Bioinformatics Unit, Institut Pasteur Montevideo, 11400 Montevideo, Uruguay.
Bioinformatics. 2011 Oct 15;27(20):2782-9. doi: 10.1093/bioinformatics/btr476. Epub 2011 Aug 16.
We present a method that identifies associations between amino acid changes in potentially significant sites in an alignment (taking into account several amino acid properties) with phenotypic data, through the phylogenetic mixed model. The latter accounts for the dependency of the observations (organisms). It is known from previous studies that the pathogenic aspect of many organisms may be associated with a single or just few changes in amino acids, which have a strong structural and/or functional impact on the protein. Discovering these sites is a big step toward understanding pathogenicity. Our method is able to discover such sites in proteins responsible for the pathogenic character of a group of bacteria.
We use our method to predict potentially significant sites in the RpoS protein from a set of 209 bacteria. Several sites with significant differences in biological relevant regions were found.
Our tool is publicly available on the CRAN network at http://cran.r-project.org/
Supplementary data are available at Bioinformatics online.
我们提出了一种方法，通过系统发育混合模型，将对齐中潜在重要位置的氨基酸变化（考虑到几种氨基酸特性）与表型数据联系起来。后者解释了观测结果（生物体）的依赖性。从之前的研究中已知，许多生物体的致病性可能与单个或少数氨基酸的变化有关，这些变化对蛋白质的结构和/或功能有很强的影响。发现这些位点是理解致病性的重要一步。我们的方法能够发现负责一组细菌致病性特征的蛋白质中的这些位点。
我们使用该方法来预测来自 209 个细菌的 RpoS 蛋白中的潜在重要位点。在生物相关区域中发现了几个具有显著差异的位点。
我们的工具可在 CRAN 网络上公开获得，网址为 http://cran.r-project.org/。
补充数据可在 Bioinformatics 在线获得。
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