Degradomics technologies in matrisome exploration
Bibliographic record
Abstract
Consisting of a defined set of extracellular proteins secreted from resident cells and with minor contributions from serum proteins, the extracellular matrix (ECM) is an essential component of all tissues. Maintaining tissue homeostasis, structural support and cellular control through cell-ECM communication, the ECM has come to be viewed as not just a passive structural entity but rather as a dynamic signaling conduit between cells and the extracellular compartment. Proteins and their cleavage products mediate this communication, and aberrant signaling, either directly or indirectly distorting the ECM, results in pathological conditions including cancer, inflammation, fibrosis, and neurodegenerative diseases. Characterization of ECM components, the matrisome, the extracellular environment and their changes in disease is therefore of importance to understand and mitigate by developing novel therapeutics. Liquid chromatography-mass spectrometry (LC-MS) proteomics has been integral to protein and proteome research for decades and long superseded the obsolescent gel-based approaches. A continuous effort has ensured progress with increased sensitivity and throughput as more advanced equipment has been developed hand in hand with specialized enrichment, detection, and identification methods. Part of this effort lies in the field of degradomics, a branch of proteomics focused on discovering novel protease substrates by identification of protease-generated neo-N termini, the N-terminome, and characterizing the responsible protease networks. Various methods to do so have been developed, some specialized for specific tissue types, others for particular proteases, throughput, or ease of use. This review aims to provide an overview of the state-of-the-art proteomics techniques that have successfully been recently utilized to characterize proteolytic cleavages in the ECM and thereby guided new research and understanding of the ECM and matrisome biology.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".