2011China Journal of BioinformaticsRequires access

Protein Fold Pattern Recognition using Scoring Matrix

Wang Chun-lian

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Abstract

Based on the amino acid sequence conservation of Protein Fold Pattern.Protein fold pattern are predicted using amino acid composition and Polarity of amino acid,Friend or stranger river character of amino acid and Electric properties of amino acid .Moreover,using one-versus-others strategy and constructing position weight matrix and sequence pattern segment,Protein Fold Pattern is recognized by five similarity scoring functions.The best prediction accuracy can reach 83.46%.The results show that the scoring matrix is a effective method for multi-class protein fold prediction.

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What this paper is about

Based on the amino acid sequence conservation of Protein Fold Pattern.Protein fold pattern are predicted using amino acid composition and Polarity of amino acid,Friend or stranger river character of amino acid and Electric properties of amino acid .Moreover,using one-versus-others strategy and constructing position weight matrix and sequence pattern segment,Protein Fold Pattern is recognized by five similarity scoring functions.The best prediction accuracy can reach 83.46%.The results show that the scoring matrix is a effective method for multi-class protein fold prediction.

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Available abstract

Based on the amino acid sequence conservation of Protein Fold Pattern.Protein fold pattern are predicted using amino acid composition and Polarity of amino acid,Friend or stranger river character of amino acid and Electric properties of amino acid .Moreover,using one-versus-others strategy and constructing position weight matrix and sequence pattern segment,Protein Fold Pattern is recognized by five similarity scoring functions.The best prediction accuracy can reach 83.46%.The results show that the scoring matrix is a effective method for multi-class protein fold prediction.

Key concepts: Fold (higher-order function), Amino acid, Pattern recognition (psychology), Protein sequencing, Protein structure prediction, Peptide sequence, Matrix (chemical analysis), Computational biology

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