Characterization of the human proliferating cell nuclear antigen physico-chemical properties: aspects of double trimer stability
Bibliographic record
Abstract
Its toroidal structure allows the proliferating cell nuclear antigen (PCNA) to wrap around and move along the DNA fiber, thereby dramatically increasing the processivity of DNA polymerization. PCNA is also involved in the regulation of a wide spectrum of other biological functions, including epigenetic inheritance. We have recently reported that mammalian PCNA forms a double trimer complex, which may be critically important in coordinating DNA replication and other cellular functions. To gain a better understanding of the stability of PCNA complexes, we characterized the physico-chemical properties of the PCNA structure by in vivo and in vitro approaches. The data obtained by gel filtration and nondenaturing gel electrophoresis of native PCNA molecules confirm our previous observations, obtained using formaldehyde crosslinking, in which PCNA exists in the cell as a double trimer. We have also found that optimal pH (pH 6.5-7.5) is critical for the stability of the PCNA structure. The presence or absence of ATP, dithiothreitol, and Mg2+ does not affect the stability of the PCNA trimer or double trimer. However, 0.02% SDS can effectively inhibit PCNA double trimer, but not single trimer, formation. Interestingly, glycerol and ammonium sulfate significantly destabilize both PCNA trimer and double trimer structures.
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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 | 0.000 |
| 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".