Metabolic Labeling of Leucine Rich Repeat Kinases 1 and 2 with Radioactive Phosphate
Bibliographic record
Abstract
Leucine rich repeat kinases 1 and 2 (LRRK1 and LRRK2) are paralogs which share a similar domain organization, including a serine-threonine kinase domain, a Ras of complex proteins domain (ROC), a C-terminal of ROC domain (COR), and leucine-rich and ankyrin-like repeats at the N-terminus. The precise cellular roles of LRRK1 and LRRK2 have yet to be elucidated, however LRRK1 has been implicated in tyrosine kinase receptor signaling, while LRRK2 is implicated in the pathogenesis of Parkinson's disease. In this report, we present a protocol to label the LRRK1 and LRRK2 proteins in cells with (32)P orthophosphate, thereby providing a means to measure the overall phosphorylation levels of these 2 proteins in cells. In brief, affinity tagged LRRK proteins are expressed in HEK293T cells which are exposed to medium containing (32)P-orthophosphate. The (32)P-orthophosphate is assimilated by the cells after only a few hours of incubation and all molecules in the cell containing phosphates are thereby radioactively labeled. Via the affinity tag (3xflag) the LRRK proteins are isolated from other cellular components by immunoprecipitation. Immunoprecipitates are then separated via SDS-PAGE, blotted to PVDF membranes and analysis of the incorporated phosphates is performed by autoradiography ((32)P signal) and western detection (protein signal) of the proteins on the blots. The protocol can readily be adapted to monitor phosphorylation of any other protein that can be expressed in cells and isolated by immunoprecipitation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".