Activation and autoregulation of DNA-PK from structured single-stranded DNA and coding end hairpins
Bibliographic record
Abstract
DNA-dependent protein kinase (DNA-PK) acts through an essential relationship with DNA to participate in the regulation of multiple cellular processes. Yet the role of DNA as a cofactor in kinase activity remains to be completely elucidated. For example, although DNA-PK activity appears to be required for the resolution of hairpin coding ends in variable diversity joining recombination, kinase activity remains to be demonstrated from hairpin ends or other DNA structures. In the present study we report that DNA-PK is strongly activated from hairpin ends and structured single-stranded DNA, but that the phosphorylation of many heterologous substrates is blocked efficiently by inactivation of the kinase through autophosphorylation. However, substrates that bound efficiently to single-stranded DNA such as p53 and replication protein A were efficiently phosphorylated by DNA-PK from structured DNA. DNA-PK also was found to be active toward heterologous substrates from hairpin ends on double-stranded DNA under conditions where autophosphorylation was minimized. These results suggest that the role of DNA-PK in resolving coding end hairpins is likely to be enzymatic rather than structural, expand understanding of how DNA-PK binding to structured DNA relates to enzyme activity, and suggest a mechanism for autoregulatory control of its kinase activity in the cell.
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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) | 0.001 | 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".