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Record W4378894923 · doi:10.1126/sciadv.adg2838

Cryo-EM visualization of DNA-PKcs structural intermediates in NHEJ

2023· article· en· W4378894923 on OpenAlexaff
Siyu Chen, Alex Vogt, Linda Lee, Tasmin Naila, Ryan McKeown, Alan E. Tomkinson, Susan P. Lees‐Miller, Yuan He

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of Calgary
FundersBiological and Environmental ResearchNational Institute of General Medical SciencesRobert H. Lurie Comprehensive Cancer CenterOffice of ScienceNational Cancer InstituteNational Institutes of HealthNorthwestern UniversityOregon Health and Science UniversityU.S. Department of Energy
KeywordsDNAVisualizationDNA-PKcsComputational biologyComputer scienceBiologyGeneticsDNA repairArtificial intelligence

Abstract

fetched live from OpenAlex

DNA double-strand breaks (DSBs), one of the most cytotoxic forms of DNA damage, can be repaired by the tightly regulated nonhomologous end joining (NHEJ) machinery (Stinson and Loparo and Zhao et al. ). Core NHEJ factors form an initial long-range (LR) synaptic complex that transitions into a DNA-PKcs (DNA-dependent protein kinase, catalytic subunit)–free, short-range state to align the DSB ends (Chen et al. ). Using single-particle cryo–electron microscopy, we have visualized three additional key NHEJ complexes representing different transition states, with DNA-PKcs adopting distinct dimeric conformations within each of them. Upon DNA-PKcs autophosphorylation, the LR complex undergoes a substantial conformational change, with both Ku and DNA-PKcs rotating outward to promote DNA break exposure and DNA-PKcs dissociation. We also captured a dimeric state of catalytically inactive DNA-PKcs, which resembles structures of other PIKK (Phosphatidylinositol 3-kinase-related kinase) family kinases, revealing a model of the full regulatory cycle of DNA-PKcs during NHEJ.

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)0.000
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 score0.007
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.307
Teacher spread0.299 · 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

Citations29
Published2023
Admission routes1
Has abstractyes

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