ArticlePLoS pathogens2009
Sequence-based prediction of type III secreted proteins.
Article in PLoS pathogens, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 162 papers, 1 of them a synthesis that pooled it.
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Who cites it
162 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.Briefings in bioinformatics · 2019Pooled it
- Article
- Evolution of N-terminal mechanical lability as a determinant for Type III secretion.Protein science : a publication of the Protein Society · 2026Article
- Comparative genomic analysis of Herbaspirillum strains associated with banana sheath rot reveals potential virulence factors.BMC genomics · 2026Article
- Genomic Basis of Lifestyle Divergence in Rice-AssociatedInternational journal of molecular sciences · 2026Article
- PLM-effector: unleashing the potential of protein language models for bacterial secreted protein prediction.Briefings in bioinformatics · 2026Article
- Effector-host interactome map links type III secretion systems in healthy gut microbiomes to immune modulation.Nature microbiology · 2026Article
- A Type III secretion system effector evolved to be mechanically labile and initiate unfolding from the N-terminus.bioRxiv : the preprint server for biology · 2026Article
- Genomic characterization and niche adaptive analysis ofFrontiers in cellular and infection microbiology · 2026Article
- Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.Frontiers in chemistry · 2026Review
- Systematic Discovery of Pathogen Effector Functions across Human Pathogens and Pathways.bioRxiv : the preprint server for biology · 2025Article
- A survey of ADP-ribosyltransferase families in the pathogenic Legionella.BMC genomics · 2025Article
- Toxin data quality: a critical examination of bacterial exotoxins and animal toxins.BMC research notes · 2025Article
- Identification and Pathogenicity Analysis ofMicroorganisms · 2025Article
- A conservedMicrobiology (Reading, England) · 2025Article
- The Bacterial Type III Secretion System as a Broadly Applied Protein Delivery Tool in Biological Sciences.Microorganisms · 2025Review
- Artificial Intelligence Methods in Infection Biology Research.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Definition of the effector landscape across 13 phytoplasma proteomes with LEAPH and EffectorComb.NAR genomics and bioinformatics · 2024Article
- Characterization and the first complete genome sequence of a novel strain of Bergeyella porcorum isolated from pigs in China.BMC microbiology · 2024Article
- Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
102 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of approximately 71% and selectivity of approximately 85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will facilitate further studies on and improve our understanding of type III secretion and its role in pathogen-host interactions.
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