4.6 Article

A Bioinformatic Strategy for the Detection, Classification and Analysis of Bacterial Autotransporters

期刊

PLOS ONE
卷 7, 期 8, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0043245

关键词

-

资金

  1. National Health & Medical Research Council (NHMRC) [606788]
  2. Australian Research Council (ARC) [FS110200015]
  3. Australian Research Council [FS110200015] Funding Source: Australian Research Council

向作者/读者索取更多资源

Autotransporters are secreted proteins that are assembled into the outer membrane of bacterial cells. The passenger domains of autotransporters are crucial for bacterial pathogenesis, with some remaining attached to the bacterial surface while others are released by proteolysis. An enigma remains as to whether autotransporters should be considered a class of secretion system, or simply a class of substrate with peculiar requirements for their secretion. We sought to establish a sensitive search protocol that could identify and characterize diverse autotransporters from bacterial genome sequence data. The new sequence analysis pipeline identified more than 1500 autotransporter sequences from diverse bacteria, including numerous species of Chlamydiales and Fusobacteria as well as all classes of Proteobacteria. Interrogation of the proteins revealed that there are numerous classes of passenger domains beyond the known proteases, adhesins and esterases. In addition the barrel-domain-a characteristic feature of autotransporters-was found to be composed from seven conserved sequence segments that can be arranged in multiple ways in the tertiary structure of the assembled autotransporter. One of these conserved motifs overlays the targeting information required for autotransporters to reach the outer membrane. Another conserved and diagnostic motif maps to the linker region between the passenger domain and barrel-domain, indicating it as an important feature in the assembly of autotransporters.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据