Journal of Biomolecular NMR

, Volume 56, Issue 2, pp 155–167 | Cite as

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

  • K. J. Fritzsching
  • Y. Yang
  • K. Schmidt-Rohr
  • Mei HongEmail author


We introduce a Python-based program that utilizes the large database of 13C and 15N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D 13C–13C, 15N–13C, or 3D 15N–13C–13C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D 13C–13C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cβ chemical shifts, the highest-ranked PLUQ assignments were 40–60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO–Cα–Cβ or N–Cα–Cβ), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.


Chemical shift correlation Amino-acid type assignment PLUQ Secondary structure Protein resonance assignment 



This research was supported by NIH grant GM088204. We are grateful to Mr. Wonghee Lee for correcting parsing errors in the original PACSY database.

Supplementary material

10858_2013_9732_MOESM1_ESM.pdf (10.3 mb)
Supplementary material 1 (PDF 10578 kb)


  1. Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13:289–302CrossRefGoogle Scholar
  2. Creighton TE (ed) (1993) Proteins: Structures and molecular properties. W.H. Freeman and Co., New YorkGoogle Scholar
  3. Eghbalnia HR, Bahrami A, Wang LY, Assadi A, Markley JL (2005) Probabilistic identification of spin systems and their assignments including coil-helix inference as output (PISTACHIO). J Biomol NMR 32:219–233CrossRefGoogle Scholar
  4. Franks WT, Zhou DH, Wylie BJ, Money BG, Graesser DT, Frericks HL, Sahota G, Rienstra CM (2005) Magic-angle spinning solid-state NMR spectroscopy of the beta1 immunoglobulin binding domain of protein G (GB1): 15 N and 13C chemical shift assignments and conformational analysis. J Am Chem Soc 127:12291–12305CrossRefGoogle Scholar
  5. Heinig M, Frishman D (2004) STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res 32:W500–W502CrossRefGoogle Scholar
  6. Herrmann T, Guntert P, Wuthrich K (2002) Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA. J Mol Biol 39:209–227CrossRefGoogle Scholar
  7. Hiller S, Garces RG, Malia TJ, Orekhov VY, Colombini M, Wagner G (2008) Solution structure of the integral human membrane protein VDAC-1 in detergent micelles. Science 321:1206–1210ADSCrossRefGoogle Scholar
  8. Hong M (2006) Solid-state NMR studies of the structure, dynamics, and assembly of beta-sheet membrane peptides and alpha-helical membrane proteins with antibiotic activities. Acc Chem Res 39:176–183CrossRefGoogle Scholar
  9. Hong M, Mishanina TV, Cady SD (2009) Accurate measurement of methyl 13C chemical shifts by solid-state NMR for the determination of protein sidechain conformation: the influenza M2 transmembrane peptide as an example. J Am Chem Soc 131:7806–7816CrossRefGoogle Scholar
  10. Hong M, Zhang Y, Hu F (2012) Membrane protein structure and dynamics from NMR spectroscopy. Annu Rev Phys Chem 63:1–24ADSCrossRefGoogle Scholar
  11. Hu KN, McGlinchey RP, Wickner RB, Tycko R (2011a) Segmental polymorphism in a functional amyloid. Biophys J 101:2242–2250CrossRefGoogle Scholar
  12. Hu KN, Qiang W, Tycko R (2011b) A general Monte Carlo/simulated annealing algorithm for resonance assignment in NMR of uniformly labeled biopolymers. J Biomol NMR 50:267–276CrossRefGoogle Scholar
  13. Ikeda K, Egawa A, Fujiwara T (2013) Secondary structural analysis of proteins based on (13)C chemical shift assignments in unresolved solid-state NMR spectra enhanced by fragmented structure database. J Biomol NMR 55:189–200CrossRefGoogle Scholar
  14. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern-recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637CrossRefGoogle Scholar
  15. Lee W, Yu W, Kim S, Chang I, Lee W, Markley JL (2012) PACSY, a relational database management system for protein structure and chemical shift analysis. J Biomol NMR 54:169–179CrossRefGoogle Scholar
  16. Linge JP, Habeck M, Rieping W, Nilges M (2003) ARIA: automated NOE assignment and NMR structure calculation. Bioinformatics 19:315–316CrossRefGoogle Scholar
