SPECC1L binds the myosin phosphatase complex MYPT1/PP1β and can regulate its distribution between microtubules and filamentous actin
Bibliographic record
Abstract
The subcellular localization, activity , and substrate specificity of the serine/threonine protein phosphatase 1 catalytic subunit (PP1 cat ) is mediated through its dynamic association with regulatory subunits in holoenzyme complexes. While some functional overlap is observed for the three human PP1 cat isoforms, they also show distinct targeting based on relative preferences for specific regulatory subunits. A well-known example is the preferential association of MYPT1 with PP1β in the myosin phosphatase complex. In smooth muscle, MYPT1/PP1β counteracts the muscle contraction induced by phosphorylation of the light chains of myosin by the myosin light chain kinase. This phosphatase complex is also found in nonmuscle cells, where it is targeted to both myosin and nonmyosin substrates and contributes to regulation of the balance of cytoskeletal structure and motility during cell migration and division. Although it remains unclear how MYPT1/PP1β traffics between microtubule- and actin-associated substrates, our identification of the microtubule- and actin-binding protein SPECC1L in both the PP1β and MYPT1 interactomes suggests that it is the missing link. Our validation of their association using coimmunoprecipitation and proximity biotinylation assays, together with the strong overlap that we observed for the SPECC1L and MYPT1 interactomes, confirmed that they exist in a stable complex in the cell. We further showed that SPECC1L binds MYPT1 directly and that it can impact the balance of the distribution of the MYPT1/PP1β complex between the microtubule and filamentous actin networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".