Phosphorylation Status of 72 kDa MMP-2 Determines Its Structure and Activity in Response to Peroxynitrite
Bibliographic record
Abstract
Matrix metalloproteinase-2 (MMP-2) is a key intra- and extra-cellular protease which contributes to several oxidative stress related pathologies. A molecular understanding of 72 kDa MMP-2 activity, directly mediated by S-glutathiolation of its cysteine residues in the presence of peroxynitrite (ONOO(-)) and by phosphorylation of its serine and threonine residues, is essential to develop new generation inhibitors of intracellular MMP-2. Within its propeptide and collagen binding domains there is an interesting juxtaposition of predicted phosphorylation sites with nearby cysteine residues which form disulfide bonds. However, the combined effect of these two post-translational modifications on MMP-2 activity has not been studied. The activity of human recombinant 72 kDa MMP-2 (hrMMP-2) following in vitro treatments was measured by troponin I proteolysis assay and a kinetic activity assay using a fluorogenic peptide substrate. ONOO(-) treatment in the presence of 30 µM glutathione resulted in concentration-dependent changes in MMP-2 activity, with 0.1-1 µM increasing up to twofold and 100 µM attenuating its activity. Dephosphorylation of MMP-2 with alkaline phosphatase markedly increased its activity by sevenfold, either with or without ONOO(-). Dephosphorylation of MMP-2 also affected the conformational structure of the enzyme as revealed by circular dichroism studies, suggesting an increase in the proportion of α-helices and a decrease in β-strands compared to the phosphorylated form of MMP-2. These results suggest that ONOO(-) activation (at low µM) and inactivation (at high µM) of 72 kDa MMP-2, in the presence or absence of glutathione, is also influenced by its phosphorylation status. These insights into the role of post-translational modifications in the structure and activity of 72 kDa MMP-2 will aid in the development of inhibitors specifically targeting intracellular MMP-2.
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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 | 0.000 |
| 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".