Design and characterization of a novel human Granzyme B inhibitor
Bibliographic record
Abstract
The intracellular roles of Granzyme B (GrB) in immune-mediated cell killing have been extensively studied. Recent data also implicate GrB in extracellular pathways of inflammation, cytokine activation and autoimmunity. Targeting (GrB) provides a new pharmaceutical agent for various inflammatory disorders. Serpina3n is a mouse extracellular inhibitor of GrB. There is no apparent equivalent in humans. In this study, we used a novel applied genetics approach to engineer a new extracellular GrB serpin. A chimeric protein was generated in which the reactive center loop (RCL) of human extracellular antichymotrypsin (ACT) was replaced with that of serpina3n. This serpin contained 27 amino acid residues from the serpina3n RCL and the remaining 395 residues from human ACT. The insertion converted human ACT into a GrB-inhibitory serpin. Several critical residues were identified by scanning mutagenesis on the chimera and serpina3n. Targeted mutagenesis was conducted on wild-type human ACT by specifically substituting those critical residues, creating a novel inhibitor that contains 99.3% human ACT sequence with only three point mutations. Wild-type human ACT had a kass for GrB of 2.26 × 10(4) M(-1) s(-1), whereas the novel inhibitor binds GrB with a kass of 7.65 × 10(5) M(-1) s(-1). This new drug candidate can be developed in animal models and further tested in clinical trials to help us understand the role of GrB in numerous disorders.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".