Essential collective dynamics analysis reveals nonlocal interactions of alpha-synuclein38-95 monomers with fibrillar seeds
Bibliographic record
Abstract
Alpha-synuclein (α-syn) is a cytoplasmic protein commonly found in the nervous system. In solution, α-syn adopts disordered unfolded conformations, although it can also form α-helices upon binding to lipid membranes. Under conditions that are not yet fully understood, α-syn can misfold and aggregate, giving rise to β-sheet rich amyloid fibrils, which then tend to accumulate in degenerating neurons. This leads to Parkinson’s disease (PD) and several other conditions collectively termed synucleinopathies. Development of disease-modifying treatments requires detailed understanding of structure and dynamics of α-syn’s misfolded aggregates. We have employed 1000 ns long, all-atom molecular dynamics simulations to investigate the interaction of monomeric α-syn38-95 fragments, which contain the most important amyloidogenic regions, with preformed fibrillar seeds composed of staggered, β-sheet rich α-syn chains of matching length. The simulations indicate that α-syn38-95 monomers tend to form aggregates with the fibrillar seeds, although we have not observed alignment of the monomeric chains with β-strands of the fibril. To analyze the stability of these aggregates, we have employed the essential collective dynamics method, which allows making accurate assessment of dynamical coupling across individual atoms in macromolecules and supramolecular complexes. The analysis revealed extensive dynamical coupling across initially monomeric α-syn chains and the fibrillar seeds including distal regions thereof that did not contact the monomer directly. We have discussed structural origins of these long-range interactions, their impacts for the stability of α-syn aggregates, and potential implications for the development of anti-PD treatments.
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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".