Predicting protein structural class by functional domain composition

被引:171
作者
Chou, KC [1 ]
Cai, YD
机构
[1] Gordon Life Sci Inst, San Diego, CA 92130 USA
[2] TIBDD, Tianjin, Peoples R China
[3] UMIST, Biomol Sci Dept, Manchester M60 1QD, Lancs, England
关键词
sequence-order-related feature; function-related feature; less than 20% sequence identity; ISort predictor;
D O I
10.1016/j.bbrc.2004.07.059
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The functional domain composition is introduced to predict the structural class of a protein or domain according to the following classification: all-alpha, all-beta, alpha/beta, alpha + beta, mu (multi-domain), sigma (small protein), and rho (peptide). The advantage by doing so is that both the sequence-order-related features and the function-related features are naturally incorporated in the predictor. As a demonstration, the jackknife cross-validation test was performed on a dataset that consists of proteins and domains with only less than 20% sequence identity to each other in order to get rid of any homologous bias. The overall success rate thus obtained was 98%. In contrast to this, the corresponding rates obtained by the simple geometry approaches based on the amino acid composition were only 36-39%. This indicates that using the functional domain composition to represent the sample of a protein for statistical prediction is very promising.. and that the functional type of a domain is closely correlated with its structural class. (C) 2004 Elsevier Inc. All rights reserved.
引用
收藏
页码:1007 / 1009
页数:3
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