title: Prediction of protein structural classes using support vector machines.
abstract: The support vector machine, a machine-learning method, is used to predict the four structural classes, i.e. mainly alpha, mainly beta, alpha-beta and fss, from the topology-level of CATH protein structure database. For the binary classification, any two structural classes which do not share any secondary structure such as alpha and beta elements could be classified with as high as 90% accuracy. The accuracy, however, will decrease to less than 70% if the structural classes to be classified contain structure elements in common. Our study also shows that the dimensions of feature space 20(2) = 400 (for dipeptide) and 20(3) = 8 000 (for tripeptide) give nearly the same prediction accuracy. Among these 4 structural classes, multi-class classification gives an overall accuracy of about 52%, indicating that the multi-class classification technique in support of vector machines may still need to be further improved in future investigation.
authors.affiliation: College of Life Science and Biotechnology, Guangxi University, Nanning, Guangxi, China.
authors.forename: X-D
authors.initials: XD
authors.lastname: Sun
authors.forename: R-B
authors.initials: RB
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D016208:Databases, Factual; D008969:Molecular Sequence Data; D011237:Predictive Value of Tests; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D011506:Proteins