Quantitative MS-based enzymology of caspases reveals distinct protein substrate specificities, hierarchies, and cellular roles
Bibliographic record
Abstract
Proteases constitute the largest enzyme family, yet their biological roles are obscured by our rudimentary understanding of their cellular substrates. There are 12 human caspases that play crucial roles in inflammation and cell differentiation and drive the terminal stages of cell death. Recent N-terminomics technologies have begun to enumerate the diverse substrates individual caspases can cleave in complex cell lysates. It is clear that many caspases have shared substrates; however, few data exist about the catalytic efficiencies (kcat/KM) of these substrates, which is critical to understanding their true substrate preferences. In this study, we use quantitative MS to determine the catalytic efficiencies for hundreds of natural protease substrates in cellular lysate for two understudied members: caspase-2 and caspase-6. Most substrates are new, and the cleavage rates vary up to 500-fold. We compare the cleavage rates for common substrates with those found for caspase-3, caspase-7, and caspase-8, involved in apoptosis. There is little correlation in catalytic efficiencies among the five caspases, suggesting each has a unique set of preferred substrates, and thus more specialized roles than previously understood. We synthesized peptide substrates on the basis of protein cleavage sites and found similar catalytic efficiencies between the protein and peptide substrates. These data suggest the rates of proteolysis are dominated more by local primary sequence, and less by the tertiary protein fold. Our studies highlight that global quantitative rate analysis for posttranslational modification enzymes in complex milieus for native substrates is critical to better define their functions and relative sequence of events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".