Literature DB >> 9147309

Prediction of beta-turns.

K C Chou1.   

Abstract

A residue-coupled model is proposed to predict the beta-turns in proteins. The rates of correct prediction for the 455 beta-turn tetrapeptides and 3807 non-beta-turn tetrapeptides in the training database are 94.7 and 81.3%, respectively. The rates of correct prediction for the 110 beta-turn tetrapeptides and 30,229 non-beta-turn tetrapeptides in the testing database are 80.0 and 80.2%, respectively. Compared with the rates of correct prediction based on the residue-independent model reported previously, the quality of prediction is significantly improved by the new model, implying that the residue-coupled effect along a polypeptide chain is important for the formation of reversal turns, such as beta-turns, during the process of protein folding.

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Year:  1997        PMID: 9147309

Source DB:  PubMed          Journal:  J Pept Res        ISSN: 1397-002X


  13 in total

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Authors:  Y Bai; J Chung; H J Dyson; P E Wright
Journal:  Protein Sci       Date:  2001-05       Impact factor: 6.725

2.  A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.

Authors:  Harpreet Kaur; G P S Raghava
Journal:  Protein Sci       Date:  2003-05       Impact factor: 6.725

3.  Prediction of beta-turns in proteins from multiple alignment using neural network.

Authors:  Harpreet Kaur; Gajendra Pal Singh Raghava
Journal:  Protein Sci       Date:  2003-03       Impact factor: 6.725

4.  Prediction of the beta-hairpins in proteins using support vector machine.

Authors:  Xiu Zhen Hu; Qian Zhong Li
Journal:  Protein J       Date:  2008-02       Impact factor: 2.371

5.  A deep dense inception network for protein beta-turn prediction.

Authors:  Chao Fang; Yi Shang; Dong Xu
Journal:  Proteins       Date:  2019-07-23

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

Authors:  Ching-Cheng Wang; Wen-Chung Lai; Woei-Jer Chuang
Journal:  J Biomol NMR       Date:  2014-05-17       Impact factor: 2.835

7.  Improving the performance of β-turn prediction using predicted shape strings and a two-layer support vector machine model.

Authors:  Zehui Tang; Tonghua Li; Rida Liu; Wenwei Xiong; Jiangming Sun; Yaojuan Zhu; Guanyan Chen
Journal:  BMC Bioinformatics       Date:  2011-07-13       Impact factor: 3.169

8.  Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts.

Authors:  Feng YongE; Kou GaoShan
Journal:  PLoS One       Date:  2015-09-30       Impact factor: 3.240

9.  Insight into a molecular interaction force supporting peptide backbones and its implication to protein loops and folding.

Authors:  Qi-Shi Du; Dong Chen; Neng-Zhong Xie; Ri-Bo Huang; Kuo-Chen Chou
Journal:  J Biomol Struct Dyn       Date:  2014-12-22

10.  Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.

Authors:  Ce Zheng; Lukasz Kurgan
Journal:  BMC Bioinformatics       Date:  2008-10-10       Impact factor: 3.169

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