Protein mechanism and therapeutic design in Parkinson's disease: A structural biology perspective
Bibliographic record
Abstract
Parkinson's disease (PD) remains one of the most elusive, progressive neurological diseases to treat due to an incomplete understanding of its pathology. Current symptomatic therapies revolve around alleviating symptoms with dopamine replacement therapy; however, this mode of treatment does not always provide long-term relief or address the underlying cause. Thus, there is still a need to better understand the mechanisms of proteins implicated in neurodegeneration as the key to developing disease-modifying treatments. Here we discuss recent advances in our understanding of six protein targets for PD therapy: α-synuclein, LRRK2, GBA1, PARKIN, PINK1, and USP30. For each, we highlight novel structures that shine light both on pathogenic mechanisms as well as novel therapies. We discuss drugs targeting these proteins that are in clinical trials, and how structures are used to improve them. • Structural biology of α-synuclein, LRRK2, GBA1, and PINK1/PARKIN enhances our understanding of Parkinson’s disease pathology. • Structures enable design of small molecules and antibodies as potential disease-modifying treatments. • Integrating structural biology and genetics accelerates precision therapies for diverse Parkinson’s disease subtypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".