Precise and efficient cleavage of recombinant fusion proteins using mammalian aspartic proteases
Bibliographic record
Abstract
Expression of recombinant proteins as translational fusions is commonly employed to enhance stability, increase solubility and facilitate purification of the desired protein. In general, such fusion proteins must be cleaved to release the mature protein in its native form. The usefulness of the procedure depends on the efficiency and precision of cleavage and its cost per unit activity. We report here the development of a general procedure for precise and highly efficient cleavage of recombinant fusion proteins using the protease chymosin. DNA encoding a modified pro-peptide from bovine chymosin was fused upstream of hirudin, carp growth hormone, thioredoxin and cystatin coding sequences and expressed in a bacterial Escherichia coli host. Each of the resulting fusion proteins was efficiently cleaved at the junction between the pro-peptide and the desired protein by the addition of chymosin, as determined by activity, N-terminal sequencing and mass spectrometry of the recovered protein. The system was tested further by cleavage of two fusion proteins, cystatin and thioredoxin, sequestered on oilbody particles obtained from transgenic Arabidopsis seeds. Even when the fusion protein was sequestered and immobilized on oilbodies, precise and efficient cleavage was obtained. The precision, efficiency and low cost of this procedure suggest that it could be used in larger scale manufacturing of recombinant proteins which benefit from expression as fusions in their host organism.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".