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Record W4360828195 · doi:10.1126/sciadv.adf7175

Clinical PARP inhibitors allosterically induce PARP2 retention on DNA

2023· article· en· W4360828195 on OpenAlexafffund
Marie-France Langelier, Xiaohui Lin, Shan Zha, John M. Pascal

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversité de Montréal
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsAllosteric regulationPARP1DNA damagePoly ADP ribose polymeraseDNADNA repairChemistryCancer researchBiologyCell biologyBiochemistryEnzymePolymerase

Abstract

fetched live from OpenAlex

PARP1 and PARP2 detect DNA breaks, which activates their catalytic production of poly(ADP-ribose) that recruits repair factors and contributes to PARP1/2 release from DNA. PARP inhibitors (PARPi) are used in cancer treatment and target PARP1/2 catalytic activity, interfering with repair and increasing PARP1/2 persistence on DNA damage. In addition, certain PARPi exert allosteric effects that increase PARP1 retention on DNA. However, no clinical PARPi exhibit this allosteric behavior toward PARP1. In contrast, we show that certain clinical PARPi exhibit an allosteric effect that retains PARP2 on DNA breaks in a manner that depends on communication between the catalytic and DNA binding regions. Using a PARP2 mutant that mimics an allosteric inhibitor effect, we observed increased PARP2 retention at cellular damage sites. The PARPi AZD5305 also exhibited a clear reverse allosteric effect on PARP2. Our results can help explain the toxicity of clinical PARPi and suggest ways to improve PARPi moving forward.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.084
GPT teacher head0.424
Teacher spread0.341 · 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

Citations49
Published2023
Admission routes2
Has abstractyes

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