In silico investigation of the interaction between α-synuclein aggregates and organic supramolecular assemblies
Bibliographic record
Abstract
α-synuclein (αSYN), and its tendancy to self-aggregate, plays an important role in the development of Parkinson’s disease (PD). αSYN aggregates are characterized by a stacking of αSYN chains and an interaction between the stackings to form dimers-like structures. The stability of those supramolecular assemblies are ensured by the pres- ence of numerous residues that adopt a "β-strand" and then "β-sheet" conformations, implying multiple interactions within and between the chains of αSYN. Following our previous study on the ability for small organic molecules to form columnar assemblies (organic nanotubes, ONs) [Phys. Chem. Chem. Phys. 2021], we propose here to unravel the ability of these ONs to interact with αSYN aggregates. More than an interaction, we expect the organic molecules to avoid the complete aggregation process (dimerization) and ideally to induce a destabilization of the stacking. Both molecular dynamics simulation and quantum mechanical-based calculations are used to identify the key parameters of the interaction and the resulting (de)stabilization of the assembly.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".