Journal of Biomolecular NMR

, Volume 35, Issue 3, pp 187–208 | Cite as

Inferential backbone assignment for sparse data

  • Olga Vitek
  • Chris Bailey-Kellogg
  • Bruce Craig
  • Jan Vitek
Article

Abstract

This paper develops an approach to protein backbone NMR assignment that effectively assigns large proteins while using limited sets of triple-resonance experiments. Our approach handles proteins with large fractions of missing data and many ambiguous pairs of pseudoresidues, and provides a statistical assessment of confidence in global and position-specific assignments. The approach is tested on an extensive set of experimental and synthetic data of up to 723 residues, with match tolerances of up to 0.5 ppm for \(\hbox{C}^{\upalpha}\) and \(\hbox{C}^{\upbeta}\) resonance types. The tests show that the approach is particularly helpful when data contain experimental noise and require large match tolerances. The keys to the approach are an empirical Bayesian probability model that rigorously accounts for uncertainty in the data at all stages in the analysis, and a hybrid stochastic tree-based search algorithm that effectively explores the large space of possible assignments.

Keywords

Bayesian modeling NMR assignment sparse data statistical inference stochastic search algorithm structural genomics 

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Notes

Acknowledgments

The authors would like to thanks Dr. Post, Purdue University, for providing the experimental data sets, Drs. Jung and Zweckstetter, Max Plank Institute for Biophysical Chemistry, for sharing their simulated data, and Drs. Moseley and Montelione, Rutgers University, for providing access to the AutoAssign data. This work was supported in part by a Purdue Dissertation Fellowship and by NSF grants EIA-9802068 and IIS-0502801.

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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Olga Vitek
    • 1
  • Chris Bailey-Kellogg
    • 2
  • Bruce Craig
    • 3
  • Jan Vitek
    • 4
  1. 1.Institute for Systems BiologySeattleUSA
  2. 2.Department of Computer ScienceDartmouth CollegeHanoverUSA
  3. 3.Department of StatisticsPurdue UniversityWest LafayetteUSA
  4. 4.Department of Computer SciencesPurdue UniversityWest LafayetteUSA

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