Identification of β-barrel membrane proteins based on amino acid composition properties and predicted secondary structure
Identification of β-barrel membrane proteins based on amino acid composition properties and predicted secondary structure
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DOI：
10.1016/s1476-9271(02)00085-3
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发表时间：
2003-07-01
影响因子：
3.1
通讯作者：
Li, YX
中科院分区：
文献类型：
--
作者：
Liu, Q;Zhu, YS;Li, YX
与全螺旋膜蛋白不同，β-桶膜蛋白不能成功地与其他蛋白，特别是与全β可溶性蛋白区分开。本文对12种β-桶膜蛋白和79种全β可溶性蛋白的β-链膜部分的氨基酸组成进行了分析。计算这两类中氨基酸组成的平均值和方差。基于Fisher判别比选择最可能与分类相关的氨基酸如Gly、Asn、瓦尔。在四重交叉验证实验中，用观察到的β链中的这些选定的氨基酸组成构建的线性分类器对于12种膜蛋白和79种可溶性蛋白实现了100%的分类准确度。针对目前二级结构预测精度较高的问题，提出了一种基于线性分类器结合二级结构预测的β-桶膜蛋白识别方法。应用于241种β-桶膜蛋白和3855种不同结构的可溶性蛋白，该方法的灵敏度为85.48%（206/241），特异性为92.53%（3567/3855）。(C)2002爱思唯尔科技有限公司版权所有。
Unlike all-helices membrane proteins, beta-barrel membrane proteins can not be successfully discriminated from other proteins, especially from all-beta soluble proteins. This paper performs an analysis on the amino acid composition in membrane parts of 12 beta-barrel membrane proteins versus beta-strands of 79 all-beta soluble proteins. The average and variance of the amino acid composition in these two classes are calculated. Amino acids such as Gly, Asn, Val that are most likely associated with classification are selected based on Fishers discriminant ratio. A linear classifier built with these selected amino acids composition in observed beta-strands achieves 100% classification accuracy for 12 membrane proteins and 79 soluble proteins in a four-fold cross-validation experiment. Since at present the accuracy of secondary structure prediction is quite high, a promising method to identify beta-barrel membrane proteins is presented based on the linear classifier coupled with predicted secondary structure. Applied to 241 beta-barrel membrane proteins and 3855 soluble proteins with various structures, the method achieves 85.48% (206/241) sensitivity and 92.53% specificity (3567/3855). (C) 2002 Elsevier Science Ltd. All rights reserved.