Membrane Protease Degradomics: Proteomic Identification and Quantification of Cell Surface Protease Substrates
Bibliographic record
Abstract
The modification of cell surface proteins by plasma membrane and soluble proteases is important for physiological and pathological processes. Methods to identify shed and soluble substrates are crucial to further define the substrate repertoire, termed the substrate degradome, of individual proteases. Identifying protease substrates is essential to elucidate protease function and involvement in different homeostatic and disease pathways. This characterisation is also crucial for drug target identification and validation, which would then allow the rational design of specific targeted inhibitors for therapeutic intervention. We describe two methods for identifying and quantifying shed cell surface protease targets in cultured cells utilising Isotope-Coded Affinity Tags (ICAT) and Isobaric Tags for Relative and Absolute Quantification (iTRAQ). As a model system to develop these techniques, we chose a cell-membrane expressed matrix metalloproteinase, MMP-14, but the concepts can be applied to proteases of other classes. By over-expression, or conversely inhibition, of a particular protease with careful selection of control conditions (e.g. vector or inactive protease) and differential labelling, shed proteins can be identified and quantified by mass spectrometry (MS), MS/MS fragmentation and database searching.
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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".