Affinity for MgADP and force of unbinding from actin of myosin purified from tonic and phasic smooth muscle
Bibliographic record
Abstract
Smooth muscle is unique in its ability to maintain force at low MgATP consumption. This property, called the latch state, is more prominent in tonic than phasic smooth muscle. Studies performed at the muscle strip level have suggested that myosin from tonic muscle has a greater affinity for MgADP and therefore remains attached to actin longer than myosin from phasic muscle, allowing for cross-bridge dephosphorylation and latch-bridge formation. An alternative hypothesis is that after dephosphorylation, myosin reattaches to actin and maintains force. We investigated these fundamental properties of smooth muscle at the molecular level. We used an in vitro motility assay to measure actin filament velocity (nu(max)) when propelled by myosin purified from phasic or tonic muscle at increasing [MgADP]. Myosin was 25% thiophosphorylated and 75% unphosphorylated to approximate in vivo conditions. The slope of nu(max) versus [MgADP] was significantly greater for tonic (-0.51+/-0.04) than phasic muscle myosin (-0.15+/-0.04), demonstrating the greater MgADP affinity of myosin from tonic muscle. We then used a laser trap assay to measure the unbinding force from actin of populations of unphosphorylated tonic and phasic muscle myosin. Both myosin types attached to actin, and their unbinding force (0.092+/-0.022 pN for phasic muscle and 0.084+/-0.017 pN for tonic muscle) was not statistically different. We conclude that the greater affinity for MgADP of tonic muscle myosin and the reattachment of dephosphorylated myosin to actin may both contribute to the latch state.
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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 | 0.001 |
| 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".