Binding of Inositol Stereoisomers To Model Amyloidogenic Peptides
Bibliographic record
Abstract
The self-aggregation of proteins into amyloid fibrils is a pathological hallmark of numerous incurable diseases such as Alzheimer's disease. scyllo-Inositol is a stereochemistry-dependent in vitro inhibitor of amyloid formation. As the first step to elucidate its mechanism of action, we present molecular dynamics simulations of scyllo-inositol and its inactive stereoisomer, chiro-inositol, with simple peptide models, alanine dipeptide (ADP) and (Gly-Ala)(4). We characterize molecular interactions and compute equilibrium binding constants between inositol and ADP as well as, successively, monomers, amorphous aggregates, and fibril-like β-sheet aggregates of (Gly-Ala)(4). Inositol interacts weakly with all peptide systems considered, with millimolar to molar affinities, and displaces the conformational equilibria of ADP but not of the (Gly-Ala)(4) systems. However, scyllo- and chiro-inositol adopt different binding modes on the surface of β-sheet aggregates. These results suggest that inositol does not inhibit amyloid formation by breaking up preformed aggregates but rather by binding to the surface of prefibrillar aggregates.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".