Outer membrane proteome of <b><i>Actinobacillus pleuropneumoniae</i></b>: LC‐MS/MS analyses validate <b><i>in silico</i></b> predictions
Bibliographic record
Abstract
The Gram-negative bacterial pathogen Actinobacillus pleuropneumoniae causes porcine pneumonia, a highly infectious respiratory disease that contributes to major economic losses in the swine industry. Outer membrane (OM) proteins play key roles in infection and may be targets for drug and vaccine research. Exploiting the genome sequence of A. pleuropneumoniae serotype 5b, we scanned in silico for proteins predicted to be localized at the cell surface. Five genome scanning programs (Proteome Analyst, PSORT-b, BOMP, Lipo, and LipoP) were run to construct a consensus prediction list of 93 OM proteins in A. pleuropneumoniae. An inventory of predicted OM proteins was complemented by proteomic analyses utilizing gel- and solution-based methods, both coupled to LC-MS/MS. Different protocols were explored to enrich for OM proteins; the most rewarding required sucrose gradient centrifugation followed by membrane washes with sodium bromide and sodium carbonate. This protocol facilitated our identification of 47 OM proteins that represent 50% of the predicted OM proteome, most of which have not been characterized. Our study establishes the first OM proteome of A. pleuropneumoniae.
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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".