title: Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes.
abstract: Compared with the conventional amino acid composition (AA), the pseudo amino acid composition (PseAA) as originally introduced by Chou can incorporate much more information of a protein sequence; this remarkably enhances the power to use a discrete model for predicting various attributes of a protein. In this study, based on the concept of Chou's PseAA, a 46-D (dimensional) PseAA was formulated to represent the sample of a protein and a new approach based on binary-tree support vector machines (BTSVMs) was proposed to predict the protein structural class. BTSVMs algorithm has the capability in solving the problem of unclassifiable data points in multi-class SVMs. The results by both the 10-fold cross-validation and jackknife tests demonstrate that the predictive performance using the new PseAA (46-D) is better than that of AA (20-D), which is widely used in many algorithms for protein structural class prediction. The results obtained by the new approach are quite encouraging, indicating that it can at least play a complimentary role to many of the existing methods and is a useful tool for predicting many other protein attributes as well.
authors.affiliation: College of Information Sciences and Technology, Donghua University, Shanghai, China.
authors.forename: T-L
authors.initials: TL
authors.lastname: Zhang
authors.forename: Y-S
authors.initials: YS
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D000596:Amino Acids; D011506:Proteins