Peptide Biosensors for the Electrochemical Measurement of Protein Kinase Activity
Bibliographic record
Abstract
The kinase activities are elucidated using the novel redox-active cosubstrate adenosine 5'-[gamma-ferrocene] triphosphate (Fc-ATP), which enables the kinase-catalyzed transfer of a redox active gamma-phosphate-Fc to a hydroxyamino acid. In this report, a versatile electrochemical biosensor is developed for monitoring the activity and inhibition of a serine/threonine kinase, casein kinase 2 (CK2), and protein tyrosine kinases, Abl1-T315I and HER2, in buffered solutions and in cell lysates. The method is based on the labeling of a specific phosphorylation event with Fc, followed by electrochemical detection. The electrochemical response obtained from the "ferrocenylated" peptides enables monitoring the activity of the kinase and its substrate, as well as the inhibition of small molecule inhibitors on protein phosphorylation. Kinetic information was extracted from the electrochemical measurements for the determination of K(m) and V(m) values, which were in agreement with those previously reported. Kinase reactions were also performed in the presence of well-defined inhibitors of CK2, 4,5,6,7-tetrabromo-2-azabenzimidazole, 2-dimethylamino-4,5,6,7-tetrabromo-1H-benzimidazole, and E-3-(2,3,4,5-tetrabromophenyl)acrylic acid as well as the nonspecific kinase inhibitors, staurosporine and N-benzoylstaurosporine. On the basis of the dependency of the Fc signal on inhibitor concentration, K(i) of the inhibitors was estimated, which were also in agreement with the literature values. The performance of the biosensor was optimized including the kinase reaction, incubation with Fc-ATP, and the small molecule inhibitors. Peptide modified electrochemical biosensors are promising candidates for cost-effective in vitro kinase activity and inhibitor screening assays.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".