Chemoproteomic Characterization of Protein Kinase Inhibitors Using Immobilized ATP
Bibliographic record
Abstract
Protein kinase inhibitors have emerged as indispensable tools for the elucidation of the biological functions of specific signal transduction pathways and as promising candidates for molecular-targeted therapy. However, because many protein kinase inhibitors are ATP-competitive inhibitors targeting the catalytic site of specific protein kinases, the large number of protein kinases that are encoded within eukaryotic genomes and the existence of many other cellular proteins that bind ATP result in the prospect of off-target effects for many of these compounds. Many of the potential off-target effects remain unrecognized because protein kinase inhibitors are often developed and tested primarily on the basis of in vitro assays using purified components. To overcome this limitation, we describe a systematic approach to characterize ATP-competitive protein kinase inhibitors employing ATP-sepharose to capture the purine-binding proteome from cell extracts. Protein kinase inhibitors can be used in competition experiments to prevent binding of specific cellular proteins to ATP-sepharose or to elute bound proteins from ATP-sepharose. Collectively, these strategies can enable validation of interactions between a specific protein kinase and an inhibitor in complex mixtures and can yield the identification of inhibitor targets.
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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".