Ten years in the life of an enzyme: the story of the human MMP-13 (collagenase-3)
Bibliographic record
Abstract
In 1994, a new human matrix metalloproteinase (MMP) was identified and cloned. This enzyme displayed the structural characteristics of a collagenase and was named collagenase-3, or MMP-13 according to MMP nomenclature. This review describes the research advances in the understanding of the function/production of the human MMP-13 at the tissular, cellular, biochemical, and molecular levels. In contrast to many human MMPs, the MMP-13 distribution pattern is restrictive in normal tissues and selective in pathological conditions. This enzyme plays a premier role in tissue remodeling as well as in some pathological processes such as cancer and arthritis. MMP-13 demonstrates versatility in its substrate utilization. In addition to being highly active on type II collagen, MMP-13 cleaves other substrates, mostly macromolecules of the extracellular matrix, but also molecules such as connective tissue (CTGF) and fibrinogen. MMP-13 is controlled at multiple levels: i.e., the expression/synthesis, activation, and inhibition of the active enzyme. Unlike other MMPs, the human MMP-13 gene is transcribed into several transcripts which could yield proteins with activities and functions different from those of the original MMP-13. Activation of MMP-13 involves a proteolytic cascade including MMP-14 (MT1-MMP) and MMP-2. Transcription is regulated by numer-ous agents, mostly by growth factors, proinflammatory cytokines and mechanical stimuli. Cloning of the MMP-13 promoter revealed the presence of a number of binding sites implicated in transcriptional regulation: TATA box, AP-1, PEA-3, OSE-2, and the newly identified negative regulator, AGRE. MMP-13 constitutes a more complex system than was originally thought. Although our knowledge of MMP-13 biochemistry and regulation has greatly increased over the years, there is still much to discover.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".