Structural Mechanism of the Interaction of Alzheimer Disease Aβ Fibrils with the Non-steroidal Anti-inflammatory Drug (NSAID) Sulindac Sulfide
Bibliographic record
Abstract
Alzheimer disease is the most severe neurodegenerative disease worldwide. In the past years, a plethora of small molecules interfering with amyloid-β (Aβ) aggregation has been reported. However, their mode of interaction with amyloid fibers is not understood. Non-steroidal anti-inflammatory drugs (NSAIDs) are known γ-secretase modulators; they influence Aβ populations. It has been suggested that NSAIDs are pleiotrophic and can interact with more than one pathomechanism. Here we present a magic angle spinning solid-state NMR study demonstrating that the NSAID sulindac sulfide interacts specifically with Alzheimer disease Aβ fibrils. We find that sulindac sulfide does not induce drastic architectural changes in the fibrillar structure but intercalates between the two β-strands of the amyloid fibril and binds to hydrophobic cavities, which are found consistently in all analyzed structures. The characteristic Asp(23)-Lys(28) salt bridge is not affected upon interacting with sulindac sulfide. The primary binding site is located in the vicinity of residue Gly(33), a residue involved in Met(35) oxidation. The results presented here will assist the search for pharmacologically active molecules that can potentially be employed as lead structures to guide the design of small molecules for the treatment of Alzheimer disease.
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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".