Binding Mechanism of Inositol Stereoisomers to Monomers and Aggregates of Aβ(16-22)
Bibliographic record
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disease with no cure. A potential therapeutic approach is to prevent or reverse the amyloid formation of Aβ42, a key pathological hallmark of AD. We examine the molecular basis for stereochemistry-dependent inhibition of the formation of Aβ fibrils in vitro by a polyol, scyllo-inositol. We present molecular dynamics simulations of the monomeric, disordered aggregate, and protofibrillar states of Aβ(16-22), an amyloid-forming peptide fragment of full-length Aβ, successively with and without scyllo-inositol and its inactive stereoisomer chiro-inositol. Both stereoisomers bind monomers and disordered aggregates with similar affinities of 10-120 mM, whereas binding to β-sheet-containing protofibrils yields affinities of 0.2-0.5 mM commensurate with in vitro inhibitory concentrations of scyllo-inositol. Moreover, scyllo-inositol displays a higher binding specificity for phenylalanine-lined grooves on the protofibril surface, suggesting that scyllo-inositol coats the surface of Aβ protofibrils and disrupts their lateral stacking into amyloid fibrils.
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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".