DNA damage repair – investigating the conformations of DNA ligase and PCNA
Bibliographic record
Abstract
DNA damage repair is a central pillar of genomic integrity and all DNA repair pathways include DNA Ligase carrying out a 3-step reaction.Damaged DNA is not only produced from exogenous sources but also arises from normal cellular activities like genome replication.During replication, the lagging strand is synthesized as Okazaki fragments that require DNA Ligase to function efficiently.The trimeric proliferating cell nuclear antigen (PCNA) orchestrates DNA Ligase with nucleases and polymerases through their PCNA interacting peptide (PIP) motif.The PIP motif of DNA Ligase I is found in its unstructured N-terminus.An N-terminal truncation led to the crystal structure of DNA Ligase I in complex with nicked DNA.However, questions remain on Ligase domain rearrangements throughout the three steps of ligation and on its interactions with PCNA.The hyperthermophilic archaeon Sulfolobus solfataricus provides a convenient model system of the DNA repair machinery found in all three branches of life.ssLigase lacks the unstructured N-terminus of human DNA Ligase I. Using a purification strategy that exploits the thermostability of the archaeal proteins, we purified S. solfataricus heterotrimeric PCNA and Ligase.ssPCNA was shown to stimulate ligation and co-eluted with ssLigase over gel filtration chromatography.We were able to visualize the ssLigase-ssPCNA-DNA complex by negative-stain electron microscopy (EM), providing insights into PCNA interaction.Using cross-linking strategies, we are pursuing a high resolution structure of this complex by cryo-EM.In parallel, we have initiated co-crystallization of ssLigase with DNA as well as the ssLigase-ssPCNA-DNA complex.With these two approaches, we will combine the atomic precision of X-ray crystallography with the ensemble domain dynamics of cryo-EM to obtain a comprehensive understanding of the final step of DNA damage repair.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".