Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels.
Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels.
复制标题
调节电压门控钾通道电压敏感性的残基的计算鉴定。
DOI：
10.1186/1472-6807-5-16
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发表时间：
2005-08-19
影响因子：
--
通讯作者：
Gallin WJ
中科院分区：
文献类型：
--
作者：
Li B;Gallin WJ
蛋白质的结构-功能关系的研究，对于没有三维结构是可用的，往往是基于多个序列比对的检查。蛋白质的许多功能上重要的残基可以被鉴定，因为它们在进化过程中是保守的。然而，如果残基的变异是蛋白质功能多样性和表型改善的原因，那么变异的残基也是至关重要的。如果研究的序列太少，对给定残基作用的假设的支持将很弱，但是对大的多重比对的分析对于简单的检查来说太复杂了。当一个蛋白质家族有大量的序列和功能数据时，成熟的数据挖掘工具，如机器学习，可以更容易、更灵敏、更可靠地提取信息。我们已经进行了这样的电压门控钾通道，跨膜蛋白家族的成员在电兴奋细胞中发挥不可或缺的作用的分析。我们应用了不同的学习算法，结合在各种实现中，以获得一个模型，该模型基于其氨基酸序列预测电压门控钾通道的半激活电压。最好的结果是使用k-最近邻分类器结合包装算法进行特征选择，预测的平均绝对误差为7.0 mV。通过排列检验和独立实验数据的评价对预测器进行了验证。特征选择鉴定了许多预测参与电压敏感性构象变化的残基;这些残基是诱变分析的良好靶候选物。机器学习分析可以识别关于电压门控钾通道家族中结构/功能关系的新的可测试假设。这种方法应该适用于任何蛋白质家族，如果训练样本的数量和训练集的序列多样性是必要的鲁棒预测经验验证。预测器和数据集可以在VKCDB网站上找到。
Studies of the structure-function relationship in proteins for which no 3D structure is available are often based on inspection of multiple sequence alignments. Many functionally important residues of proteins can be identified because they are conserved during evolution. However, residues that vary can also be critically important if their variation is responsible for diversity of protein function and improved phenotypes. If too few sequences are studied, the support for hypotheses on the role of a given residue will be weak, but analysis of large multiple alignments is too complex for simple inspection. When a large body of sequence and functional data are available for a protein family, mature data mining tools, such as machine learning, can be applied to extract information more easily, sensitively and reliably. We have undertaken such an analysis of voltage-gated potassium channels, a transmembrane protein family whose members play indispensable roles in electrically excitable cells. We applied different learning algorithms, combined in various implementations, to obtain a model that predicts the half activation voltage of a voltage-gated potassium channel based on its amino acid sequence. The best result was obtained with a k-nearest neighbor classifier combined with a wrapper algorithm for feature selection, producing a mean absolute error of prediction of 7.0 mV. The predictor was validated by permutation test and evaluation of independent experimental data. Feature selection identified a number of residues that are predicted to be involved in the voltage sensitive conformation changes; these residues are good target candidates for mutagenesis analysis. Machine learning analysis can identify new testable hypotheses about the structure/function relationship in the voltage-gated potassium channel family. This approach should be applicable to any protein family if the number of training examples and the sequence diversity of the training set that are necessary for robust prediction are empirically validated. The predictor and datasets can be found at the VKCDB web site.
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DOI：
10.1073/pnas.89.22.10915
发表时间：
1992-11-15
影响因子：
11.1
作者：
HENIKOFF, S;HENIKOFF, JG
通讯作者：
HENIKOFF, JG
影响因子：
56.9
作者：
Doyle, DA;Cabral, JM;MacKinnon, R
通讯作者：
MacKinnon, R
影响因子：
64.8
作者：
Jiang, YX;Ruta, V;MacKinnon, R
通讯作者：
MacKinnon, R
影响因子：
5.6
作者：
Fleishman, SJ;Yifrach, O;Ben-Tal, N
通讯作者：
Ben-Tal, N
影响因子：
5.8
作者：
de Lichtenberg, U;Jensen, LJ;Brunak, S
通讯作者：
Brunak, S