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

• Published in: Journal of biomolecular NMR Vol. 59; no. 3; pp. 175 - 184
• Main Authors: Wang, Ching-Cheng, Lai, Wen-Chung, Chuang, Woei-Jer
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2014
• Subjects: Amino Acid Sequence; Amino Acids - chemistry; Biochemistry; Biological and Medical Physics; Biophysics; Cluster Analysis; Magnetic Resonance Spectroscopy - methods; Models, Molecular; Molecular Sequence Data; Physics; Physics and Astronomy; Protein Structure, Secondary; Proteins - chemistry; Spectroscopy/Spectrometry; β-Turn; NMR; Chemical shift; Secondary structure; Prediction
• ISSN: 0925-2738; 1573-5001
• DOI: 10.1007/s10858-014-9837-z

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 C ο , C α , H N , and N H chemical shifts were generally ( i  + 1) > ( i  + 2). The mean values of C β and H α 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.