Identification of Streptomyces lividans proteins secreted by the twin-arginine translocation pathway following growth with different carbon sources
Bibliographic record
Abstract
Genome-based signal peptide predictions classified Streptomyces coelicolor as the microorganism that secretes the most proteins through the twin-arginine translocation (Tat)-dependent secretion pathway. Availability of a DeltatatC mutant of the closely related strain Streptomyces lividans impaired Tat-dependent protein secretion and enabled identification of many extracellular proteins that are secreted via the Tat pathway. Proteomic techniques were applied to analyze proteins from the supernatants of log-phase cultures. Since the bacterial secretome depends mainly on the carbon sources available during growth, xylose, glucose, chitin, and soil extracts were used. A total of 63 proteins were identified, among which 7 were predicted by the TATscan program, and 20 were not predicted but contained a potential Tat signal motif. Thirteen proteins having no signal sequence could be co-transported by Tat-dependent proteins because the genes that encode these proteins are in close proximity in the genome. Finally, the presence of 23 proteins lacking signal peptides was difficult to explain. More secreted proteins could be identified as Tat substrates in varying carbon sources.
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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) | 0.000 | 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".