Identification of Cellular MMP Substrates Using Quantitative Proteomics: Isotope-Coded Affinity Tags (ICAT) and Isobaric Tags for Relative and Absolute Quantification (iTRAQ)
Bibliographic record
Abstract
Identification of protease substrates is essential to understand the functional consequences of normal proteolytic processing and dysregulated proteolysis in disease. Quantitative proteomics and mass spectrometry can be used to identify protease substrates in the cellular context. Here we describe the use of two protein labeling techniques, Isotope-Coded Affinity Tags (ICAT and Isobaric Tags for Relative and Absolute Quantification (iTRAQ), which we have used successfully to identify novel matrix metalloproteinase (MMP) substrates in cell culture systems (1-4). ICAT and iTRAQ can label proteins and protease cleavage products of secreted proteins, protein domains shed from the cell membrane or pericellular matrix of protease-transfected cells that have accumulated in conditioned medium, or cell surface proteins in membrane preparations; isotopically distinct labels are used for control cells. Tryptic digestion and tandem mass spectrometry of the generated fragments enable sequencing of differentially labeled but otherwise identical pooled peptides. The isotopic tag, which is unique for each label, identifies the peptides originating from each sample, for instance, protease-transfected or control cells, and comparison of the peak areas enables relative quantification of the peptide in each sample. Thus proteins present in altered amounts between protease-expressing and null cells are implicated as protease substrates and can be further validated as such.
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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 | 0.002 | 0.001 |
| 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".