Early oligomerization stages for the non-amyloid component of α-synuclein amyloid
Bibliographic record
Abstract
In recent years, much effort has focused on the early stages of aggregation and the formation of amyloid oligomers. Aggregation processes for these proteins are complex and their non-equilibrium nature makes any experimental study very difficult. Under these conditions, simulations provide a useful alternative for understanding the dynamics of the early stages of oligomerization. Here, we focus on the non-Aβ amyloid component (NAC) of the monomer, dimer, and trimer of α-synuclein, an important 35-residue sequence involved in the aggregation and fibrillation of this protein associated with Parkinson's disease. Using Hamiltonian and temperature replica exchange molecular dynamics simulations combined with the coarse grained Optimized Potential for Efficient peptide structure Prediction potential, we identify the role of the various regions and the secondary structures for the onset of oligomerization. For this sequence, we clearly observe the passage from α-helix to β-sheet, a characteristic transition of amyloid proteins. More precisely, we find that the NAC monomer is highly structured with two α-helical regions, between residues 2-13 and 19-25. As the dimer and trimer form, β-sheet structures between residues 2-14 and 26-34 appear and rapidly structure the system. The resulting conformations are much more structured than similar dimers and trimers of β-amyloid and amylin proteins and yet display a strong polymorphism at these early stages of aggregation. In addition to its inherent experimental interest, comparison with other sequences shows that NAC could be a very useful numerical model for understanding the onset of aggregation.
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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".