Prolyl Oligopeptidase Enhances α-Synuclein Dimerization via Direct Protein-Protein Interaction
Bibliographic record
Abstract
Prolyl oligopeptidase (PREP) accelerates the aggregation of α-synuclein (aSyn), a key protein involved in development of Parkinson disease and other synucleinopathies. PREP inhibitors reduce aSyn aggregation, but the mechanism has remained unknown. We have now used protein-fragment complementation assays (PCA) and microscale thermophoresis in parallel to show that PREP interacts directly with aSyn in both intact cells and in a cell-free system. Using split luciferase-based PCA, we first showed that PREP enhances the formation of soluble aSyn dimers in live Neuro-2A neuroblastoma cells. A PREP inhibitor, KYP-2047, reduced aSyn dimerization in PREP-expressing cells but not in cells lacking PREP expression. aSyn dimerization was also enhanced by PREP(S554A), an enzymatically inactive PREP mutant, but this was not affected by KYP-2047. PCA and microscale thermophoresis studies showed that aSyn interacts with both PREP and PREP(S554A) with low micromolar affinity. Neither the proline-rich, C-terminal domain of aSyn nor the hydrolytic activity of PREP was required for the interaction with PREP. Our results show that PREP binds directly to aSyn to enhance its dimerization and may thus serve as a nucleation point for aSyn aggregation. Native gel analysis showed that KYP-2047 shifts PREP to a compact monomeric form with reduced ability to promote aSyn nucleation. As PREP inhibition also enhances autophagic clearance of aSyn, PREP inhibitors may reduce accumulation of aSyn inclusions via a dual mechanism and are thus a novel therapeutic candidate for synucleinopathies. Our results also suggest that PREP has other cellular functions in addition to its peptidase activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 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 teacher head, 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".