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Molecular modelling prediction of ligand binding site flexibility

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Abstract

We have investigated the efficacy of generating multiple sidechain conformations using a rotamer library in order to find the experimentally observed ligand binding site conformation of a protein in the presence of a bound ligand. We made use of a recently published algorithm that performs an exhaustive conformational search using a rotamer library to enumerate all possible sidechain conformations in a binding site. This approach was applied to a dataset of proteins whose structures were determined by X-ray and NMR methods. All chosen proteins had two or more structures, generally involving different bound ligands. By taking one of these structures as a reference, we were able in most cases to successfully reproduce the experimentally determined conformations of the other structures, as well as to suggest alternative low-energy conformations of the binding site. In those few cases where this procedure failed, we observed that the bound ligand had induced a high-energy conformation of the binding site. These results suggest that for most proteins that exhibit limited backbone motion, ligands tend to bind to low energy conformations of their binding sites. Our results also reveal that it is possible in most cases to use a rotamer search-based approach to predict alternative low-energy protein binding site conformations that can be used by different ligands. This opens the possibility of incorporating alternative binding site conformations to improve the efficacy of docking and structure-based drug design algorithms.

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References

  1. Zheng, Q. and Kyle, D.J., Drug Discov. Today, 2 (1997) 229.

    Google Scholar 

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

    Google Scholar 

  3. Koshland, D.E., Proc. Natl. Acad. Sci. USA, 44 (1958) 98.

    Google Scholar 

  4. Najmanovich, R., Kuttner, J., Sobolev, V. and Edelman, M., Proteins Struct. Funct. Genet., 39 (2000) 261.

    Google Scholar 

  5. Frimurer, T.M., Peters, G.H., Iversen, L.F., Andersen, H.S., Moller, N.P. and Olsen, O.H., Biophys. J., 84 (2003) 2273.

    Google Scholar 

  6. Lewi, P.J., Jonge, M., Daeyaert, F., Koymans, L., Vinkers, M., Heeres, J., Janssen, A.J., Arnold, E., Das, K., Clark, A.D. Jr, Hughs, S.H., Boyer, P.L., Bethune, M.P., Pauwels, R., Andries, K., Kukla, M., Ludovici, D., Corte, B.E., Kavash, R. and Ho, C., J. Comput.-Aided Mol. Design, 17 (2003) 129.

    Google Scholar 

  7. Uytterhoeven, K., Sponer, J. and Van Meervelt, L., Eur. J. Biochem., 269 (2002) 2868.

    Google Scholar 

  8. Leach, A.R., J. Mol. Biol., 235 (1994) 345.

    Google Scholar 

  9. Anderson, A.C., O'Niel, R.H., Surti, T.S. and Stroud, R.M., Chem. Biol., 8 (2001) 445.

    Google Scholar 

  10. Schnecke, V., Swanson, C.A., Getzoff, E.D., Tainer, J.A. and Kuhn, L.A., Proteins Struct. Funct. Genet., 33 (1998) 74.

    Google Scholar 

  11. Knegtel, R.M.A., Kuntz, I.D. and Oshiro, C.M., J. Mol. Biol., 266 (1997) 424.

    Google Scholar 

  12. John, B. and Sali, A., Nucleic Acids Res., 31 (2003) 3982.

    Google Scholar 

  13. Chung, S.Y. and Subbiah, S., In Hunter, L. and Klein, T.E. (Eds.), Pac. Symp. Biocomput., World Scientific, Hawaii, 1996, pp. 126-141.

    Google Scholar 

  14. Philippopoulos, M. and Lim, C., Proteins Struct. Funct. Genet., 36 (1999) 87.

    Google Scholar 

  15. Clarage, J.B., Romo, T., Andrews, B.K., Pettitt, B.M. and Phillips, G.N. Jr., Proc. Natl. Acad. Sci. USA, 92 (1995) 3288.

