Regulation of matrix metalloproteinase‐2 (MMP‐2) activity by phosphorylation
Bibliographic record
Abstract
The regulation of matrix metalloproteinases (MMP) has been studied extensively due to the fundamental roles these zinc-endopeptidases play in diverse physiological and pathological processes. However, phosphorylation has not previously been considered as a potential modulator of MMP activity. The ubiquitously expressed MMP-2 contains 29 potential phosphorylation sites. Mass spectrometry reveals that at least five of these sites are phosphorylated in hrMMP-2 expressed in mammalian cells. Treatment of HT1080 cells with an activator of protein kinase C results in a change in MMP-2 immunoreactivity on 2D immunoblots consistent with phosphorylation, and purified MMP-2 is phosphorylated by protein kinase C in vitro. Furthermore, MMP-2 from HT1080 cell-conditioned medium is immunoreactive with antibodies directed against phosphothreonine and phosphoserine, which suggests that it is phosphorylated. Analysis of MMP-2 activity by zymography, gelatin dequenching assays, and measurement of kinetic parameters shows that the phosphorylation status of MMP-2 significantly affects its enzymatic properties. Consistent with this, dephosphorylation of MMP-2 immunoprecipitated from HT1080 conditioned medium with alkaline phosphatase significantly increases its activity. We conclude that MMP-2 is modulated by phosphorylation on multiple sites and that protein kinase C may be a regulator of this protease in vivo.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".