PCA for predicting quaternary structure of protein

作     者：Tong WANG Hongbin SHEN Lixiu YAO Jie YANG Kuochen CHOU 

作者机构：Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghai 200240China Gordon Life Science InstituteSan DiegoCA 92130USA 

出 版 物：《Frontiers of Electrical and Electronic Engineering in China》 (中国电气与电子工程前沿（英文版）)

年 卷 期：2008年第3卷第4期

页      面：376-380页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.60704047) 

主　　题：principal component analysis(PCA) qua-ternary structure of protein pseudo position-specific score matrix(Pse-PSSM) dimension reduction method 

摘      要：The number and arrangement of subunits that form a protein are referred to as quaternary *** the quaternary structure of an uncharacterized protein provides clues to finding its biological function and interaction process with other molecules in a biological *** the explosion of protein sequences generated in the Post-Genomic Age,it is vital to develop an automated method to deal with such a *** explore this prob-lem,we adopted an approach based on the pseudo position-specific score matrix(Pse-PSSM)descriptor,proposed by Chou and Shen,representing a protein *** Pse-PSSM descriptor is advantageous in that it can combine the evolution information and sequence-correlated ***,incorporating all these effects into a descriptor may cause‘high dimension disaster’.To over-come such a problem,the fusion approach was adopted by Chou and Shen.A completely different approach,linear dimensionality reduction algorithm principal component analysis(PCA)is introduced to extract key features from the high-dimensional Pse-PSSM *** obtained dimension-reduced descriptor vector is a compact repre-sentation of the original high dimensional *** jack-knife test results indicate that the dimensionality reduction approach is efficient in coping with complicated problems in biological systems,such as predicting the quaternary struc-ture of proteins.