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Record W2122501250 · doi:10.1139/o05-153

Structural basis for phosphorylation-dependent signaling in the DNA-damage response

2005· review· en· W2122501250 on OpenAlexaffvenue
R. Scott Williams, Nina Bernstein, Megan S. Lee, M.L. Rakovszky, Diana Cui, Ruth Green, Michael Weinfeld, J. N. Mark Glover

Bibliographic record

VenueBiochemistry and Cell Biology · 2005
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDNA damageBiologyDNA repairPhosphoserineCell biologyDNA repair protein XRCC4DNA ligasePhosphorylationBiochemistryXRCC1DNANucleotide excision repairSerineGene

Abstract

fetched live from OpenAlex

The response of eukaryotic cells to DNA damage requires a multitude of protein-protein interactions that mediate the ordered repair of the damage and the arrest of the cell cycle until repair is complete. Two conserved protein modules, BRCT and forkhead-associated (FHA) domains, play key roles in the DNA-damage response as recognition elements for nuclear Ser/Thr phosphorylation induced by DNA-damage-responsive kinases. BRCT domains, first identified at the C-terminus of BRCA1, often occur as multiple tandem repeats of individual BRCT modules. Our recent structural and functional work has revealed how BRCT repeats recognize phosphoserine protein targets. It has also revealed a secondary binding pocket at the interface between tandem repeats, which recognizes the amino-acid 3 residues C-terminal to the phosphoserine. We have also studied the molecular function of the FHA domain of the DNA repair enzyme, polynucleotide kinase (PNK). This domain interacts with threonine-phosphorylated XRCC1 and XRCC4, proteins responsible for the recruitment of PNK to sites of DNA-strand-break repair. Our studies have revealed a flexible mode of recognition that allows PNK to interact with numerous negatively charged substrates.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.288
Teacher spread0.268 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations35
Published2005
Admission routes2
Has abstractyes

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