N‐terminomics and proteomics analysis of Calpain‐2 reveal key proteolytic processing of metabolic and cell adhesion proteins
Bibliographic record
Abstract
Aberrant levels of the cysteine protease Calpain-2 have been linked to neurodegeneration, inflammation, and cancer, yet our understanding of this protease and its substrates remains limited. Systematic studies to identify Calpain-2 substrates have been largely confined to peptide libraries or in vitro studies, which fail to represent physiological cellular conditions and physiologically relevant substrates. To identify existing and novel Calpain-2 substrates, we used a genetic approach to knockout Calpain-2 in the THP-1 human monocyte-like cells, followed by proteomic and N-terminomic/TAILS mass spectrometry approaches to identify Calpain-2 substrates. We identified 51 substrates that may be cleaved directly by Calpain-2 or indirectly by downstream proteases. The direct cleavage of selected substrates by Calpain-2 was confirmed using in vitro assays. Finally, metabolomics analysis identified a role for Calpain-2 in the regulation of pyrimidine and glutathione metabolism. Our unbiased and quantitative mass spectrometry analytical pipeline provides new evidence on the physiological functions of Calpain-2 and its newly identified substrates in THP-1 cells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".