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A comparative study of ligand-receptor complex binding affinity prediction methods based on glycogen phosphorylase inhibitors

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Abstract

Finding an accurate method for estimating the affinity of protein ligands activity is the most challenging task in computer-aided molecular design. In this study we investigate and compare seven different prediction methods for a set of 30 glycogen phosphorylase (GP) inhibitors with known crystal structures. Five of the methods involve quantitative structure-activity relationships (QSAR) based on the 2D or 3D structures of the GP ligands alone. They are hologram QSAR (HQSAR), receptor surface model (RSM), comparative molecular field analysis (CoMFA), and applications of genetic neural network to similarity matrix (SM/GNN) or conventional descriptors (C2GNN). All five QSAR-based models have good predictivity and yield q2 values ranging from 0.60 to 0.82. The other two methods, LUDI and a structure-based binding energy predictor (SBEP) system, make use of the structures of the ligand-receptor complexes. The weak correlation between biological activities and the LUDI scores of this set of inhibitors suggests that the LUDI scoring function, by itself, may not be a general method for reliable ranking of a congeneric series of compounds. The SBEP system is derived from a set of physical properties that characterizes ligand-receptor interactions. The final neural network model, which yields a q2 value of 0.60, employs four descriptors. A jury method that combines the predictions of the five QSAR-based models leads to an increase in predictivity. A multi-layer scoring system that utilizes all seven prediction methods is proposed for the evaluation of novel GP ligands.

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References

  1. Tokarski, J.S. and Hopfinger, A.J., J. Chem. Inf. Comput. Sci., 37 (1997) 792.

    Google Scholar 

  2. Hansch, C. and Leo, A., Substituent Constants for Correlation Analysis in Chemistry and Biology, John Wiley & Sons, Inc., New York, NY, 1979.

    Google Scholar 

  3. Randic, M., J. Am. Chem. Soc., 97 (1975) 6609.

    Google Scholar 

  4. Hall, L.H. and Kier, L.B., J. Chem. Inf. Comput. Sci., 35 (1995) 1039.

    Google Scholar 

  5. Hurst, T. and Heritage, T., Abstract of Papers of the Am. Chem. Soc., 213 (1997) 19.

    Google Scholar 

  6. Hurst, T., Heritage, T.W. and Clark, R.D., Abstract of Papers of the Am. Chem. Soc., 215 (1998) 38.

    Google Scholar 

  7. Tong, W.D., Perkins, R., Sheehan, D.M., Welsh, W.J., Lowis, D.R. and Goddette, D.W., Abstract of Papers of the Am. Chem. Soc., 214 (1997) 81.

