Validation of chemical genetics for the study of zipper‐interacting protein kinase signaling
Bibliographic record
Abstract
Abstract Zipper‐interacting protein kinase (ZIPK) is a Ser/Thr kinase that mediates a variety of cellular functions. Analogue‐sensitive kinase technology was applied to the study of ZIPK signaling in coronary artery smooth muscle cells. ZIPK was engineered in the ATP‐binding pocket by substitution of a bulky gatekeeper amino acid (Leu93) with glycine. Cell‐permeable derivatives of pyrazolo[3,4‐d]pyrimidine provided effective inhibition of L93G‐ZIPK (1NM‐PP1, IC 50 , 1.0 μM; 3MB‐PP1, IC 50 , 2.0 μM; and 1NA‐PP1, IC 50 , 8.6 μM) but only 3MB‐PP1 had inhibitory potential (IC 50 > 10 μM) toward wild‐type ZIPK. Each of the compounds also attenuated Rho‐associated coiled‐coil containing protein kinase (ROCK) activity under experimental conditions found to be optimal for inhibition of L93G‐ZIPK. In silico molecular simulations showed effective docking of 1NM‐PP1 into ZIPK following mutational enlargement of the ATP‐binding pocket. Molecular simulation of 1NM‐PP1 docking in the ATP‐binding pocket of ROCK was also completed. The 1NM‐PP1 inhibitor was selected as the optimal compound for selective chemical genetics in smooth muscle cells since it displayed the highest potency for L93G‐ZIPK relative to WT‐ZIPK and the weakest off‐target effects against other relevant kinases. Finally, the 1NM‐PP1 and L93G‐ZIPK pairing was effectively applied in vascular smooth muscle cells to manipulate the phosphorylation level of LC20, a previously defined target of ZIPK.
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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 | 0.000 | 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 | 0.000 |
| 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".