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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".