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

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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.

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References

  1. Kuntz, I.D., Science, 257 (1992) 1078.

  2. Greer, J., Erickson, J.W., Baldwin, J.J. and Varney, M.D., J. Med. Chem., 37 (1994) 1035.

    Google Scholar 

  3. Verlinde C.L.M.J. and Hol W.G.J., Structure, 2 (1994) 577.

  4. Babine, R.E. and Bender, S.L., Chem. Rev., 97 (1997) 1359.

  5. Gane, P.J. and Dean, P.M., Curr. Opin. Struct. Biol., 10 (2000) 401.

    Google Scholar 

  6. Walters, W.P., Stahl, M.T. and Murcko, M.A., Drug Discovery Today, 3 (1998) 160.

  7. Makino, S. and Kuntz, I.D., J. Comp. Chem., 18 (1997) 1812.

  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. Jones, G., Wilett, P., Glen, R.C., Leach, A.R. and Taylor, R., J. Mol. Biol., 267 (1997) 727.

    Google Scholar 

  10. Rarey, M., Kramer, B., Lengauer, T. and Klebe, G., J. Mol. Biol., 261 (1996) 470.

    Google Scholar 

  11. Böhm, H.J., Curr. Opin. Biotech., 7 (1996) 433.

    Google Scholar 

  12. Miranker, A. and Karplus, M., Proteins, 11 (1991) 29.

  13. Böhm, H.J., J. Comput. Aid. Mol. Des., 6 (1992) 61.

    Google Scholar 

  14. Gillet, V., Johnson, P. and Mata, P., J. Comput. Aid. Mol. Des., 7 (1993) 127.

    Google Scholar 

  15. Clark, D.E., Frenkel, D. and Levy, S.A., J. Comput. Aid. Mol. Des., 5 (1995) 13.

    Google Scholar 

  16. Pearlman, D.A. and Murcko, M.A., J. Med. Chem., 39 (1996) 1651.

    Google Scholar 

  17. Wang, R., Gao, Y., Lai, L., J. Mol. Model., 6(2000) 498-516.

    Google Scholar 

  18. Schneider, G., Lee, M.L., Stahl, M. and Schneider, P., J. Comput. Aid. Mol. Des., 14 (2000) 487.

    Google Scholar 

  19. Kollman, P.A., Curr. Opin. Struct. Biol., 4 (1994) 240.

  20. Ajay and Murcko, M.A., J. Med. Chem., 38 (1995) 4953.

  21. Tame, J.R.H., J. Comput. Aid. Mol. Des., 13 (1999) 99.

    Google Scholar 

  22. Goodford, P.J.A., J. Med. Chem., 28 (1985) 849.

    Google Scholar 

  23. Massova, I. and Kollman, P., Perspect. Drug Disc. Des., 18 (2000) 113.

  24. Kollman, P., Chem. Rev., 7 (1993) 2395.

    Google Scholar 

  25. Aqvist, J., Medina, C. and Samuelsson, J.E., Protein Eng., 7 (1994) 385.

  26. Carlson, H.A. and Jorgensen, W.L., J. Phys. Chem., 99 (1995) 10667.

    Google Scholar 

  27. Böhm, H.J., J. Comput. Aid. Mol. Des., 8 (1994) 243.

    Google Scholar 

  28. Jain, A.N., J. Comput. Aid. Mol. Des., 10 (1996) 427.

    Google Scholar 

  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. 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. Böhm, H.J., J. Comput. Aid. Mol. Des., 12 (1998) 309.

    Google Scholar 

  32. Wang, R., Gao, Y. and Lai, L., J. Mol. Model., 4 (1998) 379.

  33. Charifson, P.S., Corkery, J.J., Murcko, M.A. and Walters, W.P., J. Med. Chem. 42 (1999) 5100.

    Google Scholar 

  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/.

  35. SYBYL v6.2, Tripos Inc. St. Louis, MO, U.S.A. http://www.tripos.com/

  36. Wang, R., Gao, Y. and Lai, L., Perspect. Drug Disc. Des., 19 (2000) 47.

  37. Wang, R. and Wang, S., J. Chem. Inf. Comput. Sci., 41 (2001) 1422.

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Authors and Affiliations

  1. Medical Chemistry and Comprehensive Cancer Center, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-0934, U.S.A

    Renxiao Wang & Shaomeng Wang

  2. Institute of Physical Chemistry, Peking University, Beijing, 100871, P.R. China

    Luhua Lai

Authors
  1. Renxiao Wang
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  2. Luhua Lai
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  3. Shaomeng Wang
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Correspondence to Shaomeng Wang.

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Wang, R., Lai, L. & Wang, S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16, 11–26 (2002). https://doi.org/10.1023/A:1016357811882

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