The Roles of Substrate Thermal Stability and P2 and P1′ Subsite Identity on Matrix Metalloproteinase Triple-helical Peptidase Activity and Collagen Specificity
Bibliographic record
Abstract
The hydrolysis of collagen (collagenolysis) is one of the committed steps in extracellular matrix turnover. Within the matrix metalloproteinase (MMP) family distinct preferences for collagen types are seen. The substrate determinants that may guide these specificities are unknown. In this study, we have utilized 12 triple-helical substrates in combination with 10 MMPs to better define the contributions of substrate sequence and thermal stability toward triple helicase activity and collagen specificity. In general, MMP-13 was found to be distinct from MMP-8 and MT1-MMP(Delta279-523), in that enhanced substrate thermal stability has only a modest effect on activity, regardless of sequence. This result correlates to the unique collagen specificity of MMP-13 compared with MMP-8 and MT1-MMP, in that MMP-13 hydrolyzes type II collagen efficiently, whereas MMP-8 and MT1-MMP are similar in their preference for type I collagen. In turn, MMP-1 was the least efficient of the collagenolytic MMPs at processing increasingly thermal stable triple helices and thus favors type III collagen, which has a relatively flexible cleavage site. Gelatinases (MMP-2 and MMP-9(Delta444-707)) appear incapable of processing more stable helices and are thus mechanistically distinct from collagenolytic MMPs. The collagen specificity of MMPs appears to be based on a combination of substrate sequence and thermal stability. Analysis of the hydrolysis of triple-helical peptides by an MMP mutant indicated that Tyr(210) functions in triple helix binding and hydrolysis, but not in processing triple helices of increasing thermal stabilities. Further exploration of MMP active sites and exosites, in combination with substrate conformation, may prove valuable for additional dissection of collagenolysis and yield information useful in the design of more selective MMP inhibitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".