Conformational dynamics of α-synuclein and study of its intramolecular forces in the presence of selected compounds
Bibliographic record
Abstract
Protein misfolding and aggregation play crucial roles in amyloidogenic diseases through the self-assembly of intrinsically disordered proteins (IDPs) in type II diabetes (T2D), Alzheimer's disease (AD) and Parkinson's disease (PD). PD is the most common neurodegenerative disorder after AD, and is associated with the loss of dopaminergic signaling, which causes motor and nonmotor signs and symptoms. Lewy bodies and Lewy neurites are common pathological hallmarks of PD that are mainly composed of aggregates of disordered α-synuclein (α-Syn). There have been many efforts to develop chemical compounds to prevent aggregation or facilitate disruption of the aggregates. Furthermore, the roles and interactions of many compounds have yet to be revealed at the atomistic level, especially their impacts on the dynamics and chain-chain interactions of the oligomers, which are of interest in this study. The conformational diversity and detailed interactions among homo-oligomer chains of α-Syn are not fully discovered; identifying these might help uncover a practical approach to developing a potent therapy. In this study, we used an in-silico investigation to address the conformational diversity of α-Syn oligomer. The roles of several point mutations in protein aggregation in PD are known; we take this further by evaluating the interaction energies and contributions of all residues in stability and residue-chain interactions. In this study, we docked chemical derivatives of three compounds with high drug-likeness properties to evaluate the roles of our ligands in the conformational dynamicity of the oligomers, with emphasis on intramolecular forces. Free energy evaluation of the modeled inter and intramolecular interactions through MD simulation shows effective interaction and binding between α-Syn and our compounds. However, we find that they do not significantly disrupt the chain-chain interactions, compared to unliganded simulation.
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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) | 0.001 | 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".