Journal of Structural and Functional Genomics

, Volume 13, Issue 4, pp 201–212 | Cite as

A unified NMR strategy for high-throughput determination of backbone fold of small proteins

  • Dinesh Kumar
  • Anmol Gautam
  • Ramakrishna V. Hosur


An efficient semi-automated strategy called PFBD (i.e. Protein Fold from Backbone Data only) has been presented for rapid backbone fold determination of small proteins. It makes use of NMR parameters involving backbone atoms only. These include chemical shifts, amide–amide NOEs and H-bonds. The backbone chemical shifts are obtained in an automated manner from the orthogonal 2D projections of variants of HNN and HN(C)N experiments (Kumar et al., in Magn Reson Chem 50(5):357–363, 2012) using AUTOBA (Borkar et al. in J Biomol NMR 50(3):285–297, 2011); backbone H-bonds are manually derived from constant time long-range 2D-HnCO spectrum (Cordier and Grzesiek in J Am Chem Soc 121:1601–1602, 1999); and amide–amide NOEs are derived from 3D HNCO NOESY experiment which provides NOEs along the direct 1H dimension that has maximum resolution (Lohr and Ruterjans in J Biomol NMR 9(1):371–388, 1997). All the experiments needed for the execution of PFBD can be recorded and analyzed in about 24–48 h depending upon the concentration of the protein and dispersion of amide cross-peaks in the 1H–15N correlation spectrum. Thus, we believe that the strategy, because of its speed and simplicity will be very valuable in Biomolecular NMR community for high-throughput structural proteomics of small folded proteins of MW < 10–12 kDa, the regime where NMR is generally preferred over X-ray crystallography. The strategy has been validated and demonstrated here on two small globular proteins: human ubiquitin (76 aa) and chicken SH3 domain (62 aa).


AUTOBA Automated backbone assignment Backbone fold Check points HNCO NOESY NMR PFBD Structural proteomics 



Automatic backbone assignment


Biological magnetic resonance bank


Computer aided resonance assigment (a software for NMR data analysis)


Heteronuclear single quantum correlation


Nuclear magnetic resonance


Nuclear overhauser effect spectroscopy


Protein data bank


Protein fold determination from backbone data



This work is being financially supported by the Department of Science and Technology under SERC Fast Track Scheme (Registration Number: SR/FT/LS-114/2011) for carrying out the research work. We gratefully acknowledge the High field NMR facility, at the Centre for Biomedical Magnetic Resonance (CBMR)—Lucknow, India where all the experiments were carried out. The basic 3D pulse sequences: HN(C)N, hNnH, hncoCANH, and hnCOcaNH and the software-program AUTOBA described here are freely accessible at:

Supplementary material

10969_2012_9144_MOESM1_ESM.doc (3 mb)
Plots for residue-wise dihedral angle constraints derived from backbone (1HN, 15N, 13Cα, and 13C′) and mainchain (1HN, 15N, 13Cα, 1Hα, 13Cβ and 13C′) resonances of ubiquitin have been shown in Figure S1. The reliability of 13Cα and 13C′ chemical shifts for estimating backbone dihedral angle (ϕ and ψ) constraints has also been evaluated in Appendix I by calculating the cumulative (13Cα and 13C′) secondary chemical shifts. Figure S2 displays residue-wise cumulative (13Cα and 13C′) secondary shifts both for human ubiquitin and chicken Sh3 domain. An illustrative stretch of HNCO-NOESY spectrum of chicken SH3 domain (for residues Ala11-Lys18) has been shown in Figure S3. An overlay of the long range constant time and normal 2D-HnCO spectra of Human Ubiquitin has been shown in Figure S4. An overlay of the long range constant time and normal 2D-HnCO spectra of chicken Sh3 domain has been shown in Figure S5. The 20 CYANA generated backbone structures of ubiquitin using backbone amide NOEs, backbone dihedral angles, and backbone RDCs data have been shown in supplementary material (Fig. S6). The coordinates of 20 best backbone structures by NMR and the used NMR restraints have been deposited in the Protein Data Bank under the accession codes: 2LD9 (for human ubiquitin) and 2LJ3 (for chicken SH3 domain). The various experiments recorded on two proteins and the corresponding acquisition parameters along with acquisition times in each case are given in Table S1. The structural statistics of ubiquitin NMR structure 2LD9 has been shown in Table S2. (DOC 3056 kb)


