Advertisement

Journal of Biomolecular NMR

, Volume 21, Issue 4, pp 321–333 | Cite as

Automated prediction of 15N, 13Cα, 13Cβ and 13C′ chemical shifts in proteins using a density functional database

  • Xiao-Ping Xu
  • David A. Case
Article

Abstract

A database of peptide chemical shifts, computed at the density functional level, has been used to develop an algorithm for prediction of 15N and 13C shifts in proteins from their structure; the method is incorporated into a program called SHIFTS (version 4.0). The database was built from the calculated chemical shift patterns of 1335 peptides whose backbone torsion angles are limited to areas of the Ramachandran map around helical and sheet configurations. For each tripeptide in these regions of regular secondary structure (which constitute about 40% of residues in globular proteins) SHIFTS also consults the database for information about sidechain torsion angle effects for the residue of interest and for the preceding residue, and estimates hydrogen bonding effects through an empirical formula that is also based on density functional calculations on peptides. The program optionally searches for alternate side-chain torsion angles that could significantly improve agreement between calculated and observed shifts. The application of the program on 20 proteins shows good consistency with experimental data, with correlation coefficients of 0.92, 0.98, 0.99 and 0.90 and r.m.s. deviations of 1.94, 0.97, 1.05, and 1.08 ppm for 15N, 13Cα, 13Cβ and 13C′, respectively. Reference shifts fit to protein data are in good agreement with `random-coil' values derived from experimental measurements on peptides. This prediction algorithm should be helpful in NMR assignment, crystal and solution structure comparison, and structure refinement.

