The Optimization of Soluble PTEN Expression in Escherichia coli
Bibliographic record
Abstract
As a vital tumor suppressor, PTEN (Phosphatase and tension homolog deleted on chromosome 10) is involved in inherited syndromes, and is among the most frequently inactivated tumor suppressor gene in sporadic cancers. PTEN loss-of-function widely occurs in human cancers via a variety of mechanisms, including genetic alterations and posttranslational modification. These suggest PTEN has a role of functional importance in a variety of cancers. In the present study, we constructed a prokaryotic expression vector that efficiently expresses GST-PTEN (the target protein in which PTEN is fused with glutathione S-transferase tag) in E. coli. We found that the target protein was partially soluble although major portions of the protein remained in the inclusion bodies. Furthermore, we explored the optimal induction temperature, isopropyl β-D-1-thiogalactopyranoside (IPTG) concentration and induction time in a series of experiments. Expression level analysis indicated that PTEN reached its peak level at 36(○)C for 8 h with 1.5625mM IPTG, while solubility analysis revealed the optimal induction temperature was at 20(○)C, the optimal IPTG concentration was 0.1µM and the optimal induction time was up to 8 h. Taken together, we provide an optimal induction condition for expressing soluble fusion protein of PTEN in E. coli, facilitating further analysis of PTEN's biological function in vitro.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".