Rational design of structure‐based vaccines targeting misfolded alpha‐synuclein conformers of Parkinson's disease and related disorders
Bibliographic record
Abstract
Synucleinopathies, including Parkinson's disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB), are neurodegenerative disorders caused by the accumulation of misfolded alpha-synuclein protein. Developing effective vaccines against synucleinopathies is challenging due to the difficulty of stimulating an immune-specific response against alpha-synuclein without causing harmful autoimmune reactions, selectively targeting only pathological forms of alpha-synuclein. Previous attempts using linear peptides and epitopes without control of the antigen structure failed in clinical trials. The immune system was unable to distinguish between native alpha-synuclein and its amyloid form. The prion domain of the fungal HET-s protein was selected as a scaffold to introduce select epitopes from the surface of alpha-synuclein fibrils. Four vaccine candidates were generated by introducing specific amino acid substitutions onto the surface of the scaffold protein. The approach successfully mimicked the stacking of the parallel in-register beta-sheet structure seen in alpha-synuclein fibrils. All vaccine candidates induced substantial levels of IgG antibodies that recognized pathological alpha-synuclein fibrils derived from a synucleinopathy mouse model. Furthermore, the antisera recognized pathological alpha-synuclein aggregates in brain lysates from patients who died from DLB, MSA, or PD, but did not recognize linear alpha-synuclein peptides. Our approach, based on the rational design of vaccines using the structure of alpha-synuclein amyloid fibrils and strict control over the exposed antigen structure used for immunization, as well as the ability to mimic aggregated alpha-synuclein, provides a promising avenue toward developing effective vaccines against alpha-synuclein fibrils.
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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 | 0.000 |
| 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".