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Author: Tatyana Goldberg
Research area: bioinformatics

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Computational prediction shines light on type III secretion origins

Created on 10th January 2016

Tatyana Goldberg; Burkhard Rost; Yana Bromberg;

The type III secretion system transports effector proteins of pathogenic and endosymbiotic Gram-negative bacteria into the cytoplasm of host cells. During infection, effectors convert host resources to work to bacterial advantage. Existing computational methods for the prediction of type III effectors mainly employ information encoded in the N-terminal protein sequence. Here we introduce pEffect, a method that predicts type III effector proteins using the entire amino acid sequence. It combines homology-based inference with de novo predictions, reaching 87+-7% accuracy at 95+-5% coverage for a large non-redundant set of proteins. This performance is up to 3-fold higher than that of other methods. pEffect also sheds new light on effector secretion mechanisms. We establish that 'signals' for the recognition of type III effectors are distributed over the entire protein sequence instead of being confined to the N-terminus. Our method, therefore, maintains high performance even when used with sequence fragments like metagenomic reads, and potentially facilitates studies of microbial community interactions. Explorations into the evolutionary origins of type III secretion identify a variety of recently evolved effectors and highlight the possibility of type III secretion ancestor dating to times prior to the archaea/bacteria split. pEffect is available at

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