High-resolution NMR characterization of low abundance oligomers of amyloid-β without purification
Bibliographic record
Abstract
Alzheimer's disease is characterized by the misfolding and self-assembly of the amyloidogenic protein amyloid-β (Aβ). The aggregation of Aβ leads to diverse oligomeric states, each of which may be potential targets for intervention. Obtaining insight into Aβ oligomers at the atomic level has been a major challenge to most techniques. Here, we use magic angle spinning recoupling (1)H-(1)H NMR experiments to overcome many of these limitations. Using (1)H-(1)H dipolar couplings as a NMR spectral filter to remove both high and low molecular weight species, we provide atomic-level characterization of a non-fibrillar aggregation product of the Aβ1-40 peptide using non-frozen samples without isotopic labeling. Importantly, this spectral filter allows the detection of the specific oligomer signal without a separate purification procedure. In comparison to other solid-state NMR techniques, the experiment is extraordinarily selective and sensitive. A resolved 2D spectra could be acquired of a small population of oligomers (6 micrograms, 7% of the total) amongst a much larger population of monomers and fibers (93% of the total). By coupling real-time (1)H-(1)H NMR experiments with other biophysical measurements, we show that a stable, primarily disordered Aβ1-40 oligomer 5-15 nm in diameter can form and coexist in parallel with the well-known cross-β-sheet 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".