The Twin-arginine Leader-binding Protein, DmsD, Interacts with the TatB and TatC Subunits of the Escherichia coli Twin-arginine Translocase
Bibliographic record
Abstract
The twin-arginine translocase (Tat) pathway is involved in the targeting and translocation of fully folded proteins to the inner membrane and periplasm of bacteria. Proteins that use this pathway contain a characteristic twin-arginine signal sequence, which interacts with the receptor complex formed by the TatBC subunits. Recently, the DmsD protein was discovered, which binds to the twin-arginine signal sequences of the anaerobic respiratory enzymes dimethylsulfoxide reductase (DmsABC) and trimethylamine N-oxide (TMAO) reductase. In this work, the targeting of DmsD within Escherichia coli was investigated. Using cell fractionation and Western blot analysis, DmsD is found to be associated with the inner membrane of wild-type E. coli and a dmsABC mutant E. coli under anaerobic conditions. In contrast, DmsD is predominantly found in the cytoplasmic fraction of a Delta tatABCDE strain, which suggests that DmsD interacts with the membrane-associated Tat complex. Under aerobic conditions DmsD was also found primarily in the cytoplasmic fraction of wild-type E. coli, suggesting that physiological conditions have a significant effect upon the targeting of DmsD to the inner membrane. Size exclusion chromatography data and membrane washing studies indicate that DmsD is interacting tightly with an integral membrane protein and not with the lipid component of the E. coli inner membrane. Additional investigation into the nature of this interaction revealed that the TatB and TatC subunits of the translocase are important for the interaction of DmsD with the E. coli inner membrane.
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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 | 0.001 |
| 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".