Proteomic analysis of the binding partners to enteropathogenic <b><i>Escherichia coli</i></b> virulence proteins expressed in <b><i>Saccharomyces cerevisiae</i></b>
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC) is an enteric human pathogen responsible for much worldwide morbidity and mortality. EPEC uses a type III secretion system to inject bacterial proteins into the cytosol of intestinal epithelial cells to cause diarrheal disease. We are interested in determining the host proteins to which EPEC translocator and effector proteins bind during infection. To facilitate protein enrichment, we created fusions between GST and EPEC virulence proteins, and expressed these fusions individually in Saccharomyces cerevisiae. The biology of S. cerevisiae is well understood and often employed as a model eukaryote to study the function of bacterial virulence factors. We isolated the yeast proteins that interact with individual EPEC proteins by affinity purifying against the GST tag. These complexes were subjected to ICAT combined with ESI-MS/MS. Database searching of sequenced peptides provided a list of proteins that bound specifically to each EPEC virulence protein. The dataset suggests several potential mammalian targets of these proteins that may guide future experimentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".