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Phylogenetic profiling, an untapped resource for the prediction of secreted proteins and its complementation with sequence-based classifiers in bacterial type III, IV and VI secretion systems

机译:系统发育分析,预测分泌蛋白质的未开发资源及其与细菌III,IV和VI分泌系统中的序列基分类剂的互补

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摘要

In the establishment and maintenance of the interaction between pathogenic or symbiotic bacteria with a eukaryotic organism, protein substrates of specialized bacterial secretion systems called effectors play a critical role once translocated into the host cell. Proteins are also secreted to the extracellular medium by free-living bacteria or directly injected into other competing organisms to hinder or kill. In this work, we explore an approach based on the evolutionary dependence that most of the effectors maintain with their specific secretion system that analyzes the co-occurrence of any orthologous protein group and their corresponding secretion system across multiple genomes. We compared and complemented our methodology with sequence-based machine learning prediction tools for the type III, IV and VI secretion systems. Finally, we provide the predictive results for the three secretion systems in 1606 complete genomes at http://www.iib.unsam.edu. ar/orgsissec/.
机译:在具有真核生物的致病或共生细菌之间的相互作用的建立和维护,称为效应子的专用细菌分泌系统的蛋白质基质起到一旦转移到宿主细胞中就发挥着关键作用。 蛋白质也通过自由生物细菌分泌到细胞外培养基中,或者直接注射到其他竞争生物中以阻碍或杀死。 在这项工作中,我们基于进化依赖性探索一种方法,即大多数效果与其特定的分泌系统维持,分析了跨多种基因组的任何正交蛋白质组及其相应的分泌系统的共同发生。 我们对III型,IV和VI分泌系统进行了比较和与基于序列的机器学习预测工具的方法进行了比较和补充。 最后,我们在http://www.iib.unsam.edu的1606个完整基因组中为三种分泌系统提供了预测结果。 AR / ORGSISSEC /。

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