    Google Scholar 

  8. HQSAR, Version 1.0, Tripos, Inc., St. Louis, MO.

  9. Winkler, D.A. and Burden, F.R., Quant. Struct.-Act. Relat., 17 (1998) 224.

    Google Scholar 

  10. Silverman, B.D. and Platt, D.E., J. Med. Chem., 39 (1996) 2129.

    Google Scholar 

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

    Google Scholar 

  12. Cramer III, R.D., Patterson, D.E. and Bunce, J.D., J. Am. Chem. Soc., 110 (1988) 5959.

    Google Scholar 

  13. Good, A.C., So, S.-S. and Richards, W.G., J. Med. Chem., 36 (1993) 433.

    Google Scholar 

  14. Good, A.C., Peterson, S.J. and Richards, W.G., J.Med. Chem., 36 (1993) 2929.

    Google Scholar 

  15. So, S.-S. and Karplus, M., J. Med. Chem., 39 (1996) 1521.

    Google Scholar 

  16. So, S.-S. and Karplus, M., J. Med. Chem., 39 (1996) 5246.

    Google Scholar 

  17. So, S.-S. and Karplus, M., J. Med. Chem., 40 (1997) 4347.

    Google Scholar 

  18. So, S.-S. and Karplus, M., J. Med. Chem., 40 (1997) 4360.

    Google Scholar 

  19. Hahn, M., J. Med. Chem., 38 (1995) 2080.

    Google Scholar 

  20. Hahn, M. and Rogers, D., J. Med. Chem., 38 (1995) 2091.

    Google Scholar 

  21. Böhm, H.-J., J. Comput.-Aided Mol. Design, 6 (1992) 593.

    Google Scholar 

  22. Böhm, H.-J., J. Comput.-Aided Mol. Design, 8 (1994) 243.

    Google Scholar 

  23. DeWitte, R.S. and Shakhnovich, E.I., J. Am. Chem. Soc., 118 (1996) 11733.

    Google Scholar 

  24. DeWitte, R.S., Ishchenko, A.V. and Shakhnovich, E.I., J. Am. Chem. Soc., 119 (1997) 4608.

    Google Scholar 

  25. Welch, W., Ruppert, J. and Jain, A.N., Chem. Biol., 3 (1996) 449.

    Google Scholar 

  26. Holloway, M.K., Wai, J.M., Halgren, T.A., Fitzgerald, P.M., Vacca, J.P., Dorsey, B.D., Levin, R.B., Thompson, W.J., Chen, L.J. and deSolms, S.J., J. Med. Chem., 38 (1995) 305.

    Google Scholar 

  27. Åqvist, J., Medina, C. and Samuelsson, J.E., Protein Eng., 7 (1994) 385.

    Google Scholar 

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

    Google Scholar 

  29. Gilson, M.K., Given, J.A., Bush, B.L. and McCammon, J.A., Biophys. J., 72 (1997) 1047.

    Google Scholar 

  30. Martin, J.L., Johnson, L.N. and Withers, S.G., Biochemistry, 29 (1990) 10745.

    Google Scholar 

  31. Martin, J.L., Veluraja, K., Ross, K., Johnson, L.N., Fleet, G.W., Ramsden, N.G., Bruce, I., Orchard, M.G., Oikonomakos, N.G. and Papageorgiou, A.C., Biochemistry, 30 (1991) 10101.

    Google Scholar 

  32. Johnson, L.N., Snape, P., Martin, J.L., Acharya, K.R., Barford, D. and Oikonomakos, N.G., J. Mol. Biol., 232 (1993) 253.

    Google Scholar 

  33. Oikonomakos, N.G., Kontou, M., Zographos, S.E., Tsitoura, H.S., Johnson, L.N., Watson, K.A., Mitchell, E.P., Fleet, G.W., Son, J.C., Bichard, C.J., Leonidas, D.D. and Acharya, K.R., Eur. J. Drug Metab. Pharmacokinet., 19 (1994) 185.

    Google Scholar 

  34. Watson, K.A., Mitchell, E.P., Johnson, L.N., Son, J.C., Bichard, C.J., Orchard, M.G., Fleet, G.W., Oikonomakos, N.G., Leonidas, D.D. and Kontou, M., Biochemistry, 33 (1994) 5745.

    Google Scholar 

  35. Oikonomakos, N.G., Kontou, M., Zographos, S.E., Watson, K.A., Johnson, L.N., Bichard, C.J., Fleet, G.W. and Acharya, K.R., Protein Sci., 4 (1995) 2469.

    Google Scholar 

  36. Oikonomakos, N.G., Zographos, S.E., Johnson, L.N., Papageorgiou, A.C. and Acharya, K.R., J. Mol. Biol., 254 (1995) 900.

    Google Scholar 

  37. Watson, K.A., Mitchell, E.P., Johnson, L.N., Cruciani, G., Son, J.C., Bichard, C.J.F., Fleet, G.W.J., Oikonomakos, N.G., Kontou, M. and Zographos, S.E., Acta Crystallogr., D51 (1995) 458.

    Google Scholar 

  38. Bichard, C.J.F., Mitchell, E.P., Wormald, M.R., Watson, K.A., Johnson, L.N., Zographos, S.E., Koutra, D.D., Oikonomakos, N.G. and Fleet, G.W.J., Tetrahedron Lett., 36 (1995) 2145.