  1. 1.
    Powers R (2009) Advances in nuclear magnetic resonance for drug discovery. Expert Opin Drug Discov 4:1077–1098PubMedCrossRefGoogle Scholar
  2. 2.
    Atreya HS, Szyperski T (2005) Rapid NMR data collection. Methods Enzymol 394:78–108PubMedCrossRefGoogle Scholar
  3. 3.
    Atreya HS (2009) NMR methods for fast data acquisition. J Indian Inst Sci 90:87–104Google Scholar
  4. 4.
    Cavanagh J, Fairbrother WJ, Palmer AG, Skelton NJ, Rance M (2006) Protein NMR spectroscopy (Second Edition): principles and practice (ISBN-13: 978-0-12-164491-8, Academic Press)Google Scholar
  5. 5.
    Fiaux J, Bertelsen EB, Horwich AL, Wuthrich K (2002) NMR analysis of a 900K GroEL GroES complex. Nature 418:207–211PubMedCrossRefGoogle Scholar
  6. 6.
    Kupce E, Freeman R (2003) Fast multi-dimensional Hadamard spectroscopy. J Magn Reson 163:56–63PubMedCrossRefGoogle Scholar
  7. 7.
    Kupce E, Freeman R (2003) Fast multi-dimensional NMR of proteins. J Biomol NMR 25:349–354PubMedCrossRefGoogle Scholar
  8. 8.
    Kupce E, Freeman R (2003) Projection-reconstruction of three-dimensional NMR spectra. J Am Chem Soc 125:13958–13959PubMedCrossRefGoogle Scholar
  9. 9.
    Kwan AH, Mobli M, Gooley PR, King GF, Mackay JP (2011) Macromolecular NMR spectroscopy for the non-spectroscopist. FEBS J 278:687–703PubMedCrossRefGoogle Scholar
  10. 10.
    Lee KM, Androphy EJ, Baleja JD (1995) A novel method for selective isotope labeling of bacterially expressed proteins. J Biomol NMR 5:93–96PubMedCrossRefGoogle Scholar
  11. 11.
    Lescop E, Schanda P, Brutscher B (2007) A set of BEST triple-resonance experiments for time-optimized protein resonance assignment. J Magn Reson 187:163–169PubMedCrossRefGoogle Scholar
  12. 12.
    Lescop E, Brutscher B (2007) Hyperdimensional protein NMR spectroscopy in peptide-sequence space. J Am Chem Soc 129:11916–11917PubMedCrossRefGoogle Scholar
  13. 13.
    Lescop E, Rasia R, Brutscher B (2008) Hadamard amino-acid-type edited NMR experiment for fast protein resonance assignment. J Am Chem Soc 130:5014–5015PubMedCrossRefGoogle Scholar
  14. 14.
    Pervushin K, Riek R, Wider G, Wuthrich K (1997) Attenuated T2 relaxation by mutual cancellation of dipole–dipole coupling and chemical shift anisotropy indicates an avenue to NMR structures of very large biological macromolecules in solution. Proc Natl Acad Sci USA 94:12366–12371PubMedCrossRefGoogle Scholar
  15. 15.
    Riek R, Pervushin K, Wuthrich K (2000) TROSY and CRINEPT: NMR with large molecular and supramolecular structures in solution. Trends Biochem Sci 25:462–468PubMedCrossRefGoogle Scholar
  16. 16.
    Tugarinov V, Muhandiram R, Ayed A, Kay LE (2002) Four-dimensional NMR spectroscopy of a 723-residue protein: chemical shift assignments and secondary structure of malate synthase g. J Am Chem Soc 124:10025–10035PubMedCrossRefGoogle Scholar
  17. 17.
    Wagner G (1997) An account of NMR in structural biology. Nat Struct Biol 4(Suppl):841–844PubMedGoogle Scholar
  18. 18.
    Wuthrich K (1986) NMR of proteins and nucleic acids (ISBN 978-0-471-82893-8, Wiley).Google Scholar
  19. 19.
    Jaravine VA, Zhuravleva AV, Permi P, Ibraghimov I, Orekhov VY (2008) Hyperdimensional NMR spectroscopy with nonlinear sampling. J Am Chem Soc 130:3927–3936PubMedCrossRefGoogle Scholar
