Metadegradomics
Bibliographic record
Abstract
Post-translational modifications enable extra layers of control of the proteome, and perhaps the most important is proteolysis, a major irreversible modification affecting every protein. The intersection of the protease web with a proteome sculpts that proteome, dynamically modifying its state and function. Protease expression is distorted in cancer, so perturbing signaling pathways and the secretome of the tumor and reactive stromal cells. Indeed many cancer biomarkers are stable proteolytic fragments. It is crucial to determine which proteases contribute to the pathology versus their roles in homeostasis and in mitigating cancer. Thus the full substrate repertoire of a protease, termed the substrate degradome, must be deciphered to define protease function and to identify drug targets. Degradomics has been used to identify many substrates of matrix metalloproteinases that are important proteases in cancer. Here we review recent degradomics technologies that allow for the broadly applicable identification and quantification of proteases (the protease degradome) and their activity state, substrates, and interactors. Quantitative proteomics using stable isotope labeling, such as ICAT, isobaric tags for relative and absolute quantification (iTRAQ), and stable isotope labeling by amino acids in cell culture (SILAC), can reveal protease substrates by taking advantage of the natural compartmentalization of membrane proteins that are shed into the extracellular space. Identifying the actual cleavage sites in a complex proteome relies on positional proteomics and utilizes selection strategies to enrich for protease-generated neo-N termini of proteins. In so doing, important functional information is generated. Finally protease substrates and interactors can be identified by interactomics based on affinity purification of protease complexes using exosite scanning and inactive catalytic domain capture strategies followed by mass spectrometry analysis. At the global level, the N terminome analysis of whole communities of proteases in tissues and organs in vivo provides a full scale understanding of the protease web and the web-sculpted proteome, so defining metadegradomics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".