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Record W2020815084 · doi:10.1073/pnas.0600538103

Repair of DNA–polypeptide crosslinks by human excision nuclease

2006· article· en· W2020815084 on OpenAlexfundno aff
Joyce T. Reardon, Aziz Sancar

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesYork UniversityUniversity of RochesterNational Institutes of HealthHarvard UniversityMemorial Sloan-Kettering Cancer Center
KeywordsNucleotide excision repairNucleaseDNADNA repairReplication protein ABase excision repairDNA ligaseDNA repair protein XRCC4BiochemistryMolecular biologyDDB1BiologyChemistryGeneDNA-binding protein

Abstract

fetched live from OpenAlex

DNA-protein crosslinks are relatively common DNA lesions that form during the physiological processing of DNA by replication and recombination proteins, by side reactions of base excision repair enzymes, and by cellular exposure to bifunctional DNA-damaging agents such as platinum compounds. The mechanism by which pathological DNA-protein crosslinks are repaired in humans is not known. In this study, we investigated the mechanism of recognition and repair of protein-DNA and oligopeptide-DNA crosslinks by the human excision nuclease. Under our assay conditions, the human nucleotide excision repair system did not remove a 16-kDa protein crosslinked to DNA at a detectable level. However, 4- and 12-aa-long oligopeptides crosslinked to the DNA backbone were recognized by some of the damage recognition factors of the human excision nuclease with moderate selectivity and were excised from DNA at relatively efficient rates. Our data suggest that, if coupled with proteolytic degradation of the crosslinked protein, the human excision nuclease may be the major enzyme system for eliminating protein-DNA crosslinks from the genome.

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 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 score0.009
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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.

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

Citations99
Published2006
Admission routes1
Has abstractyes

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