Spatial Exclusivity Combined with Positive and Negative Selection of Phosphorylation Motifs Is the Basis for Context-Dependent Mitotic Signaling
Bibliographic record
Abstract
The timing and localization of events during mitosis are controlled by the regulated phosphorylation of proteins by the mitotic kinases, which include Aurora A, Aurora B, Nek2 (never in mitosis kinase 2), Plk1 (Polo-like kinase 1), and the cyclin-dependent kinase complex Cdk1/cyclin B. Although mitotic kinases can have overlapping subcellular localizations, each kinase appears to phosphorylate its substrates on distinct sites. To gain insight into the relative importance of local sequence context in kinase selectivity, identify previously unknown substrates of these five mitotic kinases, and explore potential mechanisms for substrate discrimination, we determined the optimal substrate motifs of these major mitotic kinases by positional scanning oriented peptide library screening (PS-OPLS). We verified individual motifs with in vitro peptide kinetic studies and used structural modeling to rationalize the kinase-specific selection of key motif-determining residues at the molecular level. Cross comparisons among the phosphorylation site selectivity motifs of these kinases revealed an evolutionarily conserved mutual exclusion mechanism in which the positively and negatively selected portions of the phosphorylation motifs of mitotic kinases, together with their subcellular localizations, result in proper substrate targeting in a coordinated manner during mitosis.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".