Identification of secreted and surface proteins from <i>Enterococcus faecalis</i>
Bibliographic record
Abstract
Secreted and surface proteins of bacteria are key molecules that interface the cell with the environment. Some of them, corresponding to virulence factors, have already been described for Enterococcus faecalis, the predominant species involved in enterococcal nosocomial infections. In a global proteomic approach, the identification of the most abundant secreted and surface-associated proteins of E. faecalis JH2-2 strain was carried out. These proteins were separated by gel electrophoresis or directly subjected to in vivo trypsinolysis and then analyzed by liquid chromatography - electrospray ion trap tandem mass spectrometry. Putative functions were assigned by homology to the translated genomic database of E. faecalis. A total of 44 proteins were identified, eight secreted proteins from the supernatant culture and 38 cell surface proteins from two-dimensional gel electrophoresis and in vivo trypsinolysis among which two are common to the two groups. Their sequences analysis revealed that 35 of the 44 proteins harbour characteristic features (signal peptide or transmembrane domains) consistent with an extracellular localization. This study may be considered as an important step to encourage proteomic-based investigations of E. faecalis cell surface associated proteins that could lead to the discovery of virulence factors and to the development of new therapeutic tools.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".