Two Ancient Bacterial-like PPP Family Phosphatases from Arabidopsis Are Highly Conserved Plant Proteins That Possess Unique Properties
Bibliographic record
Abstract
Protein phosphorylation, catalyzed by the opposing actions of protein kinases and phosphatases, is a cornerstone of cellular signaling and regulation. Since their discovery, protein phosphatases have emerged as highly regulated enzymes with specificity that rivals their counteracting kinase partners. However, despite years of focused characterization in mammalian and yeast systems, many protein phosphatases in plants remain poorly or incompletely characterized. Here, we describe a bioinformatic, biochemical, and cellular examination of an ancient, Bacterial-like subclass of the phosphoprotein phosphatase (PPP) family designated the Shewanella-like protein phosphatases (SLP phosphatases). The SLP phosphatase subcluster is highly conserved in all plants, mosses, and green algae, with members also found in select fungi, protists, and bacteria. As in other plant species, the nucleus-encoded Arabidopsis (Arabidopsis thaliana) SLP phosphatases (AtSLP1 and AtSLP2) lack genetic redundancy and phylogenetically cluster into two distinct groups that maintain different subcellular localizations, with SLP1 being chloroplastic and SLP2 being cytosolic. Using heterologously expressed and purified protein, the enzymatic properties of both AtSLP1 and AtSLP2 were examined, revealing unique metal cation preferences in addition to a complete insensitivity to the classic serine/threonine PPP protein phosphatase inhibitors okadaic acid and microcystin. The unique properties and high conservation of the plant SLP phosphatases, coupled to their exclusion from animals, red algae, cyanobacteria, archaea, and most bacteria, render understanding the function(s) of this new subclass of PPP family protein phosphatases of particular interest.
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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".