Surface display of proteins by gram-negative bacterial autotransporters.
Bibliographic record
Abstract
Expressing proteins of interest as fusions to proteins of the bacterial envelope is a powerful technique with many biotechnological and medical applications. Autotransporters have recently emerged as a good tool for bacterial surface display. These proteins are composed of an N-terminal signal peptide, followed by a passenger domain and a translocator domain that mediates the outer membrane translocation of the passenger. The natural passenger domain of autotransporters can be replaced by heterologous proteins that become displayed at the bacterial surface by the translocator domain. The simplicity and versatility of this system has made it very attractive and it has been used to display functional enzymes, vaccine antigens as well as polypeptides libraries. The recent advances in the study of the translocation mechanism of autotransporters have raised several controversial issues with implications for their use as display systems. These issues include the requirement for the displayed polypeptides to remain in a translocation-competent state in the periplasm, the requirement for specific signal sequences and "autochaperone" domains, and the influence of the genetic background of the expression host strain. It is therefore important to better understand the mechanism of translocation of autotransporters in order to employ them to their full potential. This review will focus on the recent advances in the study of the translocation mechanism of autotransporters and describe practical considerations regarding their use for bacterial surface display.
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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".