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


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).


Chemical shifts Re-referencing CheckShift 



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.


  1. Adamczak R, Porollo A, Meller J (2005) Combining prediction of secondary structure and solvent accessibility in proteins. Proteins 59(3):467–475. doi: 10.1002/prot.20441 Google Scholar
  2. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402CrossRefGoogle Scholar
  3. Ammelburg M, Hartmann MD, Djuranovic S, Alva V, Koretke KK, Martin J, Sauer G, Truffault V, Zeth K, Lupas AN, Coles M (2007) A ctp-dependent archaeal riboflavin kinase forms a bridge in the evolution of cradle-loop barrels. Structure 15(12):1577–1590. doi: 10.1016/j.str.2007.09.027 Google Scholar
  4. Berjanskii MV, Neal S, Wishart DS (2006) Preditor: a web server for predicting protein torsion angle restraints. Nucleic Acids Res 34(Web Server issue):W63–W69. doi: 10.1093/nar/gkl341
  5. Coles M, Diercks T, Liermann J, Groger A, Rockel B, Baumeister W, Koretke KK, Lupas A, Peters J, Kessler H (1999) The solution structure of VAT-N reveals a ’missing link’ in the evolution of complex enzymes from a simple βαββ element. Curr Biol 9(20):1158–1168. doi: 10.1016/S0960-9822(00)80017-2 Google Scholar
  6. Coles M, Hulko M, Djuranovic S, Truffault V, Koretke KK, Martin J, Lupas AN (2006) Common evolutionary origin of swapped-hairpin and double-psi beta barrels. Structure 14(10):1489–1498. doi: 10.1016/j.str.2006.08.005 Google Scholar
  7. Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13(3):289–302CrossRefGoogle Scholar
  8. Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins 23(4):566–579. doi: 10.1002/prot.340230412 Google Scholar
  9. Ginzinger SW, Coles M (2009) SimShiftDB; local conformational restraints derived from chemical shift similarity searches on a large synthetic database. J Biomol NMR 43(3):179–185. doi: 10.1007/s10858-009-9301-7 Google Scholar
  10. Ginzinger SW, Fischer J (2006) SimShift: identifying structural similarities from NMR chemical shifts. Bioinformatics 22(4):460–465. doi: 10.1093/bioinformatics/bti805 Google Scholar
  11. Ginzinger SW, Gerick F, Coles M, Heun V (2007a) Checkshift: automatic correction of inconsistenly referenced chemical shift data. J Biomol NMR. doi: 10.1007/s10858-007-9191-5
  12. Ginzinger SW, Gräupl T, Heun V (2007b) SimShiftDB: chemical-shift-based homology modeling. Lecture Notes Comput Sci 4414:357–370CrossRefGoogle Scholar
  13. Haupt M, Bramkamp M, Heller M, Coles M, Deckers-Hebestreit G, Herkenhoff-Hesselmann B, Altendorf K, Kessler H (2006) The holo-form of the nucleotide binding domain of the KdpFABC complex from Escherichia coli reveals a new binding mode. J Biol Chem 281(14):9641–9649. doi: 10.1074/jbc.M508290200 Google Scholar
  14. Heinig M, Frishman D (2004) STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res 32(Web Server issue):W500–W502. doi: 10.1093/nar/gkh429
  15. Heller M, John M, Coles M, Bosch G, Baumeister W, Kessler H (2004) NMR studies on the substrate-binding domains of the thermosome: structural plasticity in the protrusion region. J Mol Biol 336(3):717–729. doi: 10.1016/j.jmb.2003.12.035 Google Scholar
  16. Hulko M, Berndt F, Gruber M, Linder JU, Truffault V, Schultz A, Martin J, Schultz JE, Lupas AN, Coles M (2006) The hamp domain structure implies helix rotation in transmembrane signaling. Cell 126(5):929–940. doi: 10.1016/j.cell.2006.06.058 Google Scholar
  17. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202. doi: 10.1006/jmbi.1999.3091 Google Scholar
  18. Mao Y, Senic-Matuglia F, Di Fiore PP, Polo S, Hodsdon ME, De Camilli P (2005) Deubiquitinating function of ataxin-3: insights from the solution structure of the Josephin domain. Proc Natl Acad Sci USA 102(36):12700–12705. doi: 10.1073/pnas.0506344102 Google Scholar
  19. Neal S, Berjanskii M, Zhang H, Wishart DS (2006) Accurate prediction of protein torsion angles using chemical shifts and sequence homology. Magn Reson Chem 44:S158–S167. doi: 10.1002/mrc.1832 Google Scholar
  20. Nicastro G, Menon RP, Masino L, Knowles PP, McDonald NQ, Pastore A (2005) The solution structure of the josephin domain of ataxin-3: structural determinants for molecular recognition. Proc Natl Acad Sci USA 102(30):10493–10498. doi: 10.1073/pnas.0501732102 Google Scholar
  21. Oldfield E (1995) Chemical shifts and three-dimensional protein structures. J Biomol NMR 5(3):217–225CrossRefGoogle Scholar
  22. Pollastri G, Przybylski D, Rost B, Baldi P (2002) Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47(2):228–235CrossRefGoogle Scholar
  23. Rost B, Yachdav G, Liu J (2004) The predictprotein server. Nucleic Acids Res 32(Web Server issue):W321–W326. doi: 10.1093/nar/gkh377
  24. Schwieters CD, Kuszewski JJ, Tjandra N, Marius Clore G (2003) The xplor-nih nmr molecular structure determination package. J Magn Reson 160(1):65–73CrossRefADSGoogle Scholar
  25. Seavey BR, Farr EA, Westler WM, Markley JL (1991) A relational database for sequence-specific protein NMR data. J Biomol NMR 1:217–236CrossRefGoogle Scholar
  26. Skočibušić M (2008) Improving the CheckShift algorithm. Diploma thesis, Lugwig-Maximilians Universität MünchenGoogle Scholar
  27. Wang L, Eghbalnia HR, Bahrami A, Markley JL (2005) Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications. J Biomol NMR 32(1):13–22. doi: 10.1007/s10858-005-1717-0
  28. Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11(4):852–861CrossRefGoogle Scholar
  29. Wang L, Markley JL (2009) Empirical correlation between protein backbone 15N and 13C secondary chemical shifts and its application to nitrogen chemical shift re-referencing. J Biomol NMR 44:95–99. doi: 10.1007/s10858-009-9324-0 CrossRefGoogle Scholar
  30. Wang Y, Wishart DS (2005) A simple method to adjust inconsistently referenced 13C and 15N chemical shift assignments of proteins. J Biomol NMR 31(2):143–148. doi: 10.1007/s10858-004-7441-3 Google Scholar
  31. Wishart DS, Sykes BD, Richards FM (1992) The chemical shift index: a fast and simple method for the assignment of protein secondary structure through NMR spectroscopy. Biochemistry 31(6):1647–1651CrossRefGoogle Scholar
  32. Zhang H, Neal S, Wishart DS (2003) RefDB: a database of uniformly referenced protein chemical shifts. J Biomol NMR 25(3):173–195CrossRefGoogle Scholar

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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