The origin and radiation of the phosphoprotein phosphatase (PPP) enzymes of Eukaryotes
Bibliographic record
Abstract
Phosphoprotein phosphatase (PPP) enzymes are ubiquitous proteins involved in cellular signaling pathways and other functions. Here we have traced the origin of the PPP sequences of Eukaryotes and their radiation. Using a bacterial PPP Hidden Markov Model (HMM) we uncovered "BacterialPPP-Like" sequences in Archaea. A HMM derived from eukaryotic PPP enzymes revealed additional, unique sequences in Archaea and Bacteria that were more like the eukaryotic PPP enzymes then the bacterial PPPs. These sequences formed the basis of phylogenetic tree inference and sequence structural analysis allowing the history of these sequence types to be elucidated. Our phylogenetic tree data strongly suggest that eukaryotic PPPs ultimately arose from ancestors in the Asgard archaea. We have clarified the radiation of PPPs within Eukaryotes, substantially expanding the range of known organisms with PPP subtypes (Bsu1, PP7, PPEF/RdgC) previously thought to have a more restricted distribution. Surprisingly, sequences from the Methanosarcinaceae (Euryarchaeota) form a strongly supported sister group to eukaryotic PPPs in our phylogenetic analysis. This strongly suggests an intimate association between an Asgard ancestor and that of the Methanosarcinaceae. This is highly reminiscent of the syntrophic association recently demonstrated between the cultured Lokiarchaeal species Prometheoarchaeum and a methanogenic bacterial species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".