A fluorescent cassette-based strategy for engineering multiple domain fusion proteins
Bibliographic record
Abstract
BACKGROUND: The engineering of fusion proteins has become increasingly important and most recently has formed the basis of many biosensors, protein purification systems, and classes of new drugs. Currently, most fusion proteins consist of three or fewer domains, however, more sophisticated designs could easily involve three or more domains. Using traditional subcloning strategies, this requires micromanagement of restriction enzymes sites that results in complex workaround solutions, if any at all. RESULTS: Therefore, to aid in the efficient construction of fusion proteins involving multiple domains, we have created a new expression vector that allows us to rapidly generate a library of cassettes. Cassettes have a standard vector structure based on four specific restriction endonuclease sites and using a subtle property of blunt or compatible cohesive end restriction enzymes, they can be fused in any order and number of times. Furthermore, the insertion of PCR products into our expression vector or the recombination of cassettes can be dramatically simplified by screening for the presence or absence of fluorescence. CONCLUSIONS: Finally, the utility of this new strategy was demonstrated by the creation of basic cassettes for protein targeting to subcellular organelles and for protein purification using multiple affinity tags.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".