ReviewInfection and immunity2011
Computational prediction of type III and IV secreted effectors in gram-negative bacteria.
Review in Infection and immunity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
68 citing papers in PubMed.
- Article
- Novel insights into the genetic basis and transcriptomic elucidation of virulence attenuation inVirulence · 2025Article
- Systematic Discovery of Pathogen Effector Functions across Human Pathogens and Pathways.bioRxiv : the preprint server for biology · 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
- A Novel Effector, FSE1, Regulates the Pathogenicity ofJournal of fungi (Basel, Switzerland) · 2023Article
- Synthetic biology-inspired cell engineering in diagnosis, treatment, and drug development.Signal transduction and targeted therapy · 2023Review
- The Missing Pieces: The Role of Secretion Systems inBiomolecules · 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
- Type III secretion by Yersinia pseudotuberculosis is reliant upon an authentic N-terminal YscX secretor domain.Molecular microbiology · 2022Article
- DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework.NAR genomics and bioinformatics · 2021Article
- Transmembrane domains of type III-secreted proteins affect bacterial-host interactions in enteropathogenicVirulence · 2021Article
- Substrate-engaged type III secretion system structures reveal gating mechanism for unfolded protein translocation.Nature communications · 2021Article
- Keeping in Touch with Type-III Secretion System Effectors: Mass Spectrometry-Based Proteomics to Study Effector-Host Protein-Protein Interactions.International journal of molecular sciences · 2020Review
- Article
- Article
- Subcellular Location ofMicroorganisms · 2020Article
- Advances and Challenges in Studying Type III Secretion Effectors of Attaching and Effacing Pathogens.Frontiers in cellular and infection microbiology · 2020Article
8 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
10 authors.
Funding
Abstract
In this review, we provide an overview of the methods employed in four recent studies that described novel methods for computational prediction of secreted effectors from type III and IV secretion systems in Gram-negative bacteria. We present the results of these studies in terms of performance at accurately predicting secreted effectors and similarities found between secretion signals that may reflect biologically relevant features for recognition. We discuss the Web-based tools for secreted effector prediction described in these studies and announce the availability of our tool, the SIEVE server (http://www.sysbep.org/sieve). Finally, we assess the accuracies of the three type III effector prediction methods on a small set of proteins not known prior to the development of these tools that we recently discovered and validated using both experimental and computational approaches. Our comparison shows that all methods use similar approaches and, in general, arrive at similar conclusions. We discuss the possibility of an order-dependent motif in the secretion signal, which was a point of disagreement in the studies. Our results show that there may be classes of effectors in which the signal has a loosely defined motif and others in which secretion is dependent only on compositional biases. Computational prediction of secreted effectors from protein sequences represents an important step toward better understanding the interaction between pathogens and hosts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.