Proteomics Discovery of Metalloproteinase Substrates in the Cellular Context by iTRAQ™ Labeling Reveals a Diverse MMP-2 Substrate Degradome
Bibliographic record
Abstract
Elucidation of protease substrate degradomes is essential for understanding the function of proteolytic pathways in the protease web and how proteases regulate cell function. We identified matrix metalloproteinase-2 (MMP-2) cleaved proteins, solubilized pericellular matrix, and shed cellular ectodomains in the cellular context using a new multiplex proteomics approach. Tryptic peptides of intact and cleaved proteins, collected from conditioned culture medium of Mmp2(-/-) fibroblasts expressing low levels of transfected active human MMP-2 at different time points, were amine-labeled with iTRAQ mass tags. Peptide identification and relative quantitation between active and inactive protease transfectants were achieved following tag fragmentation during tandem MS. Known substrates of MMP-2 were identified thereby validating this technique with many novel MMP-2 substrates including the CX(3)CL1 chemokine fractalkine, osteopontin, galectin-1, and HSP90alpha also being identified and biochemically confirmed. In comparison with ICAT-labeling and quantitation, 8-9-fold more proteins and substrates were identified by iTRAQ. "Peptide mapping," the location of multiple peptides identified within a particular protein by iTRAQ in combination with their relative abundance ratios, enabled the domain shed and general location of the cleavage site to be identified in the native cellular substrate. Hence this advance in degradomics cell-based screens for native protein substrates casts new light on the roles for proteases in cell function.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".