Universal Genetic Assay for Engineering Extracellular Protein Expression
Bibliographic record
Abstract
A variety of strategies now exist for the extracellular expression of recombinant proteins using laboratory strains of Escherichia coli . However, secreted proteins often accumulate in the culture medium at levels that are too low to be practically useful for most synthetic biology and metabolic engineering applications. The situation is compounded by the lack of generalized screening tools for optimizing the secretion process. To address this challenge, we developed a genetic approach for studying and engineering protein-secretion pathways in E. coli . Using the YebF pathway as a model, we demonstrate that direct fluorescent labeling of tetracysteine-motif-tagged secretory proteins with the biarsenical compound FlAsH is possible in situ without the need to recover the cell-free supernatant. High-throughput screening of a bacterial strain library yielded superior YebF expression hosts capable of secreting higher titers of YebF and YebF-fusion proteins into the culture medium. We also show that the method can be easily extended to other secretory pathways, including type II and type III secretion, directly in E. coli . Thus, our FlAsH-tetracysteine-based genetic assay provides a convenient, high-throughput tool that can be applied generally to diverse secretory pathways. This platform should help to shed light on poorly understood aspects of these processes as well as to further assist in the construction of engineered E. coli strains for efficient secretory-protein production.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".