Abstract
Chemical shifts of amino acids in proteins are the most sensitive and easily obtainable NMR parameters that reflect the primary, secondary, and tertiary structures of the protein. In recent years, chemical shifts have been used to identify secondary structure in peptides and proteins, and it has been confirmed that 1Hα, 13Cα, 13Cβ, and 13C′ NMR chemical shifts for all 20 amino acids are sensitive to their secondary structure. Currently, most of the methods are purely based on one-dimensional statistical analyses of various chemical shifts for each residue to identify protein secondary structure. However, it is possible to achieve an increased accuracy from the two-dimensional analyses of these chemical shifts. The 2DCSi approach performs two-dimension cluster analyses of 1Hα, 1HN, 13Cα, 13Cβ, 13C′, and 15NH chemical shifts to identify protein secondary structure and the redox state of cysteine residue. For the analysis of paired chemical shifts of 6 data sets, each of the 20 amino acids has its own 15 two-dimension cluster scattering diagrams. Accordingly, the probabilities for identifying helix and extended structure were calculated by using our scoring matrix. Compared with existing the chemical shift-based methods, it appears to improve the prediction accuracy of secondary structure identification, particularly in the extended structure. In addition, the probability of the given residue to be helix or extended structure is displayed, allows the users to make decisions by themselves.
Similar content being viewed by others
Abbreviations
- 2D:
-
Two-dimension
- BMRB:
-
BioMagResBank
- H:
-
α-helix
- G:
-
310-helix
- I:
-
π-helix
- B:
-
β-strand
- E:
-
Extended structure
- C:
-
Random coil structure
- NMR:
-
Nuclear magnetic resonance
- PDB:
-
Protein data bank
References
Albrecht M, Tosatto SC, Lengauer T, Valle G (2003) Simple consensus procedures are effective and sufficient in secondary structure prediction. Protein Eng 16:459–462
Chou PY, Fasman GD (1974) Prediction of protein conformation. Biochemistry 13:222–245
Eghbalnai HR, Wang L, Bahrami A, Assadi A, Markley JL (2005) H-1, N-15 and C-13 resonance assignments of a protein involved in the autophagy process, At4g21980.1 from Arabidopsis. J Biomol NMR 32:71–81
Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins 23:566–579
Hung LH, Samudrala R (2003) Bioverse: functional, structural and contextual annotation of proteins and proteomes. Protein Sci 12:288–295
Iwadate M, Asakura T, Williamson MP (1999) C-alpha and C-beta carbon-13 chemical shifts in proteins from an empirical database. J Biomol NMR 13:199–211
Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637
Le H, Oldfield E (1994) Correlation between 15N NMR chemical shifts in proteins and secondary structure. J Biomol NMR 4:341–348
Luginbuhl P, Szyperski T, Wuthrich K (1995) Statistical Basis for the Use of 13 C alpha Chemical Shifts in Protein Structure Determination. J Magn Reson B 109:229–233
Pastore A, Saudek V (1990) The relationship between chemical shift and secondary structure in proteins. J Magn Reson 90:165–176
Rost B (2001) Review: protein secondary structure prediction continues to rise. J of Struct Biol 134:204–218
Sibley AB, Cosman M, Krishnan VV (2003) An empirical correlation between secondary structure content and averaged chemical shifts in proteins. Biophys J 84:1223–1227
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 113:5490–5492
Szilagyi L, Jardetzky O (1989) α-Proton chemical shifts and secondary structure in proteins. J Magn Reson 83:441–449
Wang CC, Chen JH, Yin SH, Chuang WJ (2006) Predicting the redox state and secondary structure of cysteine residues in proteins using NMR chemical shifts. Proteins 63:219–226
Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11:852–861
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 31:3316–3319
Wishart DS, Nip AM (1998) Protein chemical shift analysis: a practical guide. Biochem Cell Biol 76:153–163
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 4:171–180
Wishart DS, Richard FM, Sykes BD (1992) The chemical shift index: a fast and simple method for the assignment of protein secondary structure through NMR spectroscopy. Biochemistry 31:1647–1651
Zhang H, Neal S, Wishart DS (2003) RefDB: a database of uniformly referenced protein chemical shifts. J Biomol NMR 25:173–195
Acknowledgements
We are indebted to Dr. Wenya Huang for valuable comments. This work was supported by grants NSC-94-2323-B006-001 and NSC-93-2212-E-006 from the National Science Council, ROC, and by grant 91-B-FA09-1-4 from the Ministry of Education’s Program for Promoting Academic Excellence in Universities.
Author information
Authors and Affiliations
Corresponding author
Additional information
Grant sponsor: National Science Council of ROC; Grant numbers: NSC-94-2323-B006- 001, NSC-93-2212-E-006.
Electronic supplementary material
Below is the link to the electronic supplementary material. The Supplementary material contains I, II, III, IV, V and VI, respectively. In Supplementary material I, Table S1 shows the score matrix for identifying protein secondary structure. Table S2 lists global accuracy for each of 45 test proteins (∼5329) by using 2DCSi, CSI, and PsiCSI. Table S3 presents the identification errors in detail. Table S4 displays an example only with 1H nucleus assignment and the result by running 2DCSi. In Supplementary material II, a total of 117 one-dimensional frequency plots of 15NH, 1Hα, 1HN, 13Cβ, 13Cα, and 13C′ for each of 20 amino acids are presented. In Supplementary material III, 267 two-dimension cluster scattering diagrams are plotted. Supplementary material IV lists all 336 proteins with their prediction accuracy. Supplementary material V completely shows the results by using 2DCSi, CSI, and PsiCSI for 45 test proteins. Supplementary material VI lists same entries from PSSI reporting dataset with their prediction accuracy.
Rights and permissions
About this article
Cite this article
Wang, CC., Chen, JH., Lai, WC. et al. 2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts. J Biomol NMR 38, 57–63 (2007). https://doi.org/10.1007/s10858-007-9146-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10858-007-9146-x