Using Chemical Genetics to Define Zipper‐Interacting Protein Kinase Signalling Events
Bibliographic record
Abstract
Zipper‐interacting protein kinase (ZIPK) has emerged as an important regulator of apoptosis, cell motility and vascular smooth muscle (VSM) contraction. To identify the precise role of ZIPK in these processes, we set out to identify ZIPK substrates using a chemical‐genetic approach. Mutation of ZIPK at the conserved gatekeeper residue (L93G) within the ATP‐binding site was performed in order to develop cell‐based model systems for the analysis of ZIPK function. As proof of principle, kinetic analyses support the selectivity of analog‐sensitive kinase inhibitors (pyrazolo[3,5‐d]pyrimidine; PP1) for L93G‐ZIPK with minimal inhibitory potential observed for WT‐ZIPK or other VSM contractile kinases (ROK or MLCK). In this regard, the 1NM‐PP1 inhibitor (10 mM) provides maximal distinction of L93G‐ZIPK and WT‐ZIPK activities with essentially no off‐target effects. Moreover, the PP1 compounds have no effect on Ca2+‐dependent or Ca2+‐independent VSM contractions. These results are expected since the PP1 inhibitors have high specificity for the mutated kinase but minimal ‘off‐target’ effects on endogenous kinases. Although some novel off‐target effects of the analog‐sensitive kinase inhibitors were identified (i.e., able to inhibit ROK in vitro), we conclude that application of the chemical‐genetics approach with the L93G‐ZIPK and 1NM‐PP1 pairing will enable us to define the explicit actions of ZIPK. This submission is sponsored by Justin A. MacDonald, society affiliation member of ASBMB, jmacdo@ucalgary.ca Supported by the Heart & Stroke Foundation of Canada
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".