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PROSHIFT: Protein chemical shift prediction using artificial neural networks

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Abstract

The importance of protein chemical shift values for the determination of three-dimensional protein structure has increased in recent years because of the large databases of protein structures with assigned chemical shift data. These databases have allowed the investigation of the quantitative relationship between chemical shift values obtained by liquid state NMR spectroscopy and the three-dimensional structure of proteins. A neural network was trained to predict the 1H, 13C, and 15N of proteins using their three-dimensional structure as well as experimental conditions as input parameters. It achieves root mean square deviations of 0.3 ppm for hydrogen, 1.3 ppm for carbon, and 2.6 ppm for nitrogen chemical shifts. The model reflects important influences of the covalent structure as well as of the conformation not only for backbone atoms (as, e.g., the chemical shift index) but also for side-chain nuclei. For protein models with a RMSD smaller than 5 Å a correlation of the RMSD and the r.m.s. deviation between the predicted and the experimental chemical shift is obtained. Thus the method has the potential to not only support the assignment process of proteins but also help with the validation and the refinement of three-dimensional structural proposals. It is freely available for academic users at the PROSHIFT server: www.jens-meiler.de/proshift.html

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References

  • Bonneau, R., Tsai, J., Ruczinski, I., Chivian, D., Rohl, C., Strauss, C. E. M. and Baker, D. (2001) Proteins, 45(Suppl.), 119-126.

    Google Scholar 

  • Braun, D., Wider, G. and Wuethrich, K. (1994) J. Am. Chem. Soc., 116, 8466-8469.

    Google Scholar 

  • Chandonia, J.-M. and Karplus, M. (1999) Proteins Struct. Funct. Genet., 35, 293-306.

    Google Scholar 

  • Choy, W.Y., Sanctuary, B.C. and Zhu, G. (1997) J. Chem. Inf. Comput. Sci., 37, 1086-1094.

    Google Scholar 

  • Clouser, D.L. and Jurs, P.C. (1996) Anal. Chim. Acta, 321, 127-135.

    Google Scholar 

  • Cornilescu, G., Delaglio, F. and Bax, A. (1999) J. Biomol. NMR, 13, 289-302.

    Google Scholar 

  • Gronwald, W., Boyko, R.F., Sonnichsen, F.D., Wishart, D.S. and Sykes, B.D. (1997) J. Biomol. NMR, 10, 165-179.

    Google Scholar 

  • Ivanciuc, O., Rabine, J.P., Cabrol-Bass, D., Panaye, A. and Doucet, J.-P. (1996) J. Chem. Inf. Comput. Sci., 36, 644-653.

    Google Scholar 

  • Iwadate, M., Asakura, T. and Williamson, M.P. (1999) J. Biomol. NMR, 13, 199-211.

    Google Scholar 

  • Jones, D.T. (1999) J. Mol. Biol., 292, 195-202.

    Google Scholar 

  • Kneller, D.G., Cohen, F.E. and Langridge, R. (1990) J. Mol. Biol., 214, 171-182.

    Google Scholar 

  • Kvasnicka, V., Sklenak, S. and Pospichal, J. (1992) J. Chem. Inf. Comput. Sci., 32, 742-747.

    Google Scholar 

  • Le, H. and Oldfield, E. (1994) J. Biomol. NMR, 4, 341-348.

    Google Scholar 

  • Luman, N.R., King, M.P. and Augspurger, J.D. (2001) J. Comput. Chem., 22, 366-372.

    Google Scholar 

  • Meiler, J. (1996-2002) www.jens-meiler.de

  • Meiler, J. (2002a) www.jens-meiler.de/jufo.html

  • Meiler, J. (2002b) www.jens-meiler.de/proshift.html

  • Meiler, J. and Will, M. (2001) J. Chem. Inf. Comput. Sci., 41, 1535-1546.

    Google Scholar 

  • Meiler, J., Maier, W., Will, M. and Meusinger, R. (2002) J. Magn. Reson., 157, 242-252.

    Google Scholar 

  • Meiler, J., Müller, M., Zeidler, A. and Schmäschke, F. (2001) J. Mol. Model., 7, 360-369.

