Selectivity profiles and substrate recognition of Rab-phosphorylating kinases
Bibliographic record
Abstract
The Rab GTPase switch-2 region is a hotspot for post-translational modifications. Its phosphorylation can determine whether individuals develop Parkinson's disease or not. Other modifications of the same region are catalyzed by enzymes from bacterial pathogens when they infect human cells. Here, we profiled a set of kinases including LRRK1, LRRK2, DYRK1A, MST1 and TBK1 for their capability of phosphorylating Rab GTPases. We identified several novel kinase:Rab pairs, such as LRRK1:Rab43 and TBK1:Rab29. Further, we comprehensively assessed what makes a Rab GTPase a good kinase substrate, considering the Rab nucleotide-binding state and the Rab primary sequence. In a systematic mutational study, Rab variants with modulated phosphorylation properties were established, leading to the identification of a LRRK2 recognition patch in the Rab α3 helix. A Glu to Arg exchange in that patch increased the phosphorylation 18-fold, indicating that Rabs are suboptimal LRRK2 substrates. Given that this effect is also observed in a cellular model, we propose that our variants will be excellent tools for analysing the physiological function of Rab phosphorylation.
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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".