Computational prediction shines light on type III secretion origins.

Tatyana Goldberg,Burkhard Rost,Yana Bromberg

doi:10.1038/srep34516

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.

Highlights

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
Considering the central role these proteins play in pathogenicity and symbiosis, there is a need for computational tools that predict and prioritize type III effector proteins
Our method provides a basis for rapid identification of T3SS-utitlizing bacteria and their exported effector proteins as targets for future therapeutic treatments

Summary

Introduction

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. Considering the central role these proteins play in pathogenicity and symbiosis, there is a need for computational tools that predict and prioritize type III effector proteins. To address this need various machine-learning algorithms[12,13,14,15] have been developed to identify type III effectors in silico As input, these methods use similarities in gene GC content and protein amino acid composition, secondary structure, and solvent accessibility to experimentally known effectors. When tested on sequence fragments similar in length to peptides translated from shotgun sequencing reads, pEffect’s performance was not significantly different This result suggests that the information required for distinguishing effectors is not confined to any particular part of the amino acid sequence. PEffect’s high prediction accuracy and ubiquitous applicability raises an interesting question about its predictions of effectors in Gram-positive bacteria and archaea, which are not known to utilize type III secretion. Gene genealogies[20] and protein network www.nature.com/scientificreports/

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