  17. Loquet A, Sgourakis NG, Gupta R, Giller K, Riedel D, Goosmann C, Griesinger C, Kolbe M, Baker D, Becker S, Lange A (2012) Atomic model of the type III secretion system needle. Nature 486:276–279ADSGoogle Scholar
  18. Lovell SC, Word JM, Richardson JS, Richardson DC (2000) The penultimate rotamer library. Proteins: Structure. Function, and Genetics 40:389–408CrossRefGoogle Scholar
  19. Markley JL, Ulrich EL, Berman HM, Henrick K, Nakamura H, Akutsu H (2008) BioMagResBank (BMRB) as a partner in the Worldwide Protein Data Bank (wwPDB): new policies affecting biomolecular NMR depositions. J Biomol NMR 40:153–155CrossRefGoogle Scholar
  20. McDermott AE (2009) Structure and dynamics of membrane proteins by magic angle spinning solid-state NMR. Annu. Rev. Biophys. 38:385–403MathSciNetCrossRefGoogle Scholar
  21. Rohl CA, Strauss CEM, Misura KMS, Baker D (2004) Protein structure prediction using ROSETTA. Methods Enzymology 383:66–93CrossRefGoogle Scholar
  22. Schmidt-Rohr K, Fritzsching KJ, Liao SY, Hong M (2012) Spectral editing of two-dimensional magic-angle-spinning solid-state NMR spectra for protein resonance assignment and structure determination. J Biomol NMR 54:343–353CrossRefGoogle Scholar
  23. Shen Y, Lange O, Delaglio F, Rossi P, Aramini JM, Liu GH, Eletsky A, Wu YB, Singarapu KK, Lemak A, Ignatchenko A, Arrowsmith CH, Szyperski T, Montelione GT, Baker D, Bax A (2008) Consistent blind protein structure generation from NMR chemical shift data. Proc Natl Acad Sci USA 105:4685–4690ADSCrossRefGoogle Scholar
  24. Shen Y, Delaglio F, Cornilescu G, Bax A (2009) TALOS plus : a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44:213–223CrossRefGoogle Scholar
  25. Tycko R, Hu KN (2010) A Monte Carlo/simulated annealing algorithm for sequential resonance assignment in solid state NMR of uniformly labeled proteins with magic-angle spinning. J Magn Reson 205:304–314ADSCrossRefGoogle Scholar
  26. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, Livny M, Mading S, Maziuk D, Miller Z, Nakatani E, Schulte CF, Tolmie DE, Wenger RK, Yao HY, Markley JL (2008) BioMagResBank. Nucleic Acids Res 36:D402–D408CrossRefGoogle Scholar
  27. Vranken WF, Boucher W, Stevens TJ, Fogh RH, Pajon A, Llinas P, Ulrich EL, Markley JL, Ionides J, Laue ED (2005) The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins-Structure Function and Bioinformatics 59:687–696CrossRefGoogle Scholar
  28. Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11:852–861CrossRefGoogle Scholar
  29. Wasmer C, Lange A, Van Melckebeke H, Siemer AB, Riek R, Meier BH (2008) Amyloid fibrils of the HET-s(218–289) prion form a beta solenoid with a triangular hydrophobic core. Science 319:1523–1526ADSCrossRefGoogle Scholar
  30. Wishart DS, Sykes BD (1994) The C-13 Chemical-Shift Index - a Simple Method for the Identification of Protein Secondary Structure Using C-13 Chemical-Shift Data. J Biomol NMR 4:171–180CrossRefGoogle Scholar
  31. Wishart DS, Sykes BD, Richards FM (1991) Relationship between nuclear magnetic resonance chemical shift and protein secondary structure. J Mol Biol 222:311–333CrossRefGoogle Scholar
  32. Ye CH, Fu RQ, Hu JZ, Hou L, Ding SW (1993) C-13 Chemical-Shift Anisotropies of Solid Amino-Acids. Magn Reson Chem 31:699–704CrossRefGoogle Scholar
  33. Zawadzka-Kazimierczuk A, Kozminski W, Billeter M (2012) TSAR: a program for automatic resonance assignment using 2D cross-sections of high dimensionality, high-resolution spectra. J Biomol NMR 54:81–95CrossRefGoogle Scholar
  34. Zimmerman DE, Kulikowski CA, Huang YP, Feng WQ, Tashiro M, Shimotakahara S, Chien CY, Powers R, Montelione GT (1997) Automated analysis of protein NMR assignments using methods from artificial intelligence. J Mol Biol 269:592–610CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • K. J. Fritzsching
    • 1
  • Y. Yang
    • 1
  • K. Schmidt-Rohr
    • 1
  • Mei Hong
    • 1
    Email author
  1. 1.Department of ChemistryIowa State UniversityAmesUSA

Personalised recommendations