title: Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM.
abstract: The accurate identification of protein structure class solely using extracted information from protein sequence is a complicated task in the current computational biology. Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by fusing the sequence information and evolution information to represent a protein sample. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark data-sets, 1189 and 25PDB with sequence similarity lower than 40 and 25%, respectively. Comparison of our results with other methods shows that the proposed method by us is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity data-sets.
authors.affiliation: Department of Mathematics, Xidian University, Xi'an, P.R. China. zhangsl@xidian.edu.cn
authors.forename: Shengli
authors.initials: S
authors.lastname: Zhang
authors.forename: Feng
authors.initials: F
authors.lastname: Ye
authors.forename: Xiguo
authors.initials: X
authors.lastname: Yuan
mesh_terms: D000595:Amino Acid Sequence; D030562:Databases, Protein; D025341:Principal Component Analysis; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods; D060388:Support Vector Machine
chemical_list: D011506:Proteins