Keywords

Density Functional Calculation Calculated Chemical Regular Secondary Structure Peptide Chemical Backbone Torsion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ando, I., Kameda, T., Asakawa, N., Kuroki, S. and Kurosu, H. (1998) J. Mol. Struct., 441, 213-230.Google Scholar
  2. Becke, A.D. (1993) J. Chem. Phys., 98, 5648-5652.Google Scholar
  3. Bernstein, F.C., Koetzle, T.F., Williams, G.J.B., Meyer, F., Bryce, M.D., Rogers, J.R., Kennard, O., Shimanouchi, T. and Tasumi, M. (1977) J. Mol. Biol., 112, 535-542.Google Scholar
  4. Cornilescu, G., Delaglio, F. and Bax, A. (1999) J. Biomol. NMR 13, 289-302.Google Scholar
  5. Cornilescu, G., Marquardt, J.L., Ottiger, M. and Bax, A. (1998) J. Am. Chem. Soc., 120, 6836.Google Scholar
  6. de Dios, A.C. (1996) Prog. NMR Spectrosc., 97, 229-278.Google Scholar
  7. de Dios, A.C., Pearson, J.G. and Oldfield, E. (1993) Science, 260, 1491-1496.Google Scholar
  8. Frisch, M.J., Trucks, G.W., Schlegel, H.B., Gill, P.M.W., Johnson, B.G., Robb, M.A., Cheeseman, J.R., Keith, T., Petersson, G.A., Montgomery, J.A., Raghavachari, K., Al-Laham, M.A., Zakrzewski, V.G., Ortiz, J.V., Foresman, J.B., Cioslowski, J., Stefanov, B.B., Nanayakkara, A., Challacombe, M., Peng, C.Y., Ayala, P.Y., Chen,W., Wong, M.W., Andres, J.L., Replogle, E.S., Gomperts, R., Martin, R.L., Fox, D.J., Binkley, J.S., Defrees, D.J., Baker, J., Stewart, J.P., Head-Gordon, M., Gonzalez, C. and Pople, J.A. (1998) Gaussian 98, Reversion A.6, Gaussian, Inc., Pittsburgh, PA.Google Scholar
  9. Gronwald, W., Boyko, R.F., Sönnichsen, R.D., Wishart, D.S. and Sykes, B.D. (1997) J. Biomol. NMR, 10, 165-179.Google Scholar
  10. Herranz, J., Gonzalez, C., Rico, M., Nieto, J.L., Santoro, J., Jimenez, M.A., Bruix, M., Neita, J.L. and Blanco, F.J. (1992) Magn. Reson. Chem., 30, 1012-1018.Google Scholar
  11. Iwadate, M., Asakura, T. and Williamson, M.P. (1999) J. Biomol. NMR, 13, 199-211.Google Scholar
  12. Lee, C., Yang, W. and Parr, R. (1988) Phys. Rev., B37, 785-789.Google Scholar
  13. Macke, T and Case, D.A. (1998) In Molecular Modeling of Nucleic Acids, N.B. Leontis and J. SantaLucia (Eds.), American Chemical Society, Washington, pp. 379-393.Google Scholar
  14. Miehlich, B., Savin, A., Stoll, H. and Preuss, H. (1989) Chem. Phys. Lett., 157, 200.Google Scholar
  15. Ösapay, K. and Case, D.A. (1991) J. Am. Chem. Soc., 113, 9436-9444.Google Scholar
  16. Ösapay, K. and Case, D.A. (1994) J. Biomol. NMR, 4, 215-230.Google Scholar
  17. Osawa, M., Swindlls, M.B., Tanikawa, J., Tanaka, T., Mase, T., Furuya, T. and Ikura, M. (1998) J. Mol. Biol., 276, 165-176.Google Scholar
  18. Pearson, J.G., Le, H., Sanders, L.K., Godbout, N., Havlin, R.H. and Oldfield, E. (1997) J. Am. Chem. Soc., 119, 11941-11950.Google Scholar
  19. Pople, J.A., Head-Gordon, M., Fox, D. J., Raghavachari, K. and Curtiss, L.A. (1989) J. Chem. Phys., 93, 2537.Google Scholar
  20. Ramage, R., Green, J., Muir, T.W., Ogunjobi, O.M., Love, S. and Shaw, K. (1994) Biochem. J., 299, 151.Google Scholar
  21. Schwarzinger, S., Kroon, G.J.A, Foss, T.R., Chung, J., Wright, P.E. and Dyson, H.J. (2000) J. Biomol. NMR, 18, 43-48.Google Scholar
  22. Seavey, B., Farr, E.A., Westler, W.M. and Markley, J.A. (1991) J.Biomol. NMR, 1, 217-236.Google Scholar
  23. Sitkoff, D. and Case, D.A. (1997) J. Am. Chem. Soc., 119, 12262-12273.Google Scholar
  24. Spera, S. and Bax, A. (1991) J. Am. Chem. Soc., 113, 5490-5492.Google Scholar
  25. Szilágyi, L. (1995) Prog. NMR Spectrosc., 27, 325-443.Google Scholar
  26. Szilágyi, L. and Jardetzky, O. (1989) J. Magn. Reson., 83, 441.Google Scholar
  27. Vijay-Kumar, S., Bugg, C.E. and Cook, W.J. (1987) J. Mol. Biol., 194, 531.Google Scholar
  28. Williamson, M.P. and Asakura, T. (1993) J. Magn. Reson., B101, 63-71.Google Scholar
  29. Williamson, M.P., Asakura, T., Nakamura, E. and Demura, M. (1992) J. Biomol. NMR, 2, 83-98.Google Scholar
  30. Wishart, D.S., Sykes, B.D. and Richards, F.M. (1991) J. Mol. Biol., 222, 311.Google Scholar
  31. Wishart, D.S., Watson, M.S., Boyko, R.F. and Sykes, B.D. (1997) J. Biomol. NMR, 10, 329-336.Google Scholar
  32. Wolinski, K., Hilton, J.F. and Pulay, P. (1990) J. Am. Chem. Soc., 112, 8251.Google Scholar
  33. Xu, X.-P and Case, D.A. (2001) submitted.Google Scholar
  34. Yamazaki, T., Hinck, A.P., Wang, Y.-X., Nicholson, L.K., Torchia, D.A., Wingfield, P.T., Stahl, S.J., Kaufman, J.D., Chang, C.-H., Domaille, P.J. and Lam, P.Y.S. (1996) Prot. Sci., 5, 495-506.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Xiao-Ping Xu
    • 1
  • David A. Case
    • 1
  1. 1.Department of Molecular BiologyThe Scripps Research InstituteLa JollaU.S.A

Personalised recommendations