Integrated bioprocessing and genetic strategies to enhance soluble expression of anti-HER2 immunotoxin in E. Coli
Bibliographic record
Abstract
Immunotoxins are widely applied for cancer therapy. However, bacterial expression of immunotoxins usually leads to the formation of insoluble and non-functional recombinant proteins. This study was aimed to improve soluble expression of a novel anti-HER2 immunotoxin under the regulation of the trc promoter in Escherichia coli by optimization of the cultivation conditions using response surface methodology (RSM). To conduct RSM, four cultivation variables (i.e., inducer concentration, post-induction time, post-induction temperature, and medium recipe), were selected for statistical characterization and optimization using the Box-Behnken design and Design Expert software. Based on the developed model using the Box-Behnken design, the optimal cultivation conditions for soluble expression of anti-HER2 immunotoxin were determined to be 0.1 mM IPTG for induction in the LB medium at 33 °C for 18 h. The expressed immunotoxin was successfully purified using affinity chromatography with more than 90% purity and its bioactivity was confirmed using cell-based ELISA. Technical approach developed in this study can be generally applied to enhance the production yield and quality of recombinant proteins using E. coli as the gene expression system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".