Incorporating Secondary Features into the General form of Chou's PseAAC for Predicting Protein Structural Class

Title:Incorporating Secondary Features into the General form of Chou's PseAAC for Predicting Protein Structural Class

Volume: 19 Issue: 11

Author(s): Bo Liao, Qilin Xiang and Dachao Li

Affiliation:

Keywords: Binary sequence, feature representation, protein structure class, pseudo-amino acid composition(PseAAC), representation of protein sequence, support vector machine (SVM)

Abstract: Protein structure information is very useful for the confirmation of protein function. The protein structural class can provide information for protein 3D structure analysis, causing the conformation of the protein overall folding type plays a significant part in molecular biology. In this paper, we focus on the prediction of protein structural class which was based on new feature representation. We extract features from the Chou-Fasman parameter, amino acid compositions, amino acids hydrophobicity features, polarity information and pair-coupled amino acid composition. The prediction result by the Support vector machine (SVM) classifier shows that our method is better than some others.

Cite this article as:

Liao Bo, Xiang Qilin and Li Dachao, Incorporating Secondary Features into the General form of Chou's PseAAC for Predicting Protein Structural Class, Protein & Peptide Letters 2012; 19 (11) . https://dx.doi.org/10.2174/092986612803217051

DOI https://dx.doi.org/10.2174/092986612803217051	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305