Comparative Analysis Reveals Conserved Protein Phosphorylation Networks Implicated in Multiple Diseases
Bibliographic record
Abstract
Protein kinases enable cellular information processing. Although numerous human phosphorylation sites and their dynamics have been characterized, the evolutionary history and physiological importance of many signaling events remain unknown. Using target phosphoproteomes determined with a similar experimental and computational pipeline, we investigated the conservation of human phosphorylation events in distantly related model organisms (fly, worm, and yeast). With a sequence-alignment approach, we identified 479 phosphorylation events in 344 human proteins that appear to be positionally conserved over approximately 600 million years of evolution and hence are likely to be involved in fundamental cellular processes. This sequence-alignment analysis suggested that many phosphorylation sites evolve rapidly and therefore do not display strong evolutionary conservation in terms of sequence position in distantly related organisms. Thus, we devised a network-alignment approach to reconstruct conserved kinase-substrate networks, which identified 778 phosphorylation events in 698 human proteins. Both methods identified proteins tightly regulated by phosphorylation as well as signal integration hubs, and both types of phosphoproteins were enriched in proteins encoded by disease-associated genes. We analyzed the cellular functions and structural relationships for these conserved signaling events, noting the incomplete nature of current phosphoproteomes. Assessing phosphorylation conservation at both site and network levels proved useful for exploring both fast-evolving and ancient signaling events. We reveal that multiple complex diseases seem to converge within the conserved networks, suggesting that disease development might rely on common molecular networks.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".