ArticleScientific reports2016
Computational prediction shines light on type III secretion origins.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Effector-host interactome map links type III secretion systems in healthy gut microbiomes to immune modulation.Nature microbiology · 2026Article
- Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.Frontiers in chemistry · 2026Review
- Article
- Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction.Nature communications · 2025Article
- T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors.Computational and structural biotechnology journal · 2024Article
- Molecular and Genomic Characterization of theMicroorganisms · 2022Article
- Genomic and Functional Dissections of Dickeya zeae Shed Light on the Role of Type III Secretion System and Cell Wall-Degrading Enzymes to Host Range and Virulence.Microbiology spectrum · 2022Article
- Natural language processing approach to model the secretion signal of type III effectors.Frontiers in plant science · 2022Article
- DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework.NAR genomics and bioinformatics · 2021Article
- Genome analysis provides insights into the biocontrol ability of Mitsuaria sp. strain TWR114.Archives of microbiology · 2021Article
- Embeddings from deep learning transfer GO annotations beyond homology.Scientific reports · 2021Article
- DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors.Frontiers in microbiology · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2021Article
- Finding orthologous gene blocks in bacteria: the computational hardness of the problem and novel methods to address it.Bioinformatics (Oxford, England) · 2020Article
- Article
- Identification and characterization of diverse OTU deubiquitinases in bacteria.The EMBO journal · 2020Article
- Subcellular Location ofMicroorganisms · 2020Article
- Bastion3: a two-layer ensemble predictor of type III secreted effectors.Bioinformatics (Oxford, England) · 2019Article
- HrpE, the major component of the Xanthomonas type three protein secretion pilus, elicits plant immunity responses.Scientific reports · 2018Article
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3 authors.
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Abstract
Type III secretion system is a key bacterial symbiosis and pathogenicity mechanism responsible for a variety of infectious diseases, ranging from food-borne illnesses to the bubonic plague. In many Gram-negative bacteria, the type III secretion system transports effector proteins into host cells, converting resources to bacterial advantage. Here we introduce a computational method that identifies type III effectors by combining homology-based inference with de novo predictions, reaching up to 3-fold higher performance than existing tools. Our work reveals that signals for recognition and transport of effectors are distributed over the entire protein sequence instead of being confined to the N-terminus, as was previously thought. Our scan of hundreds of prokaryotic genomes identified previously unknown effectors, suggesting that type III secretion may have evolved prior to the archaea/bacteria split. Crucially, our method performs well for short sequence fragments, facilitating evaluation of microbial communities and rapid identification of bacterial pathogenicity - no genome assembly required. pEffect and its data sets are available at http://services.bromberglab.org/peffect.
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