Conformational Dynamics of α-Synuclein: A Study of its Intramolecular Forces in the Presence of Selected Compounds
Bibliographic record
Abstract
Abstract Protein misfolding and aggregation play a role in amyloidogenic diseases through the self-assembly of intrinsically disordered proteins (IDPs) in type II diabetes (T2D), Alzheimer's (AD) and Parkinson's (PD) diseases. PD is the most common neurodegenerative disorder after AD, known for the loss of dopaminergic signaling, which causes motor and non-motor signs and symptoms. Lewy bodies and Lewy neurites are common pathological hallmarks of PD that are mainly composed of an aggregate of the disordered protein, α-synuclein (α-Syn). There have been many efforts to develop chemical-based compounds to prevent aggregation or facilitate disruption of the fibrils. These have been tested in wet labs, but most fail to generate a robust impact. Further, the atomistic roles and interactions of such compounds have yet to be revealed. The conformational diversity and detailed interactions among homo-oligomer chains of α-Syn are also unknown; identifying these might help uncover a practical approach to developing a potent therapy. In this study, we use an in-silico investigation to address the conformational diversity of α-Syn oligomers. The roles of several point mutations in protein aggregation in PD are known; we take this further by evaluating the interactional energies and contributions of all residues in stability and chain–chain interactions. We dock three chemical derivatives of known compounds with high-score drug-likeness to evaluate the roles of our ligands in the conformational dynamicity of the oligomers, with emphasis on intramolecular forces. Preventing fibril formations is a heated topic in this area. Free energy evaluation of the modeled inter- and intramolecular interactions through MD simulation shows strong binding between α-Syn compounds. However, we find that they do not disrupt or even weaken the interactions, and in some cases, they contributed to boosting interactions between oligomer chains.
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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 | 0.001 |
| 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".