Tufts University, Medford, MA 02155, USA.
Proc Natl Acad Sci U S A. 2010 Mar 2;107(9):4069-74. doi: 10.1073/pnas.0909950107. Epub 2010 Feb 10.
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.
最近新测序的细菌基因组数量呈爆炸式增长，超过了研究人员尝试为所有新蛋白质赋予功能注释的能力。因此，能够帮助预测结构基序的计算方法在帮助确定这些蛋白质可能的功能方面提供了越来越重要的线索。我们引入了一种针对主要折叠成β结构基序的蛋白质的马尔可夫随机场方法，并将其应用于构建β-类桨叶形状的识别器。作为一种应用，我们确定了一类潜在的混合双组分传感器蛋白，我们预测这些蛋白含有双桨叶结构域。
