A selection of Golden Gate vectors to simplify recombinant protein production in <i>Escherichia coli</i>
Bibliographic record
Abstract
Summary In this work multiple plasmids have been created to allow the simple Golden Gate cloning of a target gene for recombinant protein production in Escherichia coli . To simplify as much as possible the generation of different target gene vector combinations, the 22 plasmids contain the same Golden Gate cloning sites ( Bsa I), antibiotic resistance (kanamycin) and promoter (T7) for expression in the standard protein production strain of E. coli BL21[DE3]. The plasmid set includes commonly used tags for purification and assays (his, twin-strep and avi tag) as well as fusion protein partners that may aid target protein solubility and yield, SUMO, MBP, GST and sfGFP. Also included are plasmids with secretion peptide signals for transport of the target protein to the E. coli periplasm via various pathways (SEC, SRP, TatA). We have evaluated the 4 of the vectors using a test super folder GFP insert and found that using the Golden Gate process allows cloning efficiencies of greater than 90% to be routinely obtained. Vectors were further evaluated by expressing and purifying the target insert. The plasmid vector set described herein should prove useful to any investigator who has to routinely evaluate numerous protein expression constructs and are freely available through Addgene.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".