Utility of Alternative Promoters for Foreign Gene Expression Using the Baculovirus Expression Vector System
Bibliographic record
Abstract
The baculovirus expression vector system (BEVS) is a widely used platform for recombinant protein production for use in a wide variety of applications. Of particular interest is production of virus-like particles (VLPs), which consist of multiple viral proteins that self-assemble in strict stoichiometric ratios to mimic the structure of a virus but lacks its genetic material, while a significant amount of effort has been spent on optimizing expression ratios by co-infecting cells with multiple recombinant BEVs and modulating different process parameters, co-expressing multiple foreign genes from a single rBEV may offer more promise. However, there is currently a lack of promoters available with which to optimize co-expression of each foreign gene. To address this, previously published transcriptome data was used to identify promoters that have incrementally lower expression profiles and compared by expressing model cytoplasmic and secreted proteins. Bioinformatics was also used to identify sequence determinants that may be important for late gene transcription regulation, and translation initiation. The identified promoters and bioinformatics analyses may be useful for optimizing expression of foreign genes in the BEVS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".