Unbiased Functional Proteomics Strategy for Protein Kinase Inhibitor Validation and Identification of<i>bona fide</i>Protein Kinase Substrates: Application to Identification of EEF1D as a Substrate for CK2
Bibliographic record
Abstract
Protein kinases have emerged as attractive targets for treatment of several diseases prompting large-scale phosphoproteomics studies to elucidate their cellular actions and the design of novel inhibitory compounds. Current limitations include extensive reliance on consensus predictions to derive kinase-substrate relationships from phosphoproteomics data and incomplete experimental validation of inhibitors. To overcome these limitations in the case of protein kinase CK2, we employed functional proteomics and chemical genetics to enable identification of physiological CK2 substrates and validation of CK2 inhibitors including TBB and derivatives. By 2D electrophoresis and mass spectrometry, we identified the translational elongation factor EEF1D as a protein exhibiting CK2 inhibitor-dependent decreases in phosphorylation in (32)P-labeled HeLa cells. Direct phosphorylation of EEF1D by CK2 was shown by performing CK2 assays with EEF1D -FLAG from HeLa cells. Dramatic increases in EEF1D phosphorylation following λ-phosphatase treatment and phospho- EEF1D antibody recognizing EEF1D pS162 indicated phosphorylation at the CK2 site in cells. Furthermore, phosphorylation of EEF1D in the presence of TBB or TBBz is restored using CK2 inhibitor-resistant mutants. Collectively, our results demonstrate that EEF1D is a bona fide physiological CK2 substrate for CK2 phosphorylation. Furthermore, this validation strategy could be adaptable to other protein kinases and readily combined with other phosphoproteomic methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".