Characterization of Critical Residues of the Granzyme B Inhibitor, Serpina3n (50.43)
Bibliographic record
Abstract
Abstract Granzyme B (GrB) is crucial for the immune system in eliminating tumor and virus-infected cells by caspase-mediated apoptosis. GrB also has detrimental effects in allograft tissue rejection and autoimmune diseases. Our aim was to design effective GrB inhibitors as unique immunosuppressive drugs in treating these disorders. Our lab has defined a novel murine inhibitor of GrB named serpina3n (ser3n). Ser3n is a superior candidate for drug design since it is the only known GrB inhibitor that is secreted extracellularly, making it more plasma stable. We performed scanning mutagenesis of the reactive centre loop (RCL) domain, which confers serpins with their protease specificity, to test which residues have inhibitory properties. Using an in vitro transcribed/translated (IVTT) ser3n protein, we found that the putative P1 residue (Met) and the P3’ residue (Lys) are important for ser3n binding to GrB. Human antichymotrypsin (ACT) is highly homologous to ser3n, but is not a GrB inhibitor. Our goal was to generate a human secretory protein that inhibits GrB. We created an ACT-ser3n chimera by replacing the RCL of ACT with that of ser3n. We then created a mutant ACT-ser3n chimera with a Met-to-Asp mutation that resulted in greater GrB binding than the original chimera, to a degree equal to ser3n binding. This inhibitor will work on pathogenic states when secreted GrB is important. This reveals a potential drug for blocking GrB in autoimmune diseases.
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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".