ArticlePLoS computational biology2019
Searching algorithm for Type IV effector proteins (S4TE) 2.0: Improved tools for Type IV effector prediction, analysis and comparison in proteobacteria.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Article
- Adaptation ofmSphere · 2026Article
- Genome characteristics and type IV effector protein repertoire of Coxiella burnetii depend rather on Genomic Groups than on host species.BMC microbiology · 2026Article
- PLM-effector: unleashing the potential of protein language models for bacterial secreted protein prediction.Briefings in bioinformatics · 2026Article
- Comparative immunoproteomics ofFrontiers in cellular and infection microbiology · 2026Article
- Article
- Bacterial Puppeteering: How the Stealth BacteriumPathogens (Basel, Switzerland) · 2025Review
- Behavioral manipulation ofmBio · 2025Article
- ThemicroLife · 2025Review
- T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors.Computational and structural biotechnology journal · 2024Article
- Cell-selective proteomics reveal novel effectors secreted by an obligate intracellular bacterial pathogen.Nature communications · 2024Article
- The multifunctionProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Cell-selective proteomics reveal novel effectors secreted by an obligate intracellular bacterial pathogen.bioRxiv : the preprint server for biology · 2023Article
- The DNA-binding induced (de)AMPylation activity of a Coxiella burnetii Fic enzyme targets Histone H3.Communications biology · 2023Article
- Antibody reactive immunomes ofFrontiers in cellular and infection microbiology · 2023Article
- Analysis of the Type 4 Effectome across the GenusInternational journal of molecular sciences · 2022Article
- KaruBioNet: a network and discussion group for a better collaboration and structuring of bioinformatics in Guadeloupe (French West Indies).Bioinformatics advances · 2022Article
- Immunoreactive Protein Repertoires of Ehrlichia chaffeensis and E. canis Reveal the Dominance of Hypothetical Proteins and Conformation-Dependent Antibody Epitopes.Infection and immunity · 2021Article
- The super repertoire of type IV effectors in the pangenome of Ehrlichia spp. provides insights into host-specificity and pathogenesis.PLoS computational biology · 2021Article
- iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles.Molecules (Basel, Switzerland) · 2021Article
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3 authors.
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Abstract
Bacterial pathogens have evolved numerous strategies to corrupt, hijack or mimic cellular processes in order to survive and proliferate. Among those strategies, Type IV effectors (T4Es) are proteins secreted by pathogenic bacteria to manipulate host cell processes during infection. They are delivered into eukaryotic cells in an ATP-dependent manner via the type IV secretion system, a specialized multiprotein complex. T4Es contain a wide spectrum of features including eukaryotic-like domains, localization signals or a C-terminal translocation signal. A combination of these features enables prediction of T4Es in a given bacterial genome. In this study, we developed a web-based comprehensive suite of tools with a user-friendly graphical interface. This version 2.0 of S4TE (Searching Algorithm for Type IV Effector Proteins; http://sate.cirad.fr) enables accurate prediction and comparison of T4Es. Search parameters and threshold can be customized by the user to work with any genome sequence, whether publicly available or not. Applications range from characterizing effector features and identifying potential T4Es to analyzing the effectors based on the genome G+C composition and local gene density. S4TE 2.0 allows the comparison of putative T4E repertoires of up to four bacterial strains at the same time. The software identifies T4E orthologs among strains and provides a Venn diagram and lists of genes for each intersection. New interactive features offer the best visualization of the location of candidate T4Es and hyperlinks to NCBI and Pfam databases. S4TE 2.0 is designed to evolve rapidly with the publication of new experimentally validated T4Es, which will reinforce the predictive power of the algorithm. The computational methodology can be used to identify a wide spectrum of candidate bacterial effectors that lack sequence conservation but have similar amino acid characteristics. This approach will provide very valuable information about bacterial host-specificity and virulence factors and help identify host targets for the development of new anti-bacterial molecules.
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