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Record W1990849368 · doi:10.1074/jbc.m312611200

Ordered Conformational Changes in Damaged DNA Induced by Nucleotide Excision Repair Factors

2004· article· en· W1990849368 on OpenAlexafffund
Angels Tapias, Jérôme Auriol, Diane Forget, Jacqueline H. Enzlin, Orlando D. Schärer, Frédéric Coin, Benoit Coulombe, Jean‐Marc Egly

Bibliographic record

VenueJournal of Biological Chemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsNucleotide excision repairTranscription factor II HDNA damageDNADNA repairERCC1EndonucleaseBiologyCell biologyChemistryGenetics

Abstract

fetched live from OpenAlex

In response to genotoxic attacks, cells activate sophisticated DNA repair pathways such as nucleotide excision repair (NER), which consists of damage removal via dual incision and DNA resynthesis. Using permanganate footprinting as well as highly purified factors, we show that NER is a dynamic process that takes place in a number of successive steps during which the DNA is remodeled around the lesion in response to the various NER factors. XPC/HR23B first recognizes the damaged structure and initiates the opening of the helix from position -3 to +6. TFIIH is then recruited and, in the presence of ATP, extends the opening from position -6 to +6; it also displaces XPC downstream from the lesion, thereby providing the topological structure for recruiting XPA and RPA, which will enlarge the opening. Once targeted by XPG, the damaged DNA is further melted from position -19 to +8. XPG and XPF/ERCC1 endonucleases then cut the damaged DNA at the limit of the opened structure that was previously "labeled" by the positioning of XPC/HR23B and TFIIH.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.021
GPT teacher head0.254
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations158
Published2004
Admission routes2
Has abstractyes

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