The many faces of alpha synuclein: from phospholipid bilayer interactions to amyloid aggregation
Bibliographic record
Abstract
Alpha synuclein (αS) is a ~14 kDa intrinsically disordered protein with a yet unknown physiological function. The conversion of monomeric αS into amyloid aggregates is believed to play a central role of the pathology of Parkinson’s disease (PD). Despite extensive studies on amyloid formation of αS in bulk solution, the mechanistic details of αS aggregation at biological interfaces like lipid membranes are unclear. Further, it is also unknown how amyloid aggregates that are formed in PD potentiate neuronal cell death. Association with (specific) cellular membranes is believed facilitate amyloid formation, but this hypothesis is clouded by the fact that the physiological function of αS probably also involves association with physiological cell membranes. Therefore, understanding interactions of αS with lipid membranes is critical to uncover its possible functional or pathological role, which we have investigated in this thesis. In particular, we focus on the following questions which form the core of the thesis: * How are physical properties of phospholipid membranes affected by αS binding and aggregation and vice versa? * How do early amyloid aggregates of αS perturb phospholipid membranes? * What is the role of N-terminal acetylation in αS on its membrane binding properties and aggregation propensities? * How do terminal domains in αS affect the morphology of amyloid aggregates? To answer these questions a wide range of biochemical and biophysical techniques like fluorescence confocal microscopy, fluorescence anisotropy, circular dichroism, Fourier transform infra-red spectroscopy and fluorescence recovery after photobleaching were used to probe the interactions of lipid membranes with monomeric αS. Our results show the importance of inter-αS, inter-lipid and αS–lipid interactions that are involved both prior to and post amyloid formation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".