Journal of Biomolecular NMR

, Volume 51, Issue 4, pp 411–424 | Cite as

Uncovering symmetry-breaking vector and reliability order for assigning secondary structures of proteins from atomic NMR chemical shifts in amino acids

  • Wookyung Yu
  • Woonghee Lee
  • Weontae Lee
  • Suhkmann Kim
  • Iksoo Chang
Article

Abstract

Unravelling the complex correlation between chemical shifts of 13 C α13 C β13 C′, 1 H α15 N1 H N atoms in amino acids of proteins from NMR experiment and local structural environments of amino acids facilitates the assignment of secondary structures of proteins. This is an important impetus for both determining the three-dimensional structure and understanding the biological function of proteins. The previous empirical correlation scores which relate chemical shifts of 13 C α13 C β13 C′, 1 H α15 N1 H N atoms to secondary structures resulted in progresses toward assigning secondary structures of proteins. However, the physical-mathematical framework for these was elusive partly due to both the limited and orthogonal exploration of higher-dimensional chemical shifts of hetero-nucleus and the lack of physical-mathematical understanding underlying those correlation scores. Here we present a simple multi-dimensional hetero-nuclear chemical shift score function (MDHN-CSSF) which captures systematically the salient feature of such complex correlations without any references to a random coil state of proteins. We uncover the symmetry-breaking vector and its reliability order not only for distinguishing different secondary structures of proteins but also for capturing the delicate sensitivity interplayed among chemical shifts of 13 C α13 C β13 C′, 1 H α15 N1 H N atoms simultaneously, which then provides a straightforward framework toward assigning secondary structures of proteins. MDHN-CSSF could correctly assign secondary structures of training (validating) proteins with the favourable (comparable) Q3 scores in comparison with those from the previous correlation scores. MDHN-CSSF provides a simple and robust strategy for the systematic assignment of secondary structures of proteins and would facilitate the de novo determination of three-dimensional structures of proteins.

Keywords

Assigning secondary structures of proteins NMR chemical shift Complex correlation between chemical shifts and secondary structures of proteins Singular value decomposition analysis 

Notes

Acknowledgments

This work was supported by the Creative Research Initiative (Center for Proteome Biophysics, Grant No. 2011-0000041 to W.Y. and I.C.) of National Research Foundation/Ministry of Education, Science and Technology, Korea. This research was also supported by World Class University (WCU) program (R33-2009-000-10123-0 to W.L.). Authors would like to thank Prof. Kurt Wuthrich for fruitful discussion and Weonjoong Kim for constructing the web-server for MDHN-CSSF.

Supplementary material

10858_2011_9579_MOESM1_ESM.pdf (4.5 mb)
Supplementary material 1 (PDF 4612 KB)
10858_2011_9579_MOESM2_ESM.pdf (6 mb)
Supplementary material 2 (PDF 6177 KB)
10858_2011_9579_MOESM3_ESM.pdf (5.3 mb)
Supplementary material 3 (PDF 5466 KB)
10858_2011_9579_MOESM4_ESM.pdf (42 kb)
Supplementary material 4 (PDF 41 KB)
10858_2011_9579_MOESM5_ESM.pdf (26 kb)
Supplementary material 5 (PDF 25 KB)

