Journal of Biomolecular NMR

, Volume 23, Issue 4, pp 263–270

Protein sequential resonance assignments by combinatorial enumeration using 13Cα chemical shifts and their (i, i−1) sequential connectivities

Article

Abstract

The need for the structural characterization of proteins on a genomic scale has brought with it demands for new technology to speed the structure determination process. In NMR, one bottleneck is the sequential assignment of backbone resonances. In this paper, we explore the computational complexity of the sequential assignment problem using only 13Cα chemical shift data and Cα (i,i−1) sequential connectivity information, all of which can potentially be obtained from a single three-dimensional NMR spectrum. Although it is generally believed that there is too much ambiguity in such data to provide sufficient information for sequential assignment, we show that a straightforward combinatorial search algorithm can be used to find correct and unambiguous sequential assignments in a reasonable amount of CPU time for small proteins (approximately 80 residues or smaller) when there is little missing data. The deleterious effect of missing or spurious peaks and the dependence on match tolerances is also explored. This simple algorithm could be used as part of a semi-automated, interactive assignment procedure, e.g., to test partial manually determined solutions fo uniqueness and to extend these solutions.

computational complexity high throughput structural genomics tree search triple-resonance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andrec, M., Du, P. and Levy, R.M. (2001) J. Biomol. NMR, 21, 335–347.Google Scholar
  2. Delaglio, F., Kontaxis, G. and Bax, A. (2000) J. Am. Chem. Soc., 122, 2142–2143.Google Scholar
  3. Grzesiek, S. and Bax, A. (1993) J. Biomol. NMR, 3, 185–204.Google Scholar
  4. Kay, L.E., Ikura, M., Tschudin, R. and Bax, A. (1990) J. Magn. Reson., 89, 496–514.Google Scholar
  5. Moseley, H.N.B. and Montelione, G.T. (1999) Curr. Opinion Struct. Biol., 9, 635–642.Google Scholar
  6. Prestegard, J.H., Tolman, J.R., Al-Hashimi, H.M. and Andrec, M. (1999) In Structure Computation and Dynamics in Protein NMR, Krishna, N.R. and Berliner, L.J., Plenum Publishers, New York, pp. 311–355.Google Scholar
  7. Seavey, B.R., Farr, E.A., Westler, W.M. and Markley, J.L. (1991) J. Biomol. NMR, 1, 217–236.Google Scholar
  8. Tian, F., Valafar, H. and Prestegard, J.H. (2001) J. Am. Chem. Soc., 123, 1179–11796.Google Scholar
  9. Wishart, D.S. and Case, D.A. (2001) Meth. Enzymol., 338, 3–34.Google Scholar
  10. Xu, J., Straus, S.K., Sanctuary, B.C. and Trimble, L. (1994) J. Magn. Reson. B, 103, 53–58.Google Scholar
  11. Zimmerman, D.E., Kulikowski, C.A., Huang, Y., Feng, W., Tashiro, M., Shimotakahara, S., Chien, C.-Y., Powers, R. and Montelione, G.T. (1997) J. Mol. Biol., 269, 592–610.Google Scholar
  12. Zweckstetter, M. and Bax, A. (2001) J. Am. Chem. Soc., 123, 9490–9491.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  1. 1.Department of Chemistry and Chemical Biology, RutgersThe State University of New JerseyPiscatawayU.S.A.

Personalised recommendations