Zipper‐Interacting Protein Kinase: Inferring Function In Smooth Muscle Contractility By Identifying Bona Fide Substrates
Bibliographic record
Abstract
Zipper‐interacting protein kinase (ZIPK) has been implicated in Ca 2+ ‐independent smooth muscle contraction, although its specific role is unknown. To understand the role of ZIPK in smooth muscle contractility, we set out to identify bona fide ZIPK substrates, using a technique that takes advantage of ‘bulky’ ATP analogues (modified at the N 6 position) and a bioengineered ZIPK capable of utilizing such substrates. Conserved amino acid residues in ZIPK that come into close contact with the N 6 region of ATP were identified and mutated. To identify substrates, 32 P‐labeled 6‐Phe‐ATP and ZIPK L93G protein were added to permeabilized rat caudal arterial smooth muscle strips. 32 P‐labelled substrate proteins were detected by autoradiography and phosphorimaging following SDS‐PAGE. Mass spectrometry was employed to identify ZIPK substrates and LC 20 was confirmed as a direct target. Two other substrates (18 kDa and 40 kDa) were also detected. Experiments were also carried out with rat caudal arterial smooth muscle in the absence of endogenous ATP, but supplemented with 6‐Phe‐ATP and ZIPK L93G, and putative ZIPK substrates identified by western blotting with phosphospecific antibodies. LC 20 was confirmed as a direct target of ZIPK by this method; however no phosphorylation of MYPT1 was detected. We conclude that ZIPK is involved in regulation of smooth muscle contraction through direct phosphorylation of LC 20 .
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".