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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".