Label-Free Electrical Sensing of Small-Molecule Inhibition on Tyrosine Phosphorylation
Bibliographic record
Abstract
Protein tyrosine kinases (PTKs) play a central role in human carcinogenesis and have emerged as the promising new targets. Small-molecule inhibitors of PTKs have shown impressive anticancer effects and are rapidly entering the clinic. PTK assays allow for high-throughput identification of small-molecule inhibitors. However, current methods of detecting kinase activity require the use of radioisotopes or expensive reagents; such as fluorescently labeled antibodies. We have developed a novel label-free approach for the quantitative detection of peptide tyrosine (Tyr) phosphorylation using the electrochemical oxidation current signal of Tyr. When the phosphorylation is achieved, the phosphorylated Tyr (Tyr-P) cannot be oxidized at approximately 0.65 V. However, when the phosphorylation is successfully inhibited using a small molecule, Tyr can be oxidized and result in a high current response on a multiwalled carbon nanotube-modified screen-printed carbon electrode. We determined the activity of cellular-sarcoma (c-Src) nonreceptor PTK, p60(c-Src), in combination with its highly specific substrate peptide, Raytide. Tyr kinase reactions were also performed in the presence of a well-defined small-molecule inhibitor, 4-amino-5-(4-chlorophenyl)-7- (tert-butyl)pyrazolo[3,4-d]pyrimidine (PP2). Based on the dependency of Tyr oxidation signal on inhibitor concentration, IC50 value, half-maximal inhibition of the inhibitor, was estimated as 5 nM for PP2. Our label-free electrochemical method is a promising candidate for pharmaceutical research and development in screening small-molecule inhibitors of PTKs.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".