Literature DB >> 9263121

Classification and prediction of beta-turn types.

K C Chou1, J R Blinn.   

Abstract

Although a beta-turn consists of only four amino acids, it assumes many different types in proteins. Is this basically dependent on the tetrapeptide sequence alone or is it due to a variety of interactions with the other part of a protein? To answer this question, a residue-coupled model is proposed that can reflect the sequence-coupling effect for a tetrapeptide in not only a beta-turn or non-beta-turn, but also different types of a beta-turn. The predicted results by the model for 6022 tetrapeptides indicate that the rates of correct prediction for beta-turn types I, I', II, II', VI, and VIII and non-beta-turns are 68.54%, 93.60%, 85.19%, 97.75%, 100%, 88.75%, and 61.02%, respectively. Each of these seven rates is significantly higher than 1/7 = 14.29%, the completely randomized rate, implying that the formation of different beta-turn types or non-beta-turns is considerably correlated with the sequences of a tetrapeptide.

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Year:  1997        PMID: 9263121     DOI: 10.1023/a:1026366706677

Source DB:  PubMed          Journal:  J Protein Chem        ISSN: 0277-8033


  14 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.  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.  A hairpin turn in a class II MHC-bound peptide orients residues outside the binding groove for T cell recognition.

Authors:  Zarixia Zavala-Ruiz; Iwona Strug; Bruce D Walker; Philip J Norris; Lawrence J Stern
Journal:  Proc Natl Acad Sci U S A       Date:  2004-08-26       Impact factor: 11.205

5.  Prediction of interaction between small molecule and enzyme using AdaBoost.

Authors:  Bing Niu; Yuhuan Jin; Lin Lu; Kaiyan Fen; Lei Gu; Zhisong He; Wencong Lu; Yixue Li; Yudong Cai
Journal:  Mol Divers       Date:  2009-02-14       Impact factor: 2.943

6.  Coarse-grained, foldable, physical model of the polypeptide chain.

Authors:  Promita Chakraborty; Ronald N Zuckermann
Journal:  Proc Natl Acad Sci U S A       Date:  2013-07-29       Impact factor: 11.205

7.  Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

Authors:  Petros Kountouris; Jonathan D Hirst
Journal:  BMC Bioinformatics       Date:  2010-07-31       Impact factor: 3.169

8.  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

9.  Predicting turns in proteins with a unified model.

Authors:  Qi Song; Tonghua Li; Peisheng Cong; Jiangming Sun; Dapeng Li; Shengnan Tang
Journal:  PLoS One       Date:  2012-11-07       Impact factor: 3.240

10.  Thailandepsin a.

Authors:  Cheng Wang; Yi-Qiang Cheng
Journal:  Acta Crystallogr Sect E Struct Rep Online       Date:  2011-10-12
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