Protein abundance is key to distinguish promiscuous from functional phosphorylation based on evolutionary information
Bibliographic record
Abstract
In eukaryotic cells, protein phosphorylation is an important and widespread mechanism used to regulate protein function. Yet, of the thousands of phosphosites identified to date, only a few hundred at best have a characterized function. It was recently shown that these functional sites are significantly more conserved than phosphosites of unknown function, stressing the importance of considering evolutionary conservation in assessing the global functional landscape of phosphosites. This leads us to review studies that examined the impact of phosphorylation on evolutionary conservation. While all these studies have shown that conservation is greater among phosphorylated sites compared with non-phosphorylated ones, the magnitude of this difference varies greatly. Further, not all studies have considered key factors that may influence the rate of phosphosite evolution. Such key factors are their localization in ordered or disordered regions, their stoichiometry or the abundance of their corresponding protein. Here we take into account all of these factors simultaneously, which reveals remarkable evolutionary patterns. First, while it is well established that protein conservation increases with abundance, we show that phosphosites partly follow an opposite trend. More precisely, Saccharomyces cerevisiae phosphosites present among abundant proteins are 1.5 times more likely to diverge in the closely related species Saccharomyces bayanus when compared with phosphosites present in the 5 per cent least abundant proteins. Second, we show that conservation is coupled to stoichiometry, whereby sites frequently phosphorylated are more conserved than those rarely phosphorylated. Finally, we provide a model of functional and noisy or 'accidental' phosphorylation that explains these observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".