title: Prediction of protein structure classes with flexible neural tree.
abstract: Prediction of protein structural classes is of great significance to better understand protein folding patterns. An array of methods has been proposed to predict these structures based on sequences. However, the accuracy is strongly affected by the homology of sequences. In the present study, the features based on correlation coefficient of sequence and amino acid composition are extracted. Flexible neutral tree is employed as the classification model. To examine the performance of this method, four benchmark datasets are selected. Altogether, the results show that a higher prediction accuracy of alpha/beta can be achieved by the method compared to others.
authors.affiliation: School of Information Science and Engineering, University of Jinan, Jinan, P.R. China.
authors.forename: Wenzheng
authors.initials: W
authors.lastname: Bao
authors.affiliation: School of Information Science and Engineering, University of Jinan, Jinan, P.R. China.
authors.forename: Yuehui
authors.initials: Y
authors.lastname: Chen
authors.affiliation: School of Information Science and Engineering, University of Jinan, Jinan, P.R. China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000648:ultrastructure*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins
keywords: Protein structural classification; correlation coefficient; flexible neural tree