Journal of Biomolecular NMR

, Volume 60, Issue 2–3, pp 131–146 | Cite as

CSI 2.0: a significantly improved version of the Chemical Shift Index

  • Noor E. Hafsa
  • David S. Wishart


Protein chemical shifts have long been used by NMR spectroscopists to assist with secondary structure assignment and to provide useful distance and torsion angle constraint data for structure determination. One of the most widely used methods for secondary structure identification is called the Chemical Shift Index (CSI). The CSI method uses a simple digital chemical shift filter to locate secondary structures along the protein chain using backbone 13C and 1H chemical shifts. While the CSI method is simple to use and easy to implement, it is only about 75–80 % accurate. Here we describe a significantly improved version of the CSI (2.0) that uses machine-learning techniques to combine all six backbone chemical shifts (13Cα, 13Cβ, 13C, 15N, 1HN, 1Hα) with sequence-derived features to perform far more accurate secondary structure identification. Our tests indicate that CSI 2.0 achieved an average identification accuracy (Q3) of 90.56 % for a training set of 181 proteins in a repeated tenfold cross-validation and 89.35 % for a test set of 59 proteins. This represents a significant improvement over other state-of-the-art chemical shift-based methods. In particular, the level of performance of CSI 2.0 is equal to that of standard methods, such as DSSP and STRIDE, used to identify secondary structures via 3D coordinate data. This suggests that CSI 2.0 could be used both in providing accurate NMR constraint data in the early stages of protein structure determination as well as in defining secondary structure locations in the final protein model(s). A CSI 2.0 web server ( is available for submitting the input queries for secondary structure identification.


Nuclear magnetic resonance Chemical shifts Secondary structure multi-class support-vector machine Markov model 



The authors would like to thank Yongjie Liang for his help in preparing the CSI 2.0 web server. Financial support from the Natural Sciences and Engineering Research Council (NSERC), the Alberta Prion Research Institute (APRI) and PrioNet is gratefully acknowledged.


