Structural Basis for Proteolytic Specificity of the Human Apoptosis-Inducing Granzyme M
Bibliographic record
Abstract
Granzyme M (GzmM), a unique serine protease constitutively expressed in NK cells, is important for granule-mediated cytolysis and can induce rapid caspase-dependent apoptosis of tumor cells. However, few substrates of GzmM have been reported to date, and the mechanism by which this enzyme recognizes and hydrolyzes substrates is unknown. To provide structural insights into the proteolytic specificity of human GzmM (hGzmM), crystal structures of wild-type hGzmM, the inactive D86N-GzmM mutant with bound peptide substrate, and the complexes with a catalytic product and with a tetrapeptide chloromethylketone inhibitor were solved to 1.96 A, 2.30 A, 2.17 A and 2.70 A, respectively. Structure-based mutagenesis revealed that the N terminus and catalytic triad of hGzmM are most essential for proteolytic function. In particular, D86N-GzmM was found to be an ideal inactive enzyme for functional studies. Structural comparisons indicated a large conformational change of the L3 loop upon substrate binding, and suggest this loop mediates the substrate specificity of hGzmM. Based on the complex structure of GzmM with its catalytic product, a tetrapeptide chloromethylketone inhibitor was designed and found to specifically block the catalytic activity of hGzmM.
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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) | 0.000 | 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) | 0.001 | 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".