    Google Scholar 

  39. Krülle, T.M., Watson, K.A., Gregoriou, M., Johnson, L.N., Crook, S., Watkin, D.J., Griffiths, R.C., Nash, R.J., Tsitsanou, K.E., Zographos, S.E., Oikonomakos, N.G. and Fleet, G.W.J., Tetrahedron Lett., 36 (1995) 8281.

    Google Scholar 

  40. Hahn, M., J. Chem. Inf. Comput. Sci., 37 (1997) 80.

    Google Scholar 

  41. Rogers, D.R. and Hopfinger, A.J., J. Chem. Inf. Comput. Sci., 34 (1994) 854.

    Google Scholar 

  42. Dean, P.M., In Kubinyi, H. (Ed.), 3D QSAR in Drug Design: Theory, Methods and Applications, ESCOM, Leiden, The Netherlands, 1993, p. 150.

    Google Scholar 

  43. Richards, W.G., In Pullman, A., Jortner, J. and Pullman, B. (Eds.), Modelling of Biomolecular Structures and Mechanisms, Kluwer Academic Publishers, Dordrecht, the Netherlands, 1995, p. 365.

    Google Scholar 

  44. Carbó, R., Leyda, L. and Arnau, M., Int. J. Quantum Chem., 17 (1980) 1185.

    Google Scholar 

  45. Hodgkin, E.E. and Richards, W.G., Int. J. Quantum Chem., Quantum Biol. Symp., 14 (1987) 105.

    Google Scholar 

  46. Search_Compare, Version 95.0, Molecular Simulations Inc., San Diego, CA.

  47. Maple, J.R., Hwang, M.-J., Stockfisch, T.P., Dinur, U., Waldman, M., Ewig, C.S. and Hagler, A.T., J. Comput. Chem., 15 (1994) 162.

    Google Scholar 

  48. Hwang, M.-J., Stockfisch, T.P. and Hagler, A.T., J. Am. Chem. Soc., 116 (1994) 2515.

    Google Scholar 

  49. Cerius2, Version 3.0, Molecular Simulations Inc., San Diego, CA.

  50. Ligand_Design, Version 95.0, Molecular Simulations Inc., San Diego, CA.

  51. Discover, Version 95.0, Molecular Simulations Inc., San Diego, CA.

  52. Froloff, N., Windemuth, A. and Honig, B., Protein Sci., 6 (1995) 1293.

    Google Scholar 

  53. Pickett, S.D. and Sternberg, M.J.E., J. Mol. Biol., 231 (1993) 825.

    Google Scholar 

  54. Eldridge, M.D., Murray, C.W., Auton, T.R., Paolini, G.V. and Mee, R.P., J. Comput.-Aided Mol. Design, 11 (1997) 425.

    Google Scholar 

  55. So, S.-S. and Richards, W.G., J. Med. Chem., 35 (1992) 3201.

    Google Scholar 

  56. Ajay, Chemometr. Intell. Lab., 24 (1994) 19.

    Google Scholar 

  57. Chandonia, J.-M., Neural network based algorithms for protein structure prediction, Ph.D. Thesis, The Committee on Higher Degrees in Biophysics, Harvard University, 1997.

  58. 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 

  59. Hansson, T., Marelius, J. and Åqvist, J., J. Comput.-Aided Mol. Design, 12 (1998) 27.

    Google Scholar 

  60. Guo, Z. and Brooks, C.L.I., J. Am. Chem. Soc., 120 (1998) 1920.

    Google Scholar 

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  1. Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA, 02138, U.S.A

    Sung-Sau So & Martin Karplus

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  1. Sung-Sau So
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So, SS., Karplus, M. A comparative study of ligand-receptor complex binding affinity prediction methods based on glycogen phosphorylase inhibitors. J Comput Aided Mol Des 13, 243–258 (1999). https://doi.org/10.1023/A:1008073215919

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