Identification of Arabidopsis Protein Kinases That Harbor Functional Type 1 Peroxisomal Targeting Signals
Bibliographic record
Abstract
Peroxisomes are eukaryotic specific organelles that perform diverse metabolic functions including fatty acid β-oxidation, reactive species metabolism, photorespiration, and responses to stress. However, the potential regulation of these functions by post-translational modifications, including protein phosphorylation, has had limited study. Recently, we identified and catalogued a large number of peroxisomal phosphorylated proteins, implicating the presence of protein kinases in this organelle. Here, we employed available prediction models coupled with sequence conservation analysis to identify 31 protein kinases from the Arabidopsis kinome (all protein kinases) that contain a putative, non-canonical peroxisomal targeting signal type 1 (PTS1). From this, twelve C-terminal domain-PTS1s were demonstrated to be functional in vivo, targeting enhanced yellow fluorescent protein to peroxisomes, increasing the list of presumptive peroxisomal protein kinases to nineteen. Of the twelve protein kinases with functional PTS1s, we obtained full length clones for eight and demonstrated that seven target to peroxisomes in vivo. Screening homozygous mutants of the presumptive nineteen protein kinases revealed one candidate (GPK1) that harbors a sugar-dependence phenotype, suggesting it is involved in regulating peroxisomal fatty acid β-oxidation. These results present new opportunities for investigating the regulation of peroxisome functions.
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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 | 0.001 |
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".