A designed ankyrin-repeat protein that targets Parkinson’s disease-associated LRRK2
Bibliographic record
Abstract
Leucine rich repeat kinase 2 (LRRK2) is a large multidomain protein containing two catalytic domains, a kinase and a GTPase, as well as protein interactions domains, including a WD40 domain. The association of increased LRRK2 kinase activity with both the familial and sporadic forms of Parkinson's disease has led to an intense interest in determining its cellular function. However, small molecule probes that can bind to LRRK2 and report on or affect its cellular activity are needed. Here, we report the identification and characterization of the first high-affinity LRRK2-binding designed ankyrin-repeat protein (DARPin), named E11. Using cryo-EM, we show that DARPin E11 binds to the LRRK2 WD40 domain. LRRK2 bound to DARPin E11 showed improved behavior on cryo-EM grids, resulting in higher resolution LRRK2 structures. DARPin E11 did not affect the catalytic activity of a truncated form of LRRK2 in vitro but decreased the phosphorylation of Rab8A, a LRRK2 substrate, in cells. We also found that DARPin E11 disrupts the formation of microtubule-associated LRRK2 filaments in cells, which are known to require WD40-based dimerization. Thus, DARPin E11 is a new tool to explore the function and dysfunction of LRRK2 and guide the development of LRRK2 kinase inhibitors that target the WD40 domain instead of the kinase.
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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) | 0.001 | 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".