Functional Divergence and Evolutionary Turnover in Mammalian Phosphoproteomes
Bibliographic record
Abstract
Protein phosphorylation is a key mechanism to regulate protein functions. However, the contribution of this protein modification to species divergence is still largely unknown. Here, we studied the evolution of mammalian phosphoregulation by comparing the human and mouse phosphoproteomes. We found that 84% of the positions that are phosphorylated in one species or the other are conserved at the residue level. Twenty percent of these conserved sites are phosphorylated in both species. This proportion is 2.5 times more than expected by chance alone, suggesting that purifying selection is preserving phosphoregulation. However, we show that the majority of the sites that are conserved at the residue level are differentially phosphorylated between species. These sites likely result from false-negative identifications due to incomplete experimental coverage, false-positive identifications and non-functional sites. In addition, our results suggest that at least 5% of them are likely to be true differentially phosphorylated sites and may thus contribute to the divergence in phosphorylation networks between mouse and humans and this, despite residue conservation between orthologous proteins. We also showed that evolutionary turnover of phosphosites at adjacent positions (in a distance range of up to 40 amino acids) in human or mouse leads to an over estimation of the divergence in phosphoregulation between these two species. These sites tend to be phosphorylated by the same kinases, supporting the hypothesis that they are functionally redundant. Our results support the hypothesis that the evolutionary turnover of phosphorylation sites contributes to the divergence in phosphorylation profiles while preserving phosphoregulation. Overall, our study provides advanced analyses of mammalian phosphoproteomes and a framework for the study of their contribution to phenotypic evolution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".