References

  1. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242CrossRefGoogle Scholar
  2. Bowers PM, Strauss CE, Baker D (2000) De novo protein structure determination using sparse NMR data. J Biomol NMR 18(4):311–318CrossRefGoogle Scholar
  3. Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Sci Agric 253:164–170ADSCrossRefGoogle Scholar
  4. Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci USA 104:9615–9620ADSCrossRefGoogle Scholar
  5. Chandonia JM, Hon G, Walker NS, Lo Conte L, Koehl P, Levitt M, Brenner SE (2004) The ASTRAL compendium in 2004. Nucleic Acids Res 32:D189–D192CrossRefGoogle Scholar
  6. Chang I, Cieplak M, Dima RI, Maritan A, Banavar JR (2001) Protein threading by learning. Proc Natl Acad Sci USA 98:14350–14355ADSCrossRefGoogle 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. 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 32:71–81CrossRefGoogle Scholar
  9. Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins Struct Funct Genet 23:566–579CrossRefGoogle Scholar
  10. Gong H, Shen Y, Rose GD (2007) Building native protein conformation from NMR backbone chemical shifts using Monte Carlo fragment assembly. Protein Sci 16:1515–1521CrossRefGoogle Scholar
  11. Han B, Liu Y, Ginzinger S, Wishart D (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50:43–57CrossRefGoogle Scholar
  12. Heo M, Kim S, Moon EJ, Cheon M, Chung K, Chang I (2005) Perceptron learning of pairwise contact energies for proteins incorporating the amino acid environment. Phys Rev E 72:11906–11915ADSCrossRefGoogle Scholar
  13. Hung LH, Samudrala R (2003) Accurate and automated classification of protein secondary structure with PsiCSI. Protein Sci 12:288–295CrossRefGoogle 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. Krauth W, Mezard M (1987) Learning algorithms with optimal stability in neural networks. J Phys A 20:L745–L752MathSciNetADSCrossRefGoogle Scholar
  16. Leon SJ (1998) Linear algebra with applications. Prentice Hall, New JerseyGoogle Scholar
  17. Luginbuhl P, Szyperski T, Wuthrich K (1995) Statistical basis for the use of 13 C alpha chemical shift in protein structure determination. J Magn Reson B 1009:229–233CrossRefGoogle Scholar
  18. Neal S, Nip AM, Zhang H, Wishart DS (2003) Rapid and accurate calculation of protein 1H, 13C and 15N chemical shifts. J Biomol NMR 26:215–240CrossRefGoogle Scholar
  19. Pastore A, Saudek V (1990) The relationship between chemical shift and secondary structure in proteins. J Magn Reson 90:165–176CrossRefGoogle Scholar
  20. Shen Y, Bax A (2007) Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. J Biomol NMR 38:289–302CrossRefGoogle Scholar
  21. Shen Y, Lange O, Delaglio F, Rossi P, Aramini JM, Liu G, Eletsky A, Wu Y, 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
  22. 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 44:213–223CrossRefGoogle Scholar
  23. Spera S, Bax A (1991) Empirical correlation between protein backbone conformation and C-ALPHA and C-BETA 13C nuclear magnetic resonance chemical shifts. J Am Chem Soc 113:5490–5492CrossRefGoogle Scholar
  24. Szilagyi L, Jardetzky O (1989) ALPHA-proton chemical shift and secondary structure in proteins. J Magn Reson 83:441–449CrossRefGoogle Scholar
  25. 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 H, Markley JL (2007) BioMagResBank. Nucleic Acids Res 36:D402–D408CrossRefGoogle Scholar
  26. Wagner G, Pardi A, Wuthrich K (1983) Hydrogen-bond length and H-1-NMR chemical shifts in proteins. J Am Chem Soc 105:5948–5949CrossRefGoogle Scholar
  27. Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11:852–861CrossRefGoogle Scholar
  28. Wang CC, Chen JH, Lai WC, Chuang WJ (2007) 2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts. J Biomol NMR 38:57–63CrossRefGoogle Scholar
  29. 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 31:3316–3319CrossRefGoogle Scholar
  30. Wishart DS, Sykes BD (1994) 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
  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. 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. Biochem Cell Biol 31(6):1647–1651CrossRefGoogle Scholar
  33. 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–W502CrossRefGoogle Scholar
  34. Zhang H, Neal S, Wishart DS (2003) RefDB: a database of uniformly referenced protein chemical shifts. J Biomol NMR 25:173–195CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Wookyung Yu
    • 1
  • Woonghee Lee
    • 2
  • Weontae Lee
    • 2
  • Suhkmann Kim
    • 3
  • Iksoo Chang
    • 1
  1. 1.Department of Physics, Center for Proteome BiophysicsPusan National UniversityBusanKorea
  2. 2.Department of Biochemistry, Structural Biochemistry and Molecular Biophysics LaboratoryYonsei UniversitySeoulKorea
  3. 3.Department of Chemistry, Biochemistry and Bio-NMR LaboratoryPusan National UniversityBusanKorea

Personalised recommendations