Quantitative characterization of protein–protein complexes involved in base excision DNA repair
Bibliographic record
Abstract
Base Excision Repair (BER) efficiently corrects the most common types of DNA damage in mammalian cells. Step-by-step coordination of BER is facilitated by multiple interactions between enzymes and accessory proteins involved. Here we characterize quantitatively a number of complexes formed by DNA polymerase β (Polβ), apurinic/apyrimidinic endonuclease 1 (APE1), poly(ADP-ribose) polymerase 1 (PARP1), X-ray repair cross-complementing protein 1 (XRCC1) and tyrosyl-DNA phosphodiesterase 1 (TDP1), using fluorescence- and light scattering-based techniques. Direct physical interactions between the APE1-Polβ, APE1-TDP1, APE1-PARP1 and Polβ-TDP1 pairs have been detected and characterized for the first time. The combined results provide strong evidence that the most stable complex is formed between XRCC1 and Polβ. Model DNA intermediates of BER are shown to induce significant rearrangement of the Polβ complexes with XRCC1 and PARP1, while having no detectable influence on the protein-protein binding affinities. The strength of APE1 interaction with Polβ, XRCC1 and PARP1 is revealed to be modulated by BER intermediates to different extents, depending on the type of DNA damage. The affinity of APE1 for Polβ is higher in the complex with abasic site-containing DNA than after the APE1-catalyzed incision. Our findings advance understanding of the molecular mechanisms underlying coordination and regulation of the BER process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".