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Open Access Research article

Prediction of bacterial type IV secreted effectors by C-terminal features

Yejun Wang1*, Xiaowei Wei1, Hongxia Bao1 and Shu-Lin Liu12*

Author Affiliations

1 Genomics Research Center, Harbin Medical University, Harbin, China

2 Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, Canada

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BMC Genomics 2014, 15:50  doi:10.1186/1471-2164-15-50

Published: 21 January 2014

Abstract

Background

Many bacteria can deliver pathogenic proteins (effectors) through type IV secretion systems (T4SSs) to eukaryotic cytoplasm, causing host diseases. The inherent property, such as sequence diversity and global scattering throughout the whole genome, makes it a big challenge to effectively identify the full set of T4SS effectors. Therefore, an effective inter-species T4SS effector prediction tool is urgently needed to help discover new effectors in a variety of bacterial species, especially those with few known effectors, e.g., Helicobacter pylori.

Results

In this research, we first manually annotated a full list of validated T4SS effectors from different bacteria and then carefully compared their C-terminal sequential and position-specific amino acid compositions, possible motifs and structural features. Based on the observed features, we set up several models to automatically recognize T4SS effectors. Three of the models performed strikingly better than the others and T4SEpre_Joint had the best performance, which could distinguish the T4SS effectors from non-effectors with a 5-fold cross-validation sensitivity of 89% at a specificity of 97%, based on the training datasets. An inter-species cross prediction showed that T4SEpre_Joint could recall most known effectors from a variety of species. The inter-species prediction tool package, T4SEpre, was further used to predict new T4SS effectors from H. pylori, an important human pathogen associated with gastritis, ulcer and cancer. In total, 24 new highly possible H. pylori T4S effector genes were computationally identified.

Conclusions

We conclude that T4SEpre, as an effective inter-species T4SS effector prediction software package, will help find new pathogenic T4SS effectors efficiently in a variety of pathogenic bacteria.

Keywords:
Type IV secretion system; Effector; Secretion signal prediction; Sequence analysis; Machine learning; Helicobacter pylori