title: 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.
authors.affiliation: College of Information science and Engineering, Hunan University, Changsha, Hunan, China. dragonbw@163.com
authors.forename: Bo
authors.initials: B
authors.lastname: Liao
authors.forename: Qilin
authors.initials: Q
authors.lastname: Xiang
authors.forename: Dachao
authors.initials: D
authors.lastname: Li
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D057927:Hydrophobic and Hydrophilic Interactions; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D020539:Sequence Analysis, Protein / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D011506:Proteins