Essential functions of chaperones and adaptors of protein secretion systems in Gram‐negative bacteria
Bibliographic record
Abstract
Equipped with a plethora of secreted toxic effectors, protein secretion systems are essential for bacteria to interact with and manipulate their neighboring environment to survive in host microbiota and other highly competitive communities. While effectors have received spotlight attention in secretion system studies, many require accessory chaperone and adaptor proteins for proper folding/unfolding and stability throughout the secretion process. Here, we review the functions of chaperones and adaptors of three protein secretions systems, type 3 secretion system (T3SS), type 4 secretion system (T4SS), and type 6 secretion system (T6SS), which are employed by many Gram-negative bacterial pathogens to deliver toxins to bacterial, plant, and mammalian host cells through direct contact. Since chaperone and adaptor functions of the T3SS and the T4SS are relatively well studied, we discuss in detail the methods of chaperone-facilitated effector secretion by the T6SS and highlight commonalities between the effector chaperone/adaptor proteins of these diverse secretion systems. While the chaperones and adaptors are generally referred to as accessory proteins as they are not directly involved in toxicities to target cells, they are nonetheless vital for the biological functions of the secretion systems. Future research on biochemical and structural properties of these chaperones will not only elucidate the mechanisms of chaperone-effector binding and release process but also facilitate custom design of cargo effectors to be translocated by these widespread secretion systems for biotechnological applications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".