The Landscape of <i>Parkin</i> Variants Reveals Pathogenic Mechanisms and Therapeutic Targets in Parkinson’s Disease
Bibliographic record
Abstract
Abstract Mutations in Parkin ( PARK2 ), which encodes an E3 ubiquitin ligase implicated in mitophagy, are the most common cause of early onset Parkinson’s Disease (PD). Hundreds of naturally occurring Parkin variants have been reported, both in PD patient and population databases. However, the effects of the majority of these variants on the function of Parkin and in PD pathogenesis remains unknown. Here we develop a framework for classification of the pathogenicity of Parkin variants based on the integration of clinical and functional evidence – including measures of mitophagy and protein stability, and predictive structural modeling – and assess 51 naturally occurring Parkin variants accordingly. Surprisingly, only a minority of Parkin variants, even among those previously associated with PD, disrupted Parkin function. Moreover, a few of these naturally occurring Parkin variants actually enhanced mitophagy. Interestingly, impaired mitophagy in several of the most common pathogenic Parkin variants could be rescued both by naturally-occurring (p.V224A) and structure-guided designer (p.W403A; p.F146A) hyperactive Parkin variants. Together, the findings provide a coherent framework to classify Parkin variants based on pathogenicity and suggest that several pathogenic Parkin variants represent promising targets to stratify patients for genotype-specific drug design.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".