ArticleBMC genomics2014
Prediction of bacterial type IV secreted effectors by C-terminal features.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 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
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- Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.Frontiers in chemistry · 2026Review
- T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors.Computational and structural biotechnology journal · 2024Article
- T4Seeker: a hybrid model for type IV secretion effectors identification.BMC biology · 2024Article
- The impact ofJournal of genomics · 2024Article
- Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
- DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria.Research (Washington, D.C.) · 2023Article
- Recruitment of heterologous substrates by bacterial secretion systems for transkingdom translocation.Frontiers in cellular and infection microbiology · 2023Review
- PreAcrs: a machine learning framework for identifying anti-CRISPR proteins.BMC bioinformatics · 2022Article
- Recent Advancements in Tracking Bacterial Effector Protein Translocation.Microorganisms · 2022Review
- Modular evolution of secretion systems and virulence plasmids in a bacterial species complex.BMC biology · 2022Article
- Article
- iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles.Molecules (Basel, Switzerland) · 2021Article
- DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors.Frontiers in microbiology · 2021Article
- T1SEstacker: A Tri-Layer Stacking Model Effectively Predicts Bacterial Type 1 Secreted Proteins Based on C-Terminal Non-repeats-in-Toxin-Motif Sequence Features.Frontiers in microbiology · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- Subcellular Location ofMicroorganisms · 2020Article
- Integrative and Conjugative Elements ofFrontiers in cellular and infection microbiology · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany 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.
resultsIn 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.
conclusionsWe 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.
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