Incorporating secondary features into the general form of Chou's PseAAC for predicting protein structural class.

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.

journal_name

Protein Pept Lett

authors

Liao B,Xiang Q,Li D

doi

10.2174/092986612803217051

subject

Has Abstract

pub_date

2012-11-01 00:00:00

pages

1133-8

issue

11

eissn

0929-8665

issn

1875-5305

pii

BSP/ PPL/ E pub/0432

journal_volume

19

pub_type

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