Unraveling the Complexities of DNA-Dependent Protein Kinase Autophosphorylation
Bibliographic record
Abstract
DNA-dependent protein kinase (DNA-PK) orchestrates DNA repair by regulating access to breaks through autophosphorylations within two clusters of sites (ABCDE and PQR). Blocking ABCDE phosphorylation (by alanine mutation) imparts a dominant negative effect, rendering cells hypersensitive to agents that cause DNA double-strand breaks. Here, a mutational approach is used to address the mechanistic basis of this dominant negative effect. Blocking ABCDE phosphorylation hypersensitizes cells to most types of DNA damage (base damage, cross-links, breaks, and damage induced by replication stress), suggesting that DNA-PK binds DNA ends that result from many DNA lesions and that blocking ABCDE phosphorylation sequesters these DNA ends from other repair pathways. This dominant negative effect requires DNA-PK's catalytic activity, as well as phosphorylation of multiple (non-ABCDE) DNA-PK catalytic subunit (DNA-PKcs) sites. PSIPRED analysis indicates that the ABCDE sites are located in the only contiguous extended region of this huge protein that is predicted to be disordered, suggesting a regulatory role(s) and perhaps explaining the large impact ABCDE phosphorylation has on the enzyme's function. Moreover, additional sites in this disordered region contribute to the ABCDE cluster. These data, coupled with recent structural data, suggest a model whereby early phosphorylations promote initiation of nonhomologous end joining (NHEJ), whereas ABCDE phosphorylations, potentially located in a "hinge" region between the two domains, lead to regulated conformational changes that initially promote NHEJ and eventually disengage NHEJ.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".