Aggregation process of A<i>β</i><sub>1-40</sub>with non-A<i>β</i>amyloid component of<i>α</i>-synuclein
Bibliographic record
Abstract
Many neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, are characterized by the presence of amyloid fibers. Recently, attention has turned from the fibers to the early stages of oligomerization where toxicity could be highest. Here, we focus on the interactions between non-A β amyloid component of a-synuclein (NAC) and A β 1-40 , two proteins found in amyloid fibrils associated with Alzheimer's disease. We combine the coarse-grained OPEP potential with a Hamiltonian and temperature replica exchange molecular dynamics simulation (HT-REMD) to identify mechanisms leading to the formation of secondary structures promoting fibrillation. We observe that the propensity to form beta-sheet remains the same for A β 1-40 whereas is decreases significantly for NAC. In particular, the 25-35 region of A β 1-40 is a significant area of secondary structure stabilization with NAC. The ionic interactions between salt-bridge D23 and K28 in A β 1-40 and K20 and E23 in NAC of the heterogeneous dimer are consistent with the salt-bridges found in NAC and A β 1-40 homogenous dimers and allow us to see that these interactions don't necessarily dominate the interchain stabilizations. Our numerical simulation also show the formation of interaction between the early oligomer of NAC and A β 1-40 .
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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".