Dependence of myosin filament structure on intracellular calcium concentration in skeletal muscle
Bibliographic record
Abstract
Contraction of skeletal muscle is triggered by an increase in intracellular calcium concentration that relieves the structural block on actin-binding sites in resting muscle, potentially allowing myosin motors to bind and generate force. However, most myosin motors are not available for actin binding because they are stabilized in folded helical tracks on the surface of myosin-containing thick filaments. High-force contraction depends on the release of the folded motors, which can be triggered by stress in the thick filament backbone, but additional mechanisms may link the activation of the thick filaments to that of the thin filaments or to intracellular calcium concentration. Here, we used x-ray diffraction in combination with temperature-jump activation to determine the steady-state calcium dependence of thick filament structure and myosin motor conformation in near-physiological conditions. We found that x-ray signals associated with the perpendicular motors characteristic of isometric force generation had almost the same calcium sensitivity as force, but x-ray signals associated with perturbations in the folded myosin helix had a much higher calcium sensitivity. Moreover, a new population of myosin motors with a longer axial periodicity became prominent at low levels of calcium activation and may represent an intermediate regulatory state of the myosin motors in the physiological pathway of filament activation.
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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 | 0.001 |
| 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".