Literature DB >> 1633801

A correlation-coefficient method to predicting protein-structural classes from amino acid compositions.

K C Chou1, C T Zhang.   

Abstract

A protein is usually classified into one of the following four structural classes: all alpha, all beta, (alpha + beta) and alpha/beta. In this paper, based on the maximum correlation-coefficient principle, a new formulation is proposed for predicting the structural class of a protein according to its amino acid composition. Calculations have been made for a development set of proteins from which the amino acid compositions for the standard structural classes were derived, and an independent set of proteins which are outside the development set. The former can test the self consistency of a method and the latter can test its extrapolating effectiveness. In both cases, the results showed that the new method gave a considerably higher rate of correct prediction than any of the previous methods, implying that a significant improvement has been achieved by implementing the maximum-correlation-coefficient principle in the new method.

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Year:  1992        PMID: 1633801     DOI: 10.1111/j.1432-1033.1992.tb17067.x

Source DB:  PubMed          Journal:  Eur J Biochem        ISSN: 0014-2956


  14 in total

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5.  Prediction of protein folding class using global description of amino acid sequence.

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Journal:  Proc Natl Acad Sci U S A       Date:  1995-09-12       Impact factor: 11.205

6.  An analysis of protein folding type prediction by seed-propagated sampling and jackknife test.

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7.  Studies on the specificity of HIV protease: an application of Markov chain theory.

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9.  Semi-supervised protein subcellular localization.

Authors:  Qian Xu; Derek Hao Hu; Hong Xue; Weichuan Yu; Qiang Yang
Journal:  BMC Bioinformatics       Date:  2009-01-30       Impact factor: 3.169

10.  iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach.

Authors:  Wang-Ren Qiu; Xuan Xiao; Wei-Zhong Lin; Kuo-Chen Chou
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