Comparison of CX-4945 and SGC-CK2-1 as inhibitors of CSNK2 using quantitative phosphoproteomics: Triple SILAC in combination with inhibitor-resistant CSNK2
Bibliographic record
Abstract
Specificity is a limiting factor when using small-molecule inhibitors to study protein kinase signalling. Since inhibitor-resistant kinase mutants (i.e., drug-resistant alleles) remain active in the presence of inhibitor, they facilitate validation of on-target effects. By combining an inhibitor-resistant kinase mutant with mass spectrometry-based phosphoproteomics, we previously devised a systematic strategy for reliable identification and validation of CSNK2 substrates. In this study, we use the same strategy to evaluate the selectivity of CX-4945, a clinical stage CSNK2 inhibitor, and SGC-CK2-1, a chemical probe selectively targeting CSNK2. Human osteosarcoma (U2OS) cells expressing exogenous wild-type CSNK2A1 (WT) or an inhibitor-resistant triple mutant (TM, V66A/H160D/I174A) were treated with CX-4945 or SGC-CK2-1 prior to analysis using triple SILAC (phospho)proteomics. The minority of phosphosites, 15% at 4 h and 5% at 24 h, that were significantly downregulated in response to CX-4945 treatment were determined to be CSNK2A1-dependent. By comparison, the majority of phosphosites, >55% at both 4 and 24 h, that were significantly downregulated in response to SGC-CK2-1 were identified as CSNK2A1-dependent. This indicates that SGC-CK2-1 exhibits significantly greater selectivity towards CSNK2A1 than CX-4945. Notably, utilization of SGC-CK2-1 in cells expressing CSNK2A1-TM enabled the identification of >300 CSNK2A1-dependent phosphosites. Overall, this study highlights the utility of exploiting highly selective chemical probes together with inhibitor-resistant kinase mutants to facilitate identification of bona fide kinase substrates.
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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 | 0.001 | 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 | 0.001 |
| 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".