  20. 20.
    Bonvin A, Houben K, Guenneugues M, Kaptein R, Boelens R (2001) Rapid protein fold determination using secondary chemical shifts and cross-hydrogen bond 15N–13C′ scalar couplings (3hbJNC′). J Biomol NMR 21:221–233PubMedCrossRefGoogle Scholar
  21. 21.
    Tang Y, Schneider WM, Shen Y, Raman S, Inouye M, Baker D, Roth MJ, Montelione GT (2010) Fully automated high-quality NMR structure determination of small (2)H-enriched proteins. J Struct Funct Genomics 11:223–232PubMedCrossRefGoogle Scholar
  22. 22.
    Borkar A, Kumar D, Hosur RV (2011) AUTOBA: automation of backbone assignment from HN(C)N suite of experiments. J Biomol NMR 50:285–297PubMedCrossRefGoogle Scholar
  23. 23.
    Fiorito F, Hiller S, Wider G, Wuthrich K (2006) Automated resonance assignment of proteins: 6D APSY-NMR. J Biomol NMR 35:27–37PubMedCrossRefGoogle Scholar
  24. 24.
    Keller RLJ (2004) Optimizing the process of nuclear magnetic resonance spectrum analysis and computer aided resonance assignment. Thèse de doctorat, ETH Zurich Thesis No. 15947, SwitzerlandGoogle Scholar
  25. 25.
    Kobayashi N, Iwahara J, Koshiba S, Tomizawa T, Tochio N, Guntert P, Kigawa T, Yokoyama S (2007) KUJIRA, a package of integrated modules for systematic and interactive analysis of NMR data directed to high-throughput NMR structure studies. J Biomol NMR 39:31–52PubMedCrossRefGoogle Scholar
  26. 26.
    Lee W, Kim JH, Westler WM, Markley JL (2011) PONDEROSA, an automated 3D-NOESY peak picking program, enables automated protein structure determination. Bioinformatics 27:1727–1728PubMedCrossRefGoogle Scholar
  27. 27.
    Herrmann T, Guntert P, Wuthrich K (2002) Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS. J Biomol NMR 24:171–189PubMedCrossRefGoogle Scholar
  28. 28.
    Jung YS, Zweckstetter M (2004) Mars—robust automatic backbone assignment of proteins. J Biomol NMR 30:11–23PubMedCrossRefGoogle Scholar
  29. 29.
    Lescop E, Brutscher B (2009) Highly automated protein backbone resonance assignment within a few hours: the “BATCH” strategy and software package. J Biomol NMR 44:43–57PubMedCrossRefGoogle Scholar
  30. 30.
    Berjanskii M, Tang P, Liang J, Cruz JA, Zhou J, Zhou Y, Bassett E, MacDonell C, Lu P, Lin G, Wishart DS (2009) GeNMR: a web server for rapid NMR-based protein structure determination. Nucleic Acids Res 37:W670–W677PubMedCrossRefGoogle Scholar
  31. 31.
    Shen Y et al (2008) Consistent blind protein structure generation from NMR chemical shift data. Proc Natl Acad Sci USA 105:4685–4690PubMedCrossRefGoogle Scholar
  32. 32.
    Shen Y, Delaglio F, Cornilescu G, Bax A (2009) TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44:213–223PubMedCrossRefGoogle Scholar
  33. 33.
    Crippen GM, Rousaki A, Revington M, Zhang Y, Zuiderweg ER (2010) SAGA: rapid automatic mainchain NMR assignment for large proteins. J Biomol NMR 46:281–298PubMedCrossRefGoogle Scholar
  34. 34.
    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:W496–W502PubMedCrossRefGoogle Scholar
  35. 35.
    Zheng D, Huang YJ, Moseley HN, Xiao R, Aramini J, Swapna GV, Montelione GT (2003) Automated protein fold determination using a minimal NMR constraint strategy. Protein Sci 12:1232–1246PubMedCrossRefGoogle Scholar
