Vectorial activation of smooth muscle myosin filaments and its modulation by telokin
Bibliographic record
Abstract
Smooth muscle myosin copurifies with myosin light chain kinase (MLCK) and calmodulin (CaM) as well as with variable amounts of myosin phosphatase. Therefore, myosin filaments formed in vitro also contain relatively high levels of these enzymes. Thus these filaments may be considered to be native-like because they are similar to those expected to exist in vivo. These endogenous enzymes are present at high concentrations relative to myosin, sufficient for rapid phosphorylation and dephosphorylation of the filaments at rates comparable to those observed for contraction and relaxation in intact muscle strips. The phosphorylation by MLCK/CaM complex appears to exhibit some directionality and is not governed by a random diffusional process. For the mixtures of myosin filaments with and without the endogenous MLCK/CaM complex, the complex preferentially phosphorylates its own parent filament at a higher rate than the neighboring filaments. This selective or vectorial-like activation is lost or absent when myosin filaments are dissolved at high ionic strength. Similar vectorial-like activation is exhibited by the reconstituted filament suspensions, but the soluble systems composed of isolated regulatory light chain or soluble myosin head subfragments exhibit normal diffusional kinetic behavior. At physiological concentrations, kinase related protein (telokin) effectively modulates the activation process by reducing the phosphorylation rate of the filaments without affecting the overall phosphorylation level. This results from telokin-induced liberation of the active MLCK/CaM complex from the filaments, so that the latter can also activate the neighboring filaments via a slower diffusional process. When this complex is bound at insufficient levels, this actually results in acceleration of the initial phosphorylation rates. In short, I suggest that in smooth muscle, telokin plays a chaperone role for myosin and its filaments.
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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".