The substrate quality of CK2 target sites has a determinant role on their function and evolution
Bibliographic record
Abstract
Abstract Most biological processes are regulated by peptide-recognition modules (PRMs) that bind to short linear motifs (SLiMs). Such interactions are rapidly reversible and often occur at low affinity. The protein kinase domain represents one such binding module, and known substrates may have full or only partial matches to the kinase recognition motif, a property known as ‘substrate quality’. However, it is not yet clear whether differences in substrate quality represent neutral variation along the phosphosite sequence or if these differences have functional consequences that are subject to selection. We explore this question in detail for the acidophilic kinase CK2. CK2 is well-characterised, clinically important, and a fundamental enzyme for many aspects of cell biology. We show that optimal CK2 sites are phosphorylated at maximal stoichiometries and found in many conditions whereas minimal substrates are phosphorylated at lower stoichiometries, are more dynamic during the cell cycle, and have regulatory functions. Optimal CK2 sites also tend to be older and more conserved than minimal sites, and evolutionary simulations indicate that the substrate quality of CK2 phosphosites is often tuned by selection. For intermediate target sites, increases or decreases to substrate quality may be deleterious, which we demonstrate experimentally for a CK2 substrate at the kinetochore. The results together suggest that minimal and optimal phosphosites are strongly differentiated in terms of their functional and evolutionary properties.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".