  1. Adamczak R, Porollo A, Meller J (2005) Combining prediction of secondary structure and solvent accessibility in proteins. Proteins Struct Funct Bioinform 59(3):467–475CrossRefGoogle Scholar
  2. Adams PD, Baker D, Brunger AT, Das R, DiMaio F, Read RJ, Richardson DC, Richardson JS, Terwilliger TC (2013) Advances, interactions, and future developments in the CNS, Phenix and Rosetta structural biology software systems. Annu Rev Biophys 43:265–287CrossRefGoogle Scholar
  3. Alexander PA, He Y, Chen Y, Orban J, Bryan PN (2009) A minimal sequence code for switching protein structure and function. Proc Natl Acad Sci 106(50):21149–21154ADSCrossRefGoogle Scholar
  4. 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
  5. Andrec M, Snyder DA, Zhou Z, Young J, Montelione GT, Levy RM (2007) A large data set comparison of protein structures determined by crystallography and NMR: statistical test for structural differences and the effect of crystal packing. Proteins Struct Funct Bioinform 69(3):449–465CrossRefGoogle Scholar
  6. Berjanskii MV, Wishart DS (2005) A simple method to predict protein flexibility using secondary chemical shifts. J Am Chem Soc 127(43):14970–14971CrossRefGoogle Scholar
  7. Berjanskii M, Tang P, Liang J, Cruz JA, Zhou J, Zhou Y, Bassett E, MacDonell C, Lu P, Wishart DS (2009) GeNMR: a web server for rapid NMR-based protein structure determination. Nucleic Acids Res 37((Web server issue)):W670–W677CrossRefGoogle Scholar
  8. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242CrossRefGoogle Scholar
  9. Camilloni C, De Simone A, Vranken WF, Vendruscolo M (2012) Determination of secondary structure populations in disordered states of proteins using nuclear magnetic resonance chemical shifts. Biochemistry 51(11):2224–2231CrossRefGoogle Scholar
  10. Cheung MS, Maguire ML, Stevens TJ, Broadhurst RW (2010) DANGLE: a Bayesian inferential method for predicting protein backbone dihedral angles and secondary structure. J Magn Reson 202(2):223–233ADSCrossRefGoogle Scholar
  11. Cole C, Barber JD, Barton GJ (2008) The Jpred 3 secondary structure prediction server. Nucleic Acids Res 36(suppl 2):W197–W201CrossRefGoogle Scholar
  12. Development Core Team R (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  13. Durrett R (2010) Probability: theory and examples, vol 3. Cambridge University Press, LondonCrossRefGoogle Scholar
  14. Eghbalnia HR, Wang L, Bahrami A, Assadi A, Markley JL (2005) Protein energetic conformational analysis from NMR chemical shifts (PECAN) and its use in determining secondary structural elements. J Biomol NMR 32(1):71–81CrossRefGoogle Scholar
  15. Eyrich VA, Marti-Renom MA, Przybylski D, Madhusudhan MS, Fiser A, Pazos F, Valencia A, Sali A, Rost B (2001) EVA: Continuous automatic evaluation of protein structure prediction servers. Bioinformatics 17:1242–1243CrossRefGoogle Scholar
  16. Fesinmeyer RM, Hudson FM, Olsen KA, White GW, Euser A, Andersen NH (2005) Chemical shifts provide fold populations and register of β-hairpins and β-sheets. J Biomol NMR 33(4):213–231CrossRefGoogle Scholar
  17. Frishman D, Argos P (1995) Knowledge‐based protein secondary structure assignment. Proteins Struct Funct Bioinform 23(4):566–579CrossRefGoogle Scholar
  18. Han B, Liu Y, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50(1):43–57CrossRefGoogle Scholar
  19. He B, Wang K, Liu Y, Xue B, Uversky VN, Dunker AK (2009) Predicting intrinsic disorder in proteins: an overview. Cell Res 19(8):929–949CrossRefGoogle Scholar
  20. Hung LH, Samudrala R (2003) Accurate and automated classification of protein secondary structure with PsiCSI. Protein Sci 12(2):288–295CrossRefGoogle Scholar
  21. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202CrossRefGoogle Scholar
  22. Jones DT, Tress M, Bryson K, Hadley C (1999) Successful recognition of protein folds using threading methods biased by sequence similarity and predicted secondary structure. Proteins Suppl 3:104–111CrossRefGoogle Scholar
  23. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637CrossRefGoogle Scholar
  24. Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) kernlab-an S4 package for kernel methods in R. J Stat Softw 11:1–20Google Scholar
  25. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(5):1–26Google Scholar
  26. Labudde D, Leitner D, Krüger M, Oschkinat H (2003) Prediction algorithm for amino acid types with their secondary structure in proteins (PLATON) using chemical shifts. J Biomol NMR 25(1):41–53CrossRefGoogle Scholar
  27. Levitt M (1978) Conformational preferences for globular proteins. J Am Chem Soc 17(20):4277–4284Google Scholar
  28. Mielke SP, Krishnan VV (2004) An evaluation of chemical shift index-based secondary structure determination in proteins: influence of random coil chemical shifts. J Biomol NMR 30(2):143–153CrossRefGoogle Scholar
  29. Mielke SP, Krishnan VV (2009) Characterization of protein secondary structure from NMR chemical shifts. Prog Nucl Magn Reson Spectrosc 54(3–4):141–165CrossRefGoogle Scholar
  30. Momen-Roknabadi A, Sadeghi M, Pezeshk H, Marashi SA (2008) Impact of residue accessible surface area on the prediction of protein secondary structures. BMC Bioinform 9(1):357CrossRefGoogle Scholar
  31. Montgomerie S, Sundraraj S, Gallin WJ, Wishart DS (2006) Improving the accuracy of protein secondary structure prediction using structural alignment. BMC Bioinform 7:301CrossRefGoogle Scholar
