Markov random fields reveal an N-terminal double beta-propeller motif as part of a bacterial hybrid two-component sensor system
Markov random fields reveal an N-terminal double beta-propeller motif as part of a bacterial hybrid two-component sensor system
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DOI：
10.1073/pnas.0909950107
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发表时间：
2010-03-02
影响因子：
11.1
通讯作者：
Cowen, Lenore
中科院分区：
文献类型：
--
作者：
Menke, Matt;Berger, Bonnie;Cowen, Lenore
最近新测序的细菌基因组的爆炸式增长超过了研究人员试图为所有新蛋白质分配功能注释的能力。因此，可以帮助预测结构基序的计算方法为帮助确定这些蛋白质的功能提供了越来越重要的线索。我们引入了一种马尔可夫随机场方法，专门用于识别折叠成主要是β结构基序的蛋白质，并将其应用于构建β螺旋桨形状的识别器。作为应用，我们确定了一类潜在的混合双组分传感器蛋白，我们预测它包含双螺旋桨结构域。
The recent explosion in newly sequenced bacterial genomes is outpacing the capacity of researchers to try to assign functional annotation to all the new proteins. Hence, computational methods that can help predict structural motifs provide increasingly important clues in helping to determine how these proteins might function. We introduce a Markov Random Field approach tailored for recognizing proteins that fold into mainly beta-structural motifs, and apply it to build recognizers for the beta-propeller shapes. As an application, we identify a potential class of hybrid two-component sensor proteins, that we predict contain a double-propeller domain.