Domain analysis of PNKP–XRCC1 interactions: Influence of genetic variants of XRCC1
Bibliographic record
Abstract
Polynucleotide kinase/phosphatase (PNKP) and X-ray repair cross-complementing 1 (XRCC1) are key proteins in the single-strand DNA break repair pathway. Phosphorylated XRCC1 stimulates PNKP by binding to its forkhead-associated (FHA) domain, whereas nonphosphorylated XRCC1 stimulates PNKP by interacting with the PNKP catalytic domain. Here, we have further studied the interactions between these two proteins, including two variants of XRCC1 (R194W and R280H) arising from single-nucleotide polymorphisms (SNPs) that have been associated with elevated cancer risk in some reports. We observed that interaction of the PNKP FHA domain with phosphorylated XRCC1 extends beyond the immediate, well-characterized phosphorylated region of XRCC1 (residues 515-526). We also found that an XRCC1 fragment, comprising residues 166-436, binds tightly to PNKP and DNA and efficiently activates PNKP's kinase activity. However, interaction of either of the SNP-derived variants of this fragment with PNKP was considerably weaker, and their stimulation of PNKP was severely reduced, although they still could bind DNA effectively. Laser microirradiation revealed reduced recruitment of PNKP to damaged DNA in cells expressing either XRCC1 variant compared with PNKP recruitment in cells expressing WT XRCC1 even though WT and variant XRCC1s were equally efficient at localizing to the damaged DNA. These findings suggest that the elevated risk of cancer associated with these XRCC1 SNPs reported in some studies may be due in part to the reduced ability of these XRCC1 variants to recruit PNKP to damaged DNA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".