Kinase Sensing Based on Protein Interactions at the Catalytic Site
Bibliographic record
Abstract
The role kinases play in regulating cellular processes makes them potential biomarkers for detecting the onset and prognosis of various diseases, including many types of cancer. Current kinase biosensors, including electrochemical and radiometric methods, rely on sensing the ATP-dependant enzymatic phosphorylation reaction. Here we introduce a new type of interaction-based electrochemical kinase biosensor that does not require any chemical labelling or modification. The basis for sensing is the interactions between the catalytic site of the kinase and the phosphorylation site of its substrate rather than the phosphorylation reaction. We demonstrated this concept with the ERK2 kinase and its substrate protein HDGF, which is involved in lung cancer. A peptide monolayer derived from the HDGF phosphorylation site was adsorbed onto a gold electrode and was used to sense ERK2 without ATP. The sensitivity of the assay was down to 10 nM of ERK2, corresponding with the range of its cellular concentrations. Surface chemistry analysis confirmed that ERK2 was bound to the HDGF peptide monolayer. This increased the permeability of redox-active species through the monolayer and resulted in ERK2 electrochemical sensing. Since our detection approach is based on protein-protein interactions and not on the enzymatic reaction, it can be further utilized for more selective detection of different types of enzymes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".