Cell-Based Identification of Natural Substrates and Cleavage Sites for Extracellular Proteases by SILAC Proteomics
Bibliographic record
Abstract
Proteolysis is one of the most important post-translational modifications of the proteome with every protein undergoing proteolysis during its synthesis and maturation and then upon inactivation and degradation. Extracellular proteolysis can either activate or inactivate bioactive molecules regulating physiological and pathological processes. Therefore, it is important to develop non-biased high-content screens capable of identifying the substrates for a specific protease. This characterization can also be useful for identifying the nodes of intersection between a protease and cellular pathways and so aid in the detection of drug targets. Classically, biochemical methods for protease substrate screening only discover what can be cleaved but this is often not what is actually cleaved in vivo. We suggest that biologically relevant protease substrates can be best found by analysis of proteolysis in a living cellular context, starting with a proteome that has never been exposed to the activity of the examined protease. Therefore, protease knockout cells form a convenient and powerful system for these screens. We describe a method for identification and quantification of shed and secreted cleaved substrates in cell cultures utilizing the cell metabolism as a labelling system. SILAC (stable isotope labelling by amino acids) utilises metabolic incorporation of stable isotope-labelled amino acids into living cells. As a model system to develop this approach, we chose the well-characterised matrix metalloproteinase, MMP-2, because of its importance in tumour metastasis and a large database of MMP substrates with which to benchmark this new approach. However, the concepts can be applied to any extracellular or cell membrane protease. Generating differential metabolically labelled proteomes is one key to the approach; the other is the use of a negative peptide selection procedure to select for cleaved N-termini in the N-terminome. Using proteomes exposed or not to a particular protease enables biologically relevant substrates and their cleavage sites to be identified and quantified by tandem mass spectrometry proteomics 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 | 0.000 | 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 | 0.000 | 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".