Membrane protease proteomics: Isotope-coded affinity tag MS identification of undescribed MT1–matrix metalloproteinase substrates
Bibliographic record
Abstract
By proteolytic modification of low abundant signaling proteins and membrane receptors, proteases exert potent posttranslational control over cell behavior at the postsecretion level. Hence, substrate discovery is indispensable for understanding the biological role of proteases in vivo. Indeed, matrix metalloproteinases (MMPs), long associated with extracellular matrix degradation, are increasingly recognized as important processing enzymes of bioactive molecules. MS is now the primary proteomic technique for detecting, identifying, and quantitating proteins in cells or tissues. Here we used isotopecoded affinity tag labeling and multidimensional liquid chromatography inline with tandem MS to identify MDA-MB-231 breast carcinoma cell proteins shed from the cell surface or the pericellular matrix and extracellular proteins that were degraded or processed after transfection with human membrane type 1-MMP (MT1-MMP). Potential substrates were identified as those having altered protein levels compared with the E240A inactive MT1-MMP mutant or vector transfectants. New substrates were biochemically confirmed by matrix-assisted laser desorption ionization-time-of-flight MS and Edman sequencing of cleavage fragments after incubation with recombinant soluble MT1-MMP in vitro. We report many previously uncharacterized substrates of MT1-MMP, including the neutrophil chemokine IL-8, secretory leukocyte protease inhibitor, pro-tumor necrosis factor alpha, death receptor-6, and connective tissue growth factor, indicating that MT1-MMP is an important signaling protease in addition to its traditionally ascribed roles in pericellular matrix remodeling. Moreover, the high-throughput and quantitative nature of isotope-coded affinity tag labeling combined with tandem MS sequencing is a previously undescribed degradomic screen for protease substrate discovery that should be generally adaptable to other classes of protease for exploring proteolytic function in complex and dynamic biological contexts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".