    Google Scholar 

  • Meiler, J., Will, M. and Meusinger, R. (2000) J. Chem. Inf. Comput. Sci., 40, 1169-1176.

    Google Scholar 

  • Meusinger, R. and Moros, R. (1995) In Software - Entwicklung in der Chemie, Vol. 10, Gasteiger, J., Ed. Gesellschaft Deutscher Chemiker, Frankfurt am Main, pp. 209-216.

    Google Scholar 

  • Oldfield, E. (1995) J. Biomol. NMR, 5, 217-225.

    Google Scholar 

  • Osapay, K. and Case, D.A. (1991) J. Am. Chem. Soc., 113, 9436-9444.

    Google Scholar 

  • 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 

  • Petersen, T.N., Lundegaard, C., Nielsen, M., Bohr, H., Bohr, J., Brunak, S., Gippert, G.P. and Lund, O. (2000) Proteins Struct. Funct. Genet., 41, 17-20.

    Google Scholar 

  • Pons, J.L. and Delsuc, M.A. (1999) J. Biomol. NMR, 15, 15-26.

    Google Scholar 

  • Qian, N. and Sejnowski, T.J. (1988) J. Mol. Biol., 202, 865-884.

    Google Scholar 

  • Ramirez, B.E., Voloshin, O.N., Camerini-Otero, R.D. and Bax, A. (2000) Protein Sci., 9, 2161.

    Google Scholar 

  • Robien, W. (1998) Nachr. Chem. Tech. Lab., 46, 74-77.

    Google Scholar 

  • Rohl, C. and Baker, D. (2002) J. Am. Chem. Soc., 124, 2723-2729.

    Google Scholar 

  • Rost, B. (1996) Meth. Enzymol., 266, 525-539.

    Google Scholar 

  • Rost, B. and Sander, C. (1993) J. Mol. Biol., 232, 584-599.

    Google Scholar 

  • Rost, B., Sander, C. and Schneider, R. (1994) J. Mol. Biol., 235, 13-26.

    Google Scholar 

  • Salamov, A.A. and Solovyev, V.V. (1997) J. Mol. Biol., 268, 31-36.

    Google Scholar 

  • Spera, S. and Bax, A. (1991) J. Am. Chem. Soc., 113, 5490-5492.

    Google Scholar 

  • Stolorz, P., Lapedes, A. and Xia, Y. (1992) J. Mol. Biol., 225, 363-377.

    Google Scholar 

  • Thomas, S. and Kleinpeter, E. (1995) J. Prakt. Chem./Chem.-Ztg., 337, 504-507.

    Google Scholar 

  • Wang, Y. and Jardetzky, O. (2002) Protein Sci., 11, 852-861.

    Google Scholar 

  • Wishart, D.S. and Sykes, B.D. (1994) J. Biomol. NMR, 4, 171-180.

    Google Scholar 

  • Wishart, D.S., Bigam, C.G., Holm, A., Hodges, R.S. and Sykes, B.D. (1995) J. Biomol. NMR, 5, 67-81.

    Google Scholar 

  • Wishart, D.S., Sykes, B.D. and Richards, F.M. (1992) Biochemistry, 31, 1647-1651.

    Google Scholar 

  • Wishart, D.S., Watson, M.S., Boyko, R.F. and Sykes, B.D. (1997) J. Biomol. NMR, 10, 329-336.

    Google Scholar 

  • Wüthrich, K. (1986) NMR of Proteins and Nucleic Acids (1HNMR Shifts of Amino Acids), John Wiley & Sons, New York, Chichester, Brisbane, Toronto, Singapore.

    Google Scholar 

  • Xu, X.-P. and Case, D.A. (2001) J. Biomol. NMR, 21, 321-333.

    Google Scholar 

  • Zupan, J. and Gasteiger, J. (1993) Neural Networks for Chemists, VCH Verlagsgesellschaft mbH, Weinheim.

    Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Biochemistry, University of Washington, Box 357350, Seattle, Washington, 98195-7350, U.S.A.

    Jens Meiler

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  1. Jens Meiler
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Meiler, J. PROSHIFT: Protein chemical shift prediction using artificial neural networks. J Biomol NMR 26, 25–37 (2003). https://doi.org/10.1023/A:1023060720156

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