Chemical Genetic Validation of CSNK2 Substrates Using an Inhibitor-Resistant Mutant in Combination with Triple SILAC Quantitative Phosphoproteomics
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Bibliographic record
Abstract
Casein Kinase 2 (CSNK2) is an extremely pleiotropic, ubiquitously expressed protein kinase involved in the regulation of numerous key biological processes. Mapping the CSNK2-dependent phosphoproteome is necessary for better characterization of its fundamental role in cellular signalling. While ATP-competitive inhibitors have enabled the identification of many putative kinase substrates, compounds targeting the highly conserved ATP-binding pocket often exhibit off-target effects limiting their utility for definitive kinase-substrate assignment. To overcome this limitation, we devised a strategy combining chemical genetics and quantitative phosphoproteomics to identify and validate CSNK2 substrates. We engineered U2OS cells expressing exogenous wild type CSNK2A1 (WT) or a triple mutant (TM, V66A/H160D/I174A) with substitutions at residues important for inhibitor binding. These cells were treated with CX-4945, a clinical-stage inhibitor of CSNK2, and analyzed using large-scale triple SILAC (Stable Isotope Labelling of Amino Acids in Cell Culture) quantitative phosphoproteomics. In contrast to wild-type CSNK2A1, CSNK2A1-TM retained activity in the presence of CX-4945 enabling identification and validation of several CSNK2 substrates on the basis of their increased phosphorylation in cells expressing CSNK2A1-TM. Based on high conservation within the kinase family, we expect that this strategy can be broadly adapted for identification of other kinase-substrate relationships.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it