Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factor

被引:170
作者
Xiao, X
Shao, SH
Huang, ZD
Chou, KC [1 ]
机构
[1] Donghua Univ, Inst Informat, Shanghai 200051, Peoples R China
[2] Jing De Zhen Ceram Inst, Dept Comp, Jing De Zhen 33300, Peoples R China
[3] Gordon Life Sci Inst, San Diego, CA 92130 USA
关键词
pseudo amino acid composition; complexity measure factor; covariant-discriminant algorithm; invariance theorem;
D O I
10.1002/jcc.20354
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of the pseudo amino acid composition [Chou, K. C. Proteins Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60], a novel approach for measuring the complexity of a protein sequence was introduced. The advantage by incorporating the complexity measure factor into the pseudo amino acid composition as one of its components is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence-order effects. It was demonstrated thru the jackknife crossvalidation test that the overall success rate by the new approach was significantly higher than those by the others. It has not escaped our notice that the introduction of the complexity measure factor can also be used to improve the prediction quality for, among many other protein attributes, subcellular localization, enzyme family class, membrane protein type, and G-protein couple receptor type. (c) 2006 Wiley Periodicals, Inc.
引用
收藏
页码:478 / 482
页数:5
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