Probing oligomerization of amyloid beta peptide <i>in silico</i>
Bibliographic record
Abstract
Abstract Aggregation of amyloid β (Aβ) peptide is implicated in fatal Alzheimer's disease, for which no cure is available. Understanding the mechanisms responsible for this aggregation is required in order for therapies to be developed. In an effort to better understand the molecular mechanisms involved in spontaneous aggregation of Aβ peptide, extensive molecular dynamics simulations are reported, and the results are analyzed through a combination of structural biology tools and a novel essential collective dynamics method. Several model systems composed of ten or twelve Aβ17–42 chains in water are investigated, and the influence of metal ions is probed. The results suggest that Aβ monomers tend to aggregate into stable globular-like oligomers with 13–23% of β-sheet content. Two stages of oligomer formation have been identified: quick collapse within the first 40 ns of the simulation, characterized by a decrease in inter-chain separation and build-up of β-sheets, and the subsequent slow relaxation of the oligomer structure. The resulting oligomers comprise a stable, coherently moving sub-aggregate of 6–9 strongly inter-correlated chains. Cu2+ and Fe2+ ions have been found to develop coordination bonds with carboxylate groups of E22, D23 and A42, which remain stable during 200 ns simulations. The presence of Fe2+, and particularly Cu2+ ions, in negatively charged cavities has been found to cause significant changes in the structure and dynamics of the oligomers. The results indicate, in particular, that formation of non-fibrillar oligomers might be involved in early template-free aggregation of Aβ17–42 monomers, with charged species such as Cu2+ or Fe2+ ions playing an important role.
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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".