Structure-Based Peptide Design to Modulate Amyloid Beta Aggregation and Reduce Cytotoxicity
Bibliographic record
Abstract
The deposition of Aβ peptide in the brain is the key event in Alzheimer disease progression. Therefore, the prevention of Aβ self assembly into disease-associated oligomers is a logical strategy for treatment. π stacking is known to provide structural stability to many amyloids; two phenylalanine residues within the Aβ 14-23 self recognition element are in such an arrangement in many solved structures. Therefore, we targeted this structural stacking by substituting these two phenylalanine residues with their D-enantiomers. The resulting peptides were able to modulate Aβ aggregation in vitro and reduce Aβ cytotoxicity in primary neuronal cultures. Using kinetic analysis of fibril formation, electron microscopy and dynamic light scattering characterization of oligomer size distributions, we demonstrate that, in addition to altering fibril structural characteristics, these peptides can induce the formation of larger amorphous aggregates which are protective against toxic oligomers, possibly because they are able to sequester the toxic oligomers during co-incubation. Alternatively, they may alter the surface structure of the oligomers such that they can no longer interact with cells to induce toxic pathways.
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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 | 0.000 |
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".