Journal of Biomolecular NMR

, Volume 59, Issue 3, pp 175–184 | Cite as

Type I and II β-turns prediction using NMR chemical shifts

  • Ching-Cheng Wang
  • Wen-Chung Lai
  • Woei-Jer Chuang


A method for predicting type I and II β-turns using nuclear magnetic resonance (NMR) chemical shifts is proposed. Isolated β-turn chemical-shift data were collected from 1,798 protein chains. One-dimensional statistical analyses on chemical-shift data of three classes β-turn (type I, II, and VIII) showed different distributions at four positions, (i) to (i + 3). Considering the central two residues of type I β-turns, the mean values of Cο, Cα, HN, and NH chemical shifts were generally (i + 1) > (i + 2). The mean values of Cβ and Hα chemical shifts were (i + 1) < (i + 2). The distributions of the central two residues in type II and VIII β-turns were also distinguishable by trends of chemical shift values. Two-dimensional cluster analyses on chemical-shift data show positional distributions more clearly. Based on these propensities of chemical shift classified as a function of position, rules were derived using scoring matrices for four consecutive residues to predict type I and II β-turns. The proposed method achieves an overall prediction accuracy of 83.2 and 84.2 % with the Matthews correlation coefficient values of 0.317 and 0.632 for type I and II β-turns, indicating that its higher accuracy for type II turn prediction. The results show that it is feasible to use NMR chemical shifts to predict the β-turn types in proteins. The proposed method can be incorporated into other chemical-shift based protein secondary structure prediction methods.


β-Turn NMR Chemical shift Secondary structure Prediction 





Nuclear magnetic resonance


Protein data bank



The work has been partially financed by National Science Council of ROC, NSC-97-2221-E-006-079-MY3 and NSC-101-2311-B-006-009-MY3. We are grateful for their supports.

Supplementary material

10858_2014_9837_MOESM1_ESM.pdf (631 kb)
Supplementary material 1 (PDF 630 kb):


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Ching-Cheng Wang
    • 1
  • Wen-Chung Lai
    • 1
  • Woei-Jer Chuang
    • 2
  1. 1.Institute of Manufacturing Information and SystemsNational Cheng Kung University College of Electrical Engineering and Computer ScienceTainanTaiwan
  2. 2.Department of Biochemistry and Molecular BiologyNational Cheng Kung University College of MedicineTainanTaiwan

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