  36. 36.
    Cordier F, Nisius L, Dingley AJ, Grzesiek S (2008) Direct detection of N–H[···]O=C hydrogen bonds in biomolecules by NMR spectroscopy. Nat Protoc 3:235–241PubMedCrossRefGoogle Scholar
  37. 37.
    Grzesiek S, Bax A (1992) Improved 3D triple-resonance NMR techniques applied to a 31 kDa protein. J Magn Reson A 96:432–440Google Scholar
  38. 38.
    Grzesiek S, Bax A (1992) An efficient experiment for sequential backbone assignment of medium-sized isotopically enriched proteins. J Magn Reson B 99:201–207Google Scholar
  39. 39.
    Grzesiek S, Bax A (1992) Correlating backbone amide and side chain resonances in larger proteins by multiple relayed triple resonance NMR. J Am Chem Soc 114:6293CrossRefGoogle Scholar
  40. 40.
    Lohr F, Ruterjans H (1997) Unambiguous NOE assignments in proteins by a combination of through-bond and through-space correlations. J Biomol NMR 9:371–388CrossRefGoogle Scholar
  41. 41.
    Yao J, Dyson HJ, Wright PE (1997) Chemical shift dispersion and secondary structure prediction in unfolded and partly folded proteins. FEBS Lett 419:285–289PubMedCrossRefGoogle Scholar
  42. 42.
    Kumar D, Borkar A, Hosur RV (2012) Facile Backbone (1H, 15N, 13Ca and 13C’) Assignment of 13C/15N labeled proteins using orthogonal projection planes of HNN and HN(C)N experiments and its Automation. Magn Reson Chem 50:357–363PubMedCrossRefGoogle Scholar
  43. 43.
    Guntert P, Mumenthaler C, Wuthrich K (1997) Torsion angle dynamics for NMR structure calculation with the new program DYANA. J Mol Biol 273:283–298PubMedCrossRefGoogle Scholar
  44. 44.
    Guntert P (2004) Automated NMR structure calculation with CYANA. Methods Mol Biol 278:353–378PubMedGoogle Scholar
  45. 45.
    Kumar D, Hosur RV (2010) An efficient high-throughput protocol based on 2D-HN(C)N for unambiguous HN and 15N backbone assignment in small folded proteins in less than a day. Res Commun Curr Sci 99:1581–1586Google Scholar
  46. 46.
    Bhavesh NS, Panchal SC, Hosur RV (2001) An efficient high-throughput resonance assignment procedure for structural genomics and protein folding research by NMR. Biochemistry 40:14727–14735PubMedCrossRefGoogle Scholar
  47. 47.
    Kumar D, Paul S, Hosur RV (2010) BEST-HNN and 2D-(HN)NH experiments for rapid backbone assignment in proteins. J Magn Reson 204:111–117PubMedCrossRefGoogle Scholar
  48. 48.
    Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13:289–302PubMedCrossRefGoogle Scholar
  49. 49.
    Berjanskii MV, Neal S, Wishart DS (2006) PREDITOR: a web server for predicting protein torsion angle restraints. Nucleic Acids Res 34:W63–W69PubMedCrossRefGoogle Scholar
  50. 50.
    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–S167PubMedCrossRefGoogle Scholar
  51. 51.
    Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci USA 104:9615–9620PubMedCrossRefGoogle Scholar
  52. 52.
    Raman S, Lange OF, Rossi P, Tyka M, Wang X, Aramini J, Liu G, Ramelot TA, Eletsky A, Szyperski T, Kennedy MA, Prestegard J, Montelione GT, Baker D (2010) NMR structure determination for larger proteins using backbone-only data. Science 327:1014–1018PubMedCrossRefGoogle Scholar
  53. 53.
