English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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