Understanding the coupling between DNA damage detection and UvrA's ATPase using bulk and single molecule kinetics
Bibliographic record
Abstract
ABSTRACT Nucleotide excision repair (NER) protects cells against diverse types of DNA damage, principally UV irradiation. In Escherichia coli , damage is recognized by 2 key enzymes: UvrA and UvrB. Despite extensive investigation, the role of UvrA's 2 ATPase domains in NER remains elusive. Combining single‐molecule fluorescence microscopy and classic biochemical methods, we have investigated the role of nucleotide binding in UvrA's kinetic cycle. Measurement of UvrA's steady‐state ATPase activity shows it is stimulated upon binding DNA ( k cat 0.71–1.07/s). Despite UvrA's ability to discriminate damage, we find UV‐damaged DNA does not alter the steady‐state ATPase. To understand how damage affects UvrA, we studied its binding to DNA under various nucleotide conditions at the single molecule level. We have found that both UV damage and nucleotide cofactors affect the attached lifetime of UvrA. In the presence of ATP and UV damage, the lifetime is significantly greater compared with undamaged DNA. To reconcile these observations, we suggest that UvrA uses negative cooperativity between its ATPase sites that is gated by damage recognition. Only in the presence of damage is the second site activated, most likely in a sequential manner.—Barnett, J. T., Kad, N. M. Understanding the coupling between DNA damage detection and UvrA's ATPase using bulk and single molecule kinetics. FASEB J. 33, 763–769 (2019). www.fasebj.org
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".