    Brunger AT, Adams PD, Clore GM, DeLano WL, Gros P, Grosse-Kunstleve RW, Jiang JS, Kuszewski J, Nilges M, Pannu NS, Read RJ, Rice LM, Simonson T, Warren GL (1998) Crystallography & NMR system (CNS): a new software suite for macromolecular structure determination. Acta Crystallogr D Biol Crystallogr 54:905–921PubMedCrossRefGoogle Scholar
  54. 54.
    Brunger AT, Nilges M (1993) Computational challenges for macromolecular structure determination by X-ray crystallography and solution NMR-spectroscopy. Q Rev Biophys 26:49–125PubMedCrossRefGoogle Scholar
  55. 55.
    Brunger AT (1993) X-PLOR version 3.1: a system for X-ray crystallography and NMR (ISBN-13: 978-0300054026 - Yale University Press)Google Scholar
  56. 56.
    Case DA, Cheatham TE III, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688PubMedCrossRefGoogle Scholar
  57. 57.
    Habeck M, Rieping W, Linge JP, Nilges M (2004) NOE assignment with ARIA 2.0: the nuts and bolts. Methods Mol Biol 278:379–402PubMedGoogle Scholar
  58. 58.
    Williamson M, Craven C (2009) Automated protein structure calculation from NMR data. J Biomol NMR 43:131–143PubMedCrossRefGoogle Scholar
  59. 59.
    Dancea F, Gunther U (2005) Automated protein NMR structure determination using wavelet de-noised NOESY spectra. J Biomol NMR 33:139–152PubMedCrossRefGoogle Scholar
  60. 60.
    Fiorito F, Herrmann T, Damberger FF, Wuthrich K (2008) Automated amino acid side-chain NMR assignment of proteins using (13)C- and (15)N-resolved 3D [(1)H, (1)H]-NOESY. J Biomol NMR 42:23–33PubMedCrossRefGoogle Scholar
  61. 61.
    Rosato A, Aramini JM, Arrowsmith C, Bagaria A, Baker D, Cavalli A (2012) Blind testing of routine, fully automated determination of protein structures from NMR data. Structure 20:227–236PubMedCrossRefGoogle Scholar
  62. 62.
    Cordier F, Grzesiek S (1999) Direct observation of hydrogen bonds in proteins by interresidue 3hJNC′ scalar couplings. J Am Chem Soc 121:1601–1602CrossRefGoogle Scholar
  63. 63.
    Musacchio A, Noble M, Pauptit R, Wierenga R, Saraste M (1992) Crystal structure of a Src-homology 3 (SH3) domain. Nature 359:851–855PubMedCrossRefGoogle Scholar
  64. 64.
    Vijay-Kumar S, Bugg CE, Cook WJ (1987) Structure of ubiquitin refined at 1.8 A resolution. J Mol Biol 194:531–544PubMedCrossRefGoogle Scholar
  65. 65.
    Andrec M, Du P, Levy RM (2001) Protein backbone structure determination using only residual dipolar couplings from one ordering medium. J Biomol NMR 21:335–347PubMedCrossRefGoogle Scholar
  66. 66.
    Hus JC, Marion D, Blackledge M (2001) Determination of protein backbone structure using only residual dipolar couplings. J Am Chem Soc 123:1541–1542PubMedCrossRefGoogle Scholar
  67. 67.
    Rohl CA, Baker D (2002) De novo determination of protein backbone structure from residual dipolar couplings using Rosetta. J Am Chem Soc 124:2723–2729PubMedCrossRefGoogle Scholar
  68. 68.
    Sippl MJ, Wiederstein M (2008) A note on difficult structure alignment problems. Bioinformatics 24:426–427PubMedCrossRefGoogle Scholar
  69. 69.
    Sippl MJ (2008) On distance and similarity in fold space. Bioinformatics 24:872–873PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Centre of Biomedical Magnetic Resonance (CBMR)LucknowIndia
  2. 2.Department of Chemical SciencesTata Institute of Fundamental ResearchColaba, MumbaiIndia

Personalised recommendations