Analysis of conditions affecting auto-phosphorylation of human kinases during expression in bacteria
Bibliographic record
Abstract
Bacterial over-expression of kinases is often associated with high levels of auto-phosphorylation resulting in heterogeneous recombinant protein preparations or sometimes in insoluble protein. Here we present expression systems for nine kinases in Escherichia coli and, for the most heavily phosphorylated, the characterisation of factors affecting auto-phosphorylation. Experiments showed that the level of auto-phosphorylation was proportional to the rate of expression. Comparison of phosphorylation states following in vitro phosphorylation with phosphorylation states following expression in E. coli showed that the non-physiological 'hyper-phosphorylation' was occurring at sites that would require local unfolding to be accessible to a kinase active site. In contrast, auto-phosphorylation on unphosphorylated kinases that had been expressed in bacteria overexpressing λ-phosphatase was only observed on distinct exposed sites. Remarkably, the Ser/Thr kinase PLK4 auto-phosphorylated on a tyrosine residue (Tyr177) located in the activation segment. The results give support to a mechanism in which auto-phosphorylation occurs before or during protein folding. In addition, the expression systems and protocols presented will be a valuable resource to the research community.
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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 | 0.000 | 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) | 0.001 | 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".