Conformational Dynamics of α-Synuclein during the Interaction with Phospholipid Nanodiscs by Millisecond Hydrogen–Deuterium Exchange Mass Spectrometry
Bibliographic record
Abstract
Both normal and pathological functions of α-synuclein (αSN), an abundant protein in the central and peripheral nervous system, have been linked to its interaction with membrane lipid bilayers. The ability to characterize structural transitions of αSN upon membrane complexation will clarify molecular mechanisms associated with αSN-linked pathologies, including Parkinson's disease (PD), multiple systems atrophy, and other synucleinopathies. In this work, time-resolved electrospray ionization hydrogen/deuterium exchange mass spectrometry (TRESI-HDX-MS) was employed to acquire a detailed picture of αSN's conformational transitions as it undergoes complexation with nanodisc membrane mimics with different headgroup charges (zwitterionic DMPC and negative POPG). Using this approach, αSN interactions with DMPC nanodiscs were shown to be rapid exchanging and to have little impact on the αSN conformational ensemble. Interactions with nanodiscs containing lipids known to promote amyloidogenesis (e.g., POPG), on the other hand, were observed to induce substantial and specific changes in the αSN conformational ensemble. Ultimately, we identify a region corresponding residues 19-28 and 45-57 of the αSN sequence that is uniquely impacted by interactions with "amyloidogenic" lipid membranes, supporting the existing "broken-helix" model for α-synuclein/membrane interactions, but do not detect a "helical extension" that is also thought to play a role in αSN 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".