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Journal of Biomolecular NMR

, Volume 44, Issue 4, pp 207–211 | Cite as

CheckShift improved: fast chemical shift reference correction with high accuracy

  • Simon W. Ginzinger
  • Marko Skočibušić
  • Volker Heun
Article

Abstract

The construction of a consistent protein chemical shift database is an important step toward making more extensive use of this data in structural studies. Unfortunately, progress in this direction has been hampered by the quality of the available data, particularly with respect to chemical shift referencing, which is often either inaccurate or inconsistently annotated. Preprocessing of the data is therefore required to detect and correct referencing errors. In an earlier study we developed CheckShift, a program for performing this task automatically. Now we spent substantial effort in improving the running time of the CheckShift algorithm, which resulted in an running time decrease of 90%, thereby achieving equivalent quality to the former version of CheckShift. The reason for the running time decrease is twofold. Firstly we improved the search for the optimal re-referencing offset considerably. Secondly, as CheckShift is based on a secondary structure prediction from the amino acid sequence (formally PsiPred was used), we evaluated a wide range of available secondary structure prediction programs focusing on the special needs of the CheckShift algorithm. The results of this evaluation prove empirically that we can use faster secondary structure prediction programs than PsiPred without sacrificing CheckShift’s accuracy. Very recently Wang and Markley (2009) gave a small list of extreme outliers of the former version of the CheckShift web-server. Those were due to the empirical reduction of the search space implemented in the old version. The new version of CheckShift now gives very similar results to RefDB and LACS for all outliers mentioned in Table 1 of Wang and Markley (2009).

Keywords

Chemical shifts Re-referencing CheckShift 

Notes

Acknowledgments

We thank Murray Coles, MPI for Developmental Biology, Tübingen, for providing, the experimental data used in our evaluations. Thanks also go to Christian Weichenberger from the research group of Manfred Sippl, University of Salzburg, who compiled the list for our smaller secondary structure content prediction benchmark.

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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Simon W. Ginzinger
    • 2
  • Marko Skočibušić
    • 1
  • Volker Heun
    • 1
  1. 1.Institut für InformatikLudwig-Maximilians-Universität MünchenMünchenDeutschland
  2. 2.Department of Molecular Biology Division of Bioinformatics, Center of Applied Molecular EngineeringUniversity of SalzburgSalzburgÖsterreich

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