Maximal Activation of Skeletal Muscle Thin Filaments Requires Both Rigor Myosin S1 and Calcium
Bibliographic record
Abstract
The regulation by calcium and rigor-bound myosin-S1 of the rate of acceleration of 2'-deoxy-3'-O-(N-methylanthraniloyl)ADP (mdADP) release from myosin-mdADP-P(i) by skeletal muscle thin filaments (reconstituted from actin-tropomyosin-troponin) was measured using double mixing stopped-flow fluorescence with the nucleotide substrate 2'-deoxy-3'-O-(N-methylanthraniloyl). The predominant mechanism of regulation is the acceleration of product dissociation by a factor of approximately 200 by thin filaments in the fully activated conformation (bound calcium and rigor S1) relative to the inhibited conformation (no bound calcium or rigor S1). In contrast, only 2-3-fold regulation is due to a change in actin affinity such as would be expected by "steric blocking" of the myosin binding site of the thin filament by tropomyosin. The binding of one ligand (either calcium or rigor-S1) produces partial activation of the rate of product dissociation, but the binding of both is required to maximally accelerate product dissociation to a rate similar to that obtained with F-actin in the absence of regulatory proteins. The data support an allosteric regulation model in which the binding of either calcium or rigor S1 alone to the thin filament shifts the equilibrium in favor of the active conformation, but full activation requires binding of both ligands.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".