ArticlePLoS pathogens2009
Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems.
Article in PLoS pathogens, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers.
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Who cites it
123 citing papers in PubMed.
- PLM-effector: unleashing the potential of protein language models for bacterial secreted protein prediction.Briefings in bioinformatics · 2026Article
- Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.Frontiers in chemistry · 2026Review
- Cyclic-di-GMP controls Type III effector export and symptom development in Pseudomonas syringae infections via the export ATPase HrcN.PLoS pathogens · 2025Article
- Cell-selective proteomics reveal novel effectors secreted by an obligate intracellular bacterial pathogen.Nature communications · 2024Article
- Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Cell-selective proteomics reveal novel effectors secreted by an obligate intracellular bacterial pathogen.bioRxiv : the preprint server for biology · 2023Article
- Type III Secretion inMicrobiology and molecular biology reviews : MMBR · 2023Review
- Virulence-associated type III secretion systems in Gram-negative bacteria.Microbiology (Reading, England) · 2023Review
- Recruitment of heterologous substrates by bacterial secretion systems for transkingdom translocation.Frontiers in cellular and infection microbiology · 2023Review
- Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding.Bioinformatics advances · 2023Article
- Recognition of a translocation motif in the regulator HpaA fromFrontiers in plant science · 2022Article
- Natural language processing approach to model the secretion signal of type III effectors.Frontiers in plant science · 2022Article
- Transmembrane domains of type III-secreted proteins affect bacterial-host interactions in enteropathogenicVirulence · 2021Article
- DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework.NAR genomics and bioinformatics · 2021Article
- Application of aJournal of bacteriology · 2021Article
- CwlQ Is Required for Swarming Motility but Not Flagellar Assembly in Bacillus subtilis.Journal of bacteriology · 2021Article
- iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- Review
- Interesting Biochemistries in the Structure and Function of Bacterial Effectors.Frontiers in cellular and infection microbiology · 2021Review
63 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates--effector proteins--are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.
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Registered trials
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