Functional discrimination of membrane proteins using machine learning techniques.
Functional discrimination of membrane proteins using machine learning techniques.
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DOI：
10.1186/1471-2105-9-135
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发表时间：
2008-03-03
影响因子：
3
通讯作者：
Yabuki Y
中科院分区：
文献类型：
--
作者：
Gromiha MM;Yabuki Y
基于功能的膜蛋白鉴别是基因组注释中的一项重要任务。在这项工作中，我们已经分析了氨基酸残基的特性功能的膜蛋白，执行主要功能，如通道/孔，电化学电位驱动的转运蛋白和主要的主动转运蛋白。我们观察到残基Asp、Asn和Tyr在通道/孔中占主导地位，而疏水残基Phe、Gly、Ile、Leu和瓦尔的组成在电化学电位驱动的转运蛋白中较高。初级主动转运蛋白中所有氨基酸的组成介于其他两类蛋白质之间。我们利用不同的机器学习算法，如贝叶斯规则，逻辑函数，神经网络，支持向量机，决策树等来区分这些类别的蛋白质。我们观察到，大多数算法都以相似的准确度区分它们。神经网络方法区分通道/孔，电化学电位驱动的转运蛋白和主动转运蛋白的5倍交叉验证的准确率为64%，在1718个膜蛋白的数据集。氨基酸出现率的应用将整体准确率提高到68%。此外，我们还将转运蛋白与其他α螺旋和β桶膜蛋白进行了k-近邻法鉴别，准确率为85%。转运蛋白和所有其他蛋白（球状和膜）的分类显示82%的准确性。氨基酸出现率的判别性能优于氨基酸组成的判别性能。我们认为，这种方法可以有效地用于区分转运蛋白从所有其他球状和膜蛋白，并将它们分为通道/孔，电化学和主动转运蛋白。
Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters. We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classification of transporters and all other proteins (globular and membrane) showed the accuracy of 82%. The performance of discrimination with amino acid occurrence is better than that with amino acid composition. We suggest that this method could be effectively used to discriminate transporters from all other globular and membrane proteins, and classify them into channels/pores, electrochemical and active transporters.
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影响因子：
14.9
作者：
Ren Q;Chen K;Paulsen IT
通讯作者：
Paulsen IT
影响因子：
5.8
作者：
Gromiha, MM;Suwa, M
通讯作者：
Suwa, M
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作者：
Dutzler, R;Campbell, EB;MacKinnon, R
通讯作者：
MacKinnon, R
影响因子：
3
作者：
Taguchi, Y-h;Gromiha, M. Michael
通讯作者：
Gromiha, M. Michael
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作者：
Natt, NK;Kaur, H;Raghava, GPS
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