Myosin Assembly, Maintenance and Degradation in Muscle: Role of the Chaperone UNC-45 in Myosin Thick Filament Dynamics
Bibliographic record
Abstract
Myofibrillogenesis in striated muscle cells requires a precise ordered pathway to assemble different proteins into a linear array of sarcomeres. The sarcomere relies on interdigitated thick and thin filaments to ensure muscle contraction, as well as properly folded and catalytically active myosin head. Achieving this organization requires a series of protein folding and assembly steps. The folding of the myosin head domain requires chaperone activity to attain its functional conformation. Folded or unfolded myosin can spontaneously assemble into short myosin filaments, but further assembly requires the short and incomplete myosin filaments to assemble into the developing thick filament. These longer filaments are then incorporated into the developing sarcomere of the muscle. Both myosin folding and assembly require factors to coordinate the formation of the thick filament in the sarcomere and these factors include chaperone molecules. Myosin folding and sarcomeric assembly requires association of classical chaperones as well as folding cofactors such as UNC-45. Recent research has suggested that UNC-45 is required beyond initial myosin head folding and may be directly or indirectly involved in different stages of myosin thick filament assembly, maintenance and degradation.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".