Development of α-Helical Calpain Probes by Mimicking a Natural Protein–Protein Interaction
Bibliographic record
Abstract
We have designed a highly specific inhibitor of calpain by mimicking a natural protein-protein interaction between calpain and its endogenous inhibitor calpastatin. To enable this goal we established a new method of stabilizing an α-helix in a small peptide by screening 24 commercially available cross-linkers for successful cysteine alkylation in a model peptide sequence. The effects of cross-linking on the α-helicity of selected peptides were examined by CD and NMR spectroscopy, and revealed structurally rigid cross-linkers to be the best at stabilizing α-helices. We applied this strategy to the design of inhibitors of calpain that are based on calpastatin, an intrinsically unstable polypeptide that becomes structured upon binding to the enzyme. A two-turn α-helix that binds proximal to the active site cleft was stabilized, resulting in a potent and selective inhibitor for calpain. We further expanded the utility of this inhibitor by developing irreversible calpain family activity-based probes (ABPs), which retained the specificity of the stabilized helical inhibitor. We believe the inhibitor and ABPs will be useful for future investigation of calpains, while the cross-linking technique will enable exploration of other protein-protein interactions.
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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 | 0.001 | 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".