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Sci Rep. 2016 Oct 7;6:34516. doi: 10.1038/srep34516.

Computational prediction shines light on type III secretion origins.

Author information

1
Department of Informatics, Bioinformatics &Computational Biology - I12, TUM, Garching, Germany.
2
Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM, Garching, Germany.
3
Institute for Advanced Study (TUM-IAS), Garching, Germany.
4
Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
5
Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ, USA.

Abstract

Type III secretion system is a key bacterial symbiosis and pathogenicity mechanism responsible for a variety of infectious diseases, ranging from food-borne illnesses to the bubonic plague. In many Gram-negative bacteria, the type III secretion system transports effector proteins into host cells, converting resources to bacterial advantage. Here we introduce a computational method that identifies type III effectors by combining homology-based inference with de novo predictions, reaching up to 3-fold higher performance than existing tools. Our work reveals that signals for recognition and transport of effectors are distributed over the entire protein sequence instead of being confined to the N-terminus, as was previously thought. Our scan of hundreds of prokaryotic genomes identified previously unknown effectors, suggesting that type III secretion may have evolved prior to the archaea/bacteria split. Crucially, our method performs well for short sequence fragments, facilitating evaluation of microbial communities and rapid identification of bacterial pathogenicity - no genome assembly required. pEffect and its data sets are available at http://services.bromberglab.org/peffect.

PMID:
27713481
PMCID:
PMC5054392
DOI:
10.1038/srep34516
[Indexed for MEDLINE]
Free PMC Article

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