Where Do Phosphosites Come from and Where Do They Go after Gene Duplication?
Bibliographic record
Abstract
Gene duplication followed by divergence is an important mechanism that leads to molecular innovation. Divergence of paralogous genes can be achieved at functional and regulatory levels. Whereas regulatory divergence at the transcriptional level is well documented, little is known about divergence of posttranslational modifications (PTMs). Protein phosphorylation, one of the most important PTMs, has recently been shown to be an important determinant of the retention of paralogous genes. Here we test whether gains and losses of phosphorylated amino acids after gene duplication may specifically modify the regulation of these duplicated proteins. We show that when phosphosites are lost in one paralog, transitions from phosphorylated serines and threonines are significantly biased toward negatively charged amino acids, which can mimic their phosphorylated status in a constitutive manner. Our analyses support the hypothesis that divergence between paralogs can be generated by a loss of the posttranslational regulatory control on a function rather than by the complete loss of the function itself. Surprisingly, these favoured transitions cannot be reached by single mutational steps, which suggests that the function of a phosphosite needs to be completely abolished before it is restored through substitution by these phosphomimetic residues. We conclude by discussing how gene duplication could facilitate the transitions between phosphorylated and phosphomimetic amino acids.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".