  32. Ratnaparkhi GS, Ramachandran S, Udgaonkar JB, Varadarajan R (1998) Discrepancies between the NMR and X-ray structures of uncomplexed barstar: analysis suggests that packing densities of protein structures determined by NMR are unreliable. Biochemistry 37(19):6958–6966CrossRefGoogle Scholar
  33. Rost B, Sander C, Schneider R (1994) Redefining the goals of protein secondary structure prediction. J Mol Biol 235:13–26CrossRefGoogle Scholar
  34. Schwarzinger S, Kroon GJ, Foss TR, Chung J, Wright PE, Dyson HJ (2001) Sequence-dependent correction of random coil NMR chemical shifts. J Am Chem Soc 123(13):2970–2978CrossRefGoogle Scholar
  35. Shen Y, Bax A (2012) Identification of helix capping and β-turn motifs from NMR chemical shifts. J Biomol NMR 52(3):211–232CrossRefGoogle Scholar
  36. Shen Y, Bax A (2013) Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. J Biomol NMR 56(3):227–241CrossRefGoogle Scholar
  37. Shen Y, Delaglio F, Cornilescu G, Bax A (2009a) TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44(4):213–223CrossRefGoogle Scholar
  38. Shen Y, Vernon R, Baker D, Bax A (2009b) De novo protein structure generation from incomplete chemical shift assignments. J Biomol NMR 43(2):63–78CrossRefGoogle Scholar
  39. Shen Y, Bryan PN, He Y, Orban J, Baker D, Bax A (2010) De novo structure generation using chemical shifts for proteins with high-sequence identity but different folds. Protein Sci 19(2):349–356CrossRefGoogle Scholar
  40. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W et al (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7(1):539CrossRefGoogle Scholar
  41. Soding J, Remmert M (2011) Protein sequence comparison and fold recognition: progress and good practice benchmarking. Curr Opin Struct Biol 21(3):404–411CrossRefGoogle Scholar
  42. Soding J, Biegert A, Lupas AN (2005) The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res 33((Web server issue)):W244–W248CrossRefGoogle Scholar
  43. Tyagi M, Bornot A, Offmann B, de Brevern AG (2009) Analysis of loop boundaries using different local structure assignment methods. Prot Sci 18(9):1869–1881CrossRefGoogle Scholar
  44. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J et al (2008) BioMagResBank. Nucleic Acids Res 36(Suppl 1):D402–D408Google Scholar
  45. UniProt Consortium (2010) The universal protein resource (UniProt) in 2010. Nucleic Acids Res 38(Suppl 1):D142–D148CrossRefGoogle Scholar
  46. Valdar WSJ (2002) Scoring residue conservation. Proteins Struct Funct Bioinform 48(2):227–241CrossRefGoogle Scholar
  47. Wang G, Dunbrack RLJ (2003) PISCES: a protein culling server. Bioinformatics 19(12):1589–1591CrossRefGoogle Scholar
  48. Wang Y, Jardetzky O (2002a) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11(4):852–861CrossRefGoogle Scholar
  49. Wang Y, Jardetzky O (2002b) Investigation of the neighboring residue effects on protein chemical shifts. J Am Chem Soc 124(47):14075–14084CrossRefGoogle Scholar
  50. Wang CC, Chen JH, Lai WC, Chuang WJ (2007a) 2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts. J Biomol NMR 38(1):57–63CrossRefGoogle Scholar
  51. Wang L, Eghbalnia HR, Markley JL (2007b) Nearest-neighbor effects on backbone alpha and beta carbon chemical shifts in proteins. J Biomol NMR 39(3):247–257CrossRefGoogle Scholar
  52. Willard L, Ranjan A, Zhang H, Monzavi H, Boyko RF, Sykes BD, Wishart DS (2003) VADAR: a web server for quantitative evaluation of protein structure quality. Nucleic Acids Res 31(13):3316–3319CrossRefGoogle Scholar
  53. Wishart DS (2011) Interpreting protein chemical shift data. Prog Nucl Magn Reson Spectrosc 58(1):62–87CrossRefGoogle Scholar
  54. Wishart DS, Case DA (2002) Use of chemical shifts in macromolecular structure determination. Methods Enzymol 338:3–34CrossRefGoogle Scholar
  55. Wishart DS, Nip AM (1998) Protein chemical shift analysis: a practical guide. Biochm Cell Biol 76(2–3):153–163Google Scholar
  56. Wishart DS, Sykes BD (1994a) Chemical shifts as a tool for structure determination. Methods Enzymol 239:363–392CrossRefGoogle Scholar
  57. Wishart DS, Sykes BD (1994b) The 13C chemical shift index: a simple method for the identification of protein secondary structure using 13C chemical shift data. J Biomol NMR 4(2):171–180CrossRefGoogle Scholar
  58. 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
  59. Wishart DS, Arndt D, Berjanskii M, Tang P, Zhou J, Lin G (2008) CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data. Nucleic Acids Res 36((Web server issue)):W496–W502CrossRefGoogle Scholar
  60. Wuthrich K (1986) NMR of proteins and nucleic acids. Wiley, New YorkGoogle Scholar
  61. Wuthrich K (1990) Protein structure determination in solution by NMR spectroscopy. J Bio Chem 265(36):22059–22062Google Scholar
  62. Zemla A, Venclovas C, Fidelis K, Rost B (1999) A modified definition of SOV, a segment-based measure for protein secondary structure prediction assessment. Proteins 34:220–223CrossRefGoogle Scholar
  63. Zhang H, Neal S, Wishat DS (2003) RefDB: a database of uniformly referenced protein chemical shifts. J Biomol NMR 25:173–195CrossRefGoogle Scholar
  64. Zhang W, Dunker AK, Zhou Y (2008) Assessing secondary structure assignment of protein structures by using pairwise sequence‐alignment benchmarks. Proteins Struct Funct Bioinform 71(1):61–67CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Department of Biological SciencesUniversity of AlbertaEdmontonCanada

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