243.Google Scholar
  28. 28.
    Jain, A.N., J. Comput. Aid. Mol. Des., 10 (1996) 427.Google Scholar
  29. 29.
    Head, R.D., Smythe, M.L., Oprea, T.I., Waller, C.L., Green, S.M. and Marshall, G.R., J. Am. Chem. Soc., 118 (1996) 3959.Google Scholar
  30. 30.
    Eldridge, M.D., Murray, C.W., Auton, T.R., Paolini, G.V. and Mee, R.P., J. Comput. Aid. Mol. Des., 11 (1997) 425.Google Scholar
  31. 31.
    Böhm, H.J., J. Comput. Aid. Mol. Des., 12 (1998) 309.Google Scholar
  32. 32.
    Wang, R., Gao, Y. and Lai, L., J. Mol. Model., 4 (1998) 379.Google Scholar
  33. 33.
    Charifson, P.S., Corkery, J.J., Murcko, M.A. and Walters, W.P., J. Med. Chem. 42 (1999) 5100.Google Scholar
  34. 34.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N. and Bourne, P.E., Nucleic Acids Res., 28 (2000) 235, http://www.rcsb.org/pdb/.Google Scholar
  35. 35.
    SYBYL v6.2, Tripos Inc. St. Louis, MO, U.S.A. http://www.tripos.com/Google Scholar
  36. 36.
    Wang, R., Gao, Y. and Lai, L., Perspect. Drug Disc. Des., 19 (2000) 47.Google Scholar
  37. 37.
    Wang, R. and Wang, S., J. Chem. Inf. Comput. Sci., 41 (2001) 1422.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  1. 1.Medical Chemistry and Comprehensive Cancer CenterUniversity of MichiganAnn ArborU.S.A
  2. 2.Institute of Physical ChemistryPeking UniversityBeijingP.R. China

Personalised recommendations