Diffusion of myosin light chain kinase on actin: A mechanism to enhance myosin phosphorylation rates in smooth muscle
Bibliographic record
Abstract
Smooth muscle myosin (SMM) light chain kinase (MLCK) phosphorylates SMM, thereby activating the ATPase activity required for muscle contraction. The abundance of active MLCK, which is tightly associated with the contractile apparatus, is low relative to that of SMM. SMM phosphorylation is rapid despite the low ratio of MLCK to SMM, raising the question of how one MLCK rapidly phosphorylates many SMM molecules. We used total internal reflection fluorescence microscopy to monitor single molecules of streptavidin-coated quantum dot-labeled MLCK interacting with purified actin, actin bundles, and stress fibers of smooth muscle cells. Surprisingly, MLCK and the N-terminal 75 residues of MLCK (N75) moved on actin bundles and stress fibers of smooth muscle cell cytoskeletons by a random one-dimensional (1-D) diffusion mechanism. Although diffusion of proteins along microtubules and oligonucleotides has been observed previously, this is the first characterization to our knowledge of a protein diffusing in a sustained manner along actin. By measuring the frequency of motion, we found that MLCK motion is permitted only if acto-myosin and MLCK-myosin interactions are weak. From these data, diffusion coefficients, and other kinetic and geometric considerations relating to the contractile apparatus, we suggest that 1-D diffusion of MLCK along actin (a) ensures that diffusion is not rate limiting for phosphorylation, (b) allows MLCK to locate to areas in which myosin is not yet phosphorylated, and (c) allows MLCK to avoid getting "stuck" on myosins that have already been phosphorylated. Diffusion of MLCK along actin filaments may be an important mechanism for enhancing the rate of SMM phosphorylation in smooth muscle.
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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 | 0.001 |
| 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".