Identification of allosteric inhibitors against caspase-6 activity in Alzheimer's disease
Bibliographic record
Abstract
Knock-in transgenic mouse models and human post-mortem studies have demonstrated Caspase-6 (Casp6) activity to be involved in both age-dependent cognitive impairment and Alzheimer disease, suggesting Casp6 as a potential therapeutic target. Although Casp6 inhibitors have been identified, many of them are non-selective. Here, we show that by using a novel human genetic approach, we identified a non-conserved allosteric pocket that bears favorable tertiary architecture for accommodating small molecules. An in silico screen of 77,000 diverse molecules against this pocket identified fifty-four potential Casp6 inhibitors. This thesis will focus on the fifteen hits identified from the Sigma-Aldrich commercial library. In vitro screening of these compounds identified compound 10 and its analogues as the most potent and selective Casp6 inhibitors. In addition, kinetic analyses show that these compounds inhibit Casp6 through a non-competitive mode of inhibition. Furthermore, these compounds are non-toxic and reduce Casp6 activity in HCT116 cells, whereas they induce cytotoxicity in HEK293T cells. Finally, extensive compound 10 analogue screens have identified compound 10P as our hit compound that could potentially be used as a starting point for medicinal chemistry for the optimization of a more potent, selective and non-toxic allosteric inhibitor. Together, not only do these findings identify a novel class of Casp6 inhibitors, but also validate a novel approach for the discovery of allosteric sites for other drug targets.
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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) | 0.002 | 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".