Genome-wide identification of <i>Pseudomonas aeruginosa</i> exported proteins using a consensus computational strategy combined with a laboratory-based PhoA fusion screen
Bibliographic record
Abstract
The Gram-negative pathogen Pseudomonas aeruginosa encodes multiple protein export systems, the substrates of which contain export signals such as N-terminal signal peptides. Here we report the first genome-wide computational and laboratory screen for N-terminal signal peptides in this important opportunistic pathogen. The computational identification of signal peptides was based on a consensus between multiple predictive tools and showed that 38% of the P. aeruginosa PAO1 proteome was predicted to encode exported proteins, most of which utilize cleavable type I signal peptides or uncleavable transmembrane helices. In addition, known and novel lipoproteins (type II), twin arginine transporter (TAT), and prepilin peptidase substrates (type IV) were also identified. A laboratory-based screen using the alkaline phosphatase (PhoA) fusion method was then used to test our predictions. In total, 310 nonredundant PhoA fusions were successfully identified, 296 of which possess a predicted export signal. Analysis of the PhoA fusion proteins lacking an export signal revealed that three proteins have alternate translation start sites that encode signal peptides, two proteins may use an unknown export signal, and the remaining nine proteins are likely cytoplasmic proteins and represent false positives associated with the PhoA screen. Our approach to identify exported proteins illustrates how computational and laboratory-based methods are complementary, where computational analyses provide a large number of accurate predictions while laboratory methods both confirm predictions and reveal unique cases meriting further analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".