<i>trans</i> Autophosphorylation at DNA-Dependent Protein Kinase's Two Major Autophosphorylation Site Clusters Facilitates End Processing but Not End Joining
Bibliographic record
Abstract
Recent studies have established that DNA-dependent protein kinase (DNA-PK) undergoes a series of autophosphorylation events that facilitate successful completion of nonhomologous DNA end joining. Autophosphorylation at sites in two distinct clusters regulates DNA end access to DNA end-processing factors and to other DNA repair pathways. Autophosphorylation within the kinase's activation loop regulates kinase activity. Additional autophosphorylation events (as yet undefined) occur that mediate kinase dissociation. Here we provide the first evidence that autophosphorylation within the two major clusters (regulating end access) occurs in trans. Further, both UV-induced and double-strand break (DSB)-induced phosphorylation in the two major clusters is predominantly autophosphorylation. Finally, we show that while autophosphorylation in trans on one of two synapsed DNA-PK complexes facilitates appropriate end processing, this is not sufficient to promote efficient end joining. This suggests that end joining in living cells requires additional phosphorylation events that either occur in cis or that occur on both sides of the DNA-PK synapse. These data support an emerging consensus that, via a series of autophosphorylation events, DNA-PK undergoes a sequence of conformational changes that promote efficient and appropriate repair of DSBs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".