    Google Scholar 

  16. Chandrasekaran, R. and Ramachandran, G.N., Int. J. Protein Res., 2 (1970) 223.

    Google Scholar 

  17. Janin, J., Wodak, S., Levitt, M. and Maigret, B., J. Mol. Biol., 125 (1978) 357.

    Google Scholar 

  18. Bhat, T.N., Sasisekharan, V. and Vijayan, M., Int. J. Pept. Protein Res., 13 (1979) 170.

    Google Scholar 

  19. Benedetti, E., Morelli, G., Nemethy, G. and Scheraga, H.A., Int. J. Pept. Protein Res., 22 (1983) 1.

    Google Scholar 

  20. James, M.N. and Sielecki, A.R., J. Mol. Biol., 15 (1983) 299.

    Google Scholar 

  21. Ponder, J.W. and Richards, F.M., J. Mol. Biol., 193 (1987) 775.

    Google Scholar 

  22. Gelin, B.R. and Karplus, M., Biochemistry, 18 (1979) 1256.

    Google Scholar 

  23. Dunbrack, R.L. and Karplus, M., J. Mol. Biol., 230 (1993) 543.

    Google Scholar 

  24. Dunbrack, R.L. and Cohen, F.E., Protein Sci., 6 (1997) 1661.

    Google Scholar 

  25. McGregor, M.J., Islam, S.A. and Sternberg, M.J., J. Mol. Biol., 198 (1987) 295.

    Google Scholar 

  26. Schrauber, H., Eisenhaber, F. and Argos, P., J. Mol. Biol., 230 (1993) 592.

    Google Scholar 

  27. Tuffery, P., Vaney, M.C., Mornon, J.P. and Hazout, S., J. Mol. Graph., 9 (1991) 175.

    Google Scholar 

  28. Sali, A. and Blundell, T.L., J. Mol. Biol., 234 (1993) 779.

    Google Scholar 

  29. Laughton, C.A., J. Mol. Biol., 235 (1994) 1088.

    Google Scholar 

  30. Koehl, P. and Delarue, M., J. Mol. Biol., 239 (1994) 249.

    Google Scholar 

  31. Bower, M.J., Cohen, F.E. and Dunbrack, R.L., J. Mol. Biol., 267 (1997) 1268.

    Google Scholar 

  32. Källblad, P. and Dean, P.M., J. Mol. Biol., 326 (2003) 1651.

    Google Scholar 

  33. 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.

    Google Scholar 

  34. Murzin, A.G., Brenner, S.E., Hubbard, T. and Chothia, C., J. Mol. Biol., 247 (1995) 536.

    Google Scholar 

  35. Kelley, L.A., Gardner, S.P. and Sutcliffe, M.J., Protein Eng., 9 (1996) 1063.

    Google Scholar 

  36. Leach, A.R. Molecular Modelling-Principle and Applica-tions, 2nd edition. Longman, Singapore, 1996.

  37. Sutcliffe, M.J., Protein Sci., 2 (1993) 936.

    Google Scholar 

  38. Dunbrack, R.L. and Karplus, M., Nature Struct. Biol., 1 (1997) 334.

    Google Scholar 

  39. Accelrys Inc., San Diego, CA, USA.

  40. Maple. J., Dinur, U. and Hagler, A.T., Proc. Natl. Acad. Sci. USA, 85 (1988) 5350.

    Google Scholar 

  41. Dunbrack, R.L., Curr. Opin. Struct. Biol., 12 (2002) 431.

    Google Scholar 

  42. West, N.J. and Smith, L.J., J. Mol. Biol., 280 (1998) 867.

    Google Scholar 

  43. Marti, D.N., Schaller, J. and Llinas, M., Biochemistry, 38 (1999) 15741.

    Google Scholar 

  44. Oh, B.H., Ames, G.F. and Kim, S.H., J. Biol. Chem., 25 (1994) 26323.

    Google Scholar 

  45. Källblad, P., Todorov, N.P., Willems, H.M.G. and Alberts, I.L., J. Med. Chem., 47 (2004) 2761.

    Google Scholar 

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Yi-Ching Yang, A., Källblad, P. & Mancera, R.L. Molecular modelling prediction of ligand binding site flexibility. J Comput Aided Mol Des 18, 235–250 (2004). https://doi.org/10.1023/B:JCAM.0000046820.08222.83

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