Literature DB >> 24838372

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

Ching-Cheng Wang1, Wen-Chung Lai, Woei-Jer Chuang.   

Abstract

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, H(N), and N(H) 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.

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Year:  2014        PMID: 24838372     DOI: 10.1007/s10858-014-9837-z

Source DB:  PubMed          Journal:  J Biomol NMR        ISSN: 0925-2738            Impact factor:   2.835


  51 in total

1.  Prediction of the location and type of beta-turns in proteins using neural networks.

Authors:  A J Shepherd; D Gorse; J M Thornton
Journal:  Protein Sci       Date:  1999-05       Impact factor: 6.725

2.  RefDB: a database of uniformly referenced protein chemical shifts.

Authors:  Haiyan Zhang; Stephen Neal; David S Wishart
Journal:  J Biomol NMR       Date:  2003-03       Impact factor: 2.835

3.  Analysis and identification of beta-turn types using multinomial logistic regression and artificial neural network.

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Journal:  Bioinformatics       Date:  2007-06-28       Impact factor: 6.937

4.  Prediction of β-turn types in protein by using composite vector.

Authors:  Xiaobo Shi; Xiuzhen Hu; Shaobo Li; Xingxing Liu
Journal:  J Theor Biol       Date:  2011-07-19       Impact factor: 2.691

5.  13C NMR chemical shifts can predict disulfide bond formation.

Authors:  D Sharma; K Rajarathnam
Journal:  J Biomol NMR       Date:  2000-10       Impact factor: 2.835

6.  Turn prediction in proteins using a pattern-matching approach.

Authors:  F E Cohen; R M Abarbanel; I D Kuntz; R J Fletterick
Journal:  Biochemistry       Date:  1986-01-14       Impact factor: 3.162

7.  Protein phi and psi dihedral restraints determined from multidimensional hypersurface correlations of backbone chemical shifts and their use in the determination of protein tertiary structures.

Authors:  R D Beger; P H Bolton
Journal:  J Biomol NMR       Date:  1997-09       Impact factor: 2.835

8.  Protein beta-turn prediction using nearest-neighbor method.

Authors:  Saejoon Kim
Journal:  Bioinformatics       Date:  2004-01-01       Impact factor: 6.937

9.  2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts.

Authors:  Ching-Cheng Wang; Jui-Hung Chen; Wen-Chung Lai; Woei-Jer Chuang
Journal:  J Biomol NMR       Date:  2007-02-27       Impact factor: 2.582

10.  CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data.

Authors:  David S Wishart; David Arndt; Mark Berjanskii; Peter Tang; Jianjun Zhou; Guohui Lin
Journal:  Nucleic Acids Res       Date:  2008-05-30       Impact factor: 16.971

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  1 in total

1.  Predicting the redox state and secondary structure of cysteine residues using multi-dimensional classification analysis of NMR chemical shifts.

Authors:  Ching-Cheng Wang; Wen-Chung Lai; Woei-Jer Chuang
Journal:  J Biomol NMR       Date:  2016-09-09       Impact factor: 2.835

  1 in total

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