TY - JOUR
T1 - Type i and II β-turns prediction using NMR chemical shifts
AU - Wang, Ching Cheng
AU - Lai, Wen Chung
AU - Chuang, Woei Jer
N1 - Funding Information:
Acknowledgments 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.
PY - 2014/7
Y1 - 2014/7
N2 - 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 C β 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.
AB - 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 C β 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.
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U2 - 10.1007/s10858-014-9837-z
DO - 10.1007/s10858-014-9837-z
M3 - Article
C2 - 24838372
AN - SCOPUS:84904039891
SN - 0925-2738
VL - 59
SP - 175
EP - 184
JO - Journal of Biomolecular NMR
JF - Journal of Biomolecular NMR
IS - 3
ER -