Aggregation of Aβ40/42 chains in the presence of cyclic neuropeptides investigated by molecular dynamics simulations
Bibliographic record
Abstract
Alzheimer's disease is associated with the formation of toxic aggregates of amyloid beta (Aβ) peptides. Despite tremendous efforts, our understanding of the molecular mechanisms of aggregation, as well as cofactors that might influence it, remains incomplete. The small cyclic neuropeptide somatostatin-14 (SST14) was recently found to be the most selectively enriched protein in human frontal lobe extracts that binds Aβ42 aggregates. Furthermore, SST14's presence was also found to promote the formation of toxic Aβ42 oligomers in vitro. In order to elucidate how SST14 influences the onset of Aβ oligomerization, we performed all-atom molecular dynamics simulations of model mixtures of Aβ42 or Aβ40 peptides with SST14 molecules and analyzed the structure and dynamics of early-stage aggregates. For comparison we also analyzed the aggregation of Aβ42 in the presence of arginine vasopressin (AVP), a different cyclic neuropeptide. We observed the formation of self-assembled aggregates containing the Aβ chains and small cyclic peptides in all mixtures of Aβ42-SST14, Aβ42-AVP, and Aβ40-SST14. The Aβ42-SST14 mixtures were found to develop compact, dynamically stable, but small aggregates with the highest exposure of hydrophobic residues to the solvent. Differences in the morphology and dynamics of aggregates that comprise SST14 or AVP appear to reflect distinct (1) regions of the Aβ chains they interact with; (2) propensities to engage in hydrogen bonds with Aβ peptides; and (3) solvent exposures of hydrophilic and hydrophobic groups. The presence of SST14 was found to impede aggregation in the Aβ42-SST14 system despite a high hydrophobicity, producing a stronger "sticky surface" effect in the aggregates at the onset of Aβ42-SST14 oligomerization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".