<i>In Silico</i> Screening for Small Molecules to Alter Calpain Proteolysis through Modulating Conformation Changes Induced by Heterodimerization
Bibliographic record
Abstract
Dysregulated calpain-1 and calpain-2 protease activity linked to several diseases has encouraged efforts to explore inhibiting calpain to provide therapeutic benefits. However, there are currently no clinically approved drugs that specifically target calpain functionality. To address this unmet need, we carried out in silico drug discovery efforts to identify small molecules capable of modulating calpain activity. Our approach is based on the observation that heterodimer formation of the calpain-2 catalytic (CAPN2) and regulatory (CAPNS1) subunits is needed for both proteolytic activity and CAPN2 stability. In recognition of this obligate protein–protein interaction (PPI), the CAPN2–CAPNS1 interface was targeted with nearly 3.6 million small molecules to find candidates that bind at the interface with high affinity and introduce steric clashes capable of altering heterodimerization or the conformation of CAPN2, thereby modulating proteolytic activity. Twenty small molecules predicted to disrupt the most hydrogen bonds at the CAPN2–CAPNS1 interface were validated experimentally. Five small molecules inhibited calpain activity by 53.6 ± 4.1, 36.8 ± 38.3, 31.1 ± 17.5, 69.8 ± 27.3, and 47.1 ± 18.5%, while two enhanced protease activity by 163.0 ± 41.9 and 129.2 ± 11.9%. Unexpectedly, the effects of these seven molecules on the CAPN2–CAPNS1 PPI assay did not correlate with their effects on protease activity. Molecular simulation showed that small molecules that modulate calpain activity without abolishing heterodimerization do so by altering the conformational changes needed for the activity. This apparent allosteric mechanism paves the way for developing novel therapeutic solutions for modulating the calpain activity in various diseases associated with calpain dysregulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".