Comparison of the Luminescent ADP-Glo Assay to a Standard Radiometric Assay for Measurement of Protein Kinase Activity
Bibliographic record
Abstract
Many assay technologies have been developed and utilized to efficiently assay and screen against protein kinase targets. The radiometric assay format for assaying the protein kinase targets has been considered the "Gold Standard" format since it allows the direct readout of kinase functional activity and is a universal assay that is highly sensitive. However, the hazardous nature of the radiometric assay together with the regulatory hurdles has led to the development of alternative assay formats for assessing protein kinase activity measurements. The luminescent ADP-Glo assay has been developed as an alternative to radiometric format for assaying protein kinase targets. This assay allows the measurement of the ADP product formed during the kinase reaction. Therefore, the luminescent ADP-Glo assay is similar to the radiometric format in that it measures the direct product of the protein kinase reaction. Furthermore, since the ADP product is generated by all protein kinase reactions, this is a universal format that can be used for assaying any given protein kinase target. Analysis of data generated with multiple protein kinase targets and the luminescent ADP-Glo technology shows comparable results to the radiometric assay format. Therefore, the luminescent ADP-Glo assay is a robust new technology for evaluating catalytic function of protein kinases as well as other ATPases.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".