title: A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC.
abstract: Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php.
authors: School of Mathematics and Computational Science, Xiangtan University, Hunan 411105, China.
authors: Guo-Sheng
authors: GS
authors: Han
authors: School of Mathematics and Computational Science, Xiangtan University, Hunan 411105, China; School of Mathematical Science, Queensland University of Technology, GPO Box 2434, Brisbane Q 4001, Australia. Electronic address: yuzg1970@yahoo.com.
authors: Zu-Guo
authors: ZG
authors: Yu
authors: School of Mathematical Science, Queensland University of Technology, GPO Box 2434, Brisbane Q 4001, Australia.
authors: Vo
authors: V
authors: Anh
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry / Q000145:classification*; D000818:Animals; D002627:Chemistry, Physical; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D008565:Membrane Proteins / Q000032:analysis / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D008565:Membrane Proteins
keywords: Amino acid classification; Feature extraction; Hilbert–Huang transform; Membrane protein type; Support vector machine