Kinetics of Myosin Light Chain Kinase Activation of Smooth Muscle Myosin in an<i>in Vitro</i>Model System
Bibliographic record
Abstract
During activation of smooth muscle contraction, one myosin light chain kinase (MLCK) molecule rapidly phosphorylates many smooth muscle myosin (SMM) molecules, suggesting that muscle activation rates are influenced by the kinetics of MLCK-SMM interactions. To determine the rate-limiting step underlying activation of SMM by MLCK, we measured the kinetics of calcium-calmodulin (Ca²⁺CaM)-MLCK-mediated SMM phosphorylation and the corresponding initiation of SMM-based F-actin motility in an in vitro system with SMM attached to a coverslip surface. Fitting the time course of SMM phosphorylation to a kinetic model gave an initial phosphorylation rate, kp(o), of ~1.17 heads s⁻¹ MLCK⁻¹. Also, we measured the dwell time of single streptavidin-coated quantum dot-labeled MLCK molecules interacting with surface-attached SMM and phosphorylated SMM using total internal reflection fluorescence microscopy. From these data, the dissociation rate constant from phosphorylated SMM was 0.80 s⁻¹, which was similar to the kp(o) mentioned above and with rates measured in solution. This dissociation rate was essentially independent of the phosphorylation state of SMM. From calculations using our measured dissociation rates and Kd values, and estimates of SMM and MLCK concentrations in muscle, we predict that the dissociation of MLCK from phosphorylated SMM is rate-limiting and that the rate of the phosphorylation step is faster than this dissociation rate. Also, association with SMM (11-46 s⁻¹) would be much faster than with pSMM (<0.1-0.2 s⁻¹). This suggests that the probability of MLCK interacting with unphosphorylated versus phosphorylated SMM is 55-460 times greater. This would avoid sequestering MLCK to unproductive interactions with previously phosphorylated SMM, potentially leading to faster rates of phosphorylation in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".