An evolutionary proteomics approach identifies substrates of the cAMP-dependent protein kinase
Bibliographic record
Abstract
Protein kinases are important mediators of much of the signal transduction that occurs in eukaryotic cells. Unfortunately, the identification of protein kinase substrates has proven to be a difficult task, and we generally know few, if any, of the physiologically relevant targets of any particular kinase. Here, we describe a sequence-based approach that simplified this substrate identification process for the cAMP-dependent protein kinase (PKA) in Saccharomyces cerevisiae. In this method, the evolutionary conservation of all PKA consensus sites in the S. cerevisiae proteome was systematically assessed within a group of related yeasts. The basic premise was that a higher degree of conservation would identify those sites that are functional in vivo. This method identified 44 candidate PKA substrates, 5 of which had been described. A phosphorylation analysis showed that all of the identified candidates were phosphorylated by PKA and that the likelihood of phosphorylation was strongly correlated with the degree of target site conservation. Finally, as proof of principle, the activity of one particular target, Atg1, a key regulator of autophagy, was shown to be controlled by PKA phosphorylation in vivo. These data therefore suggest that this evolutionary proteomics approach identified a number of PKA substrates that had not been uncovered by other methods. Moreover, these data show how this approach could be generally used to identify the physiologically relevant occurrences of any protein motif identified in a eukaryotic proteome.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".