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Record W4294897419 · doi:10.1126/sciadv.abq0414

A two-step mechanism governing PARP1-DNA retention by PARP inhibitors

2022· article· en· W4294897419 on OpenAlexaff
Amit Bhardwaj, Yandong Yin, Carel Fijen, Anastasiya Ephstein, Lianglin Zhang, Xia Ding, John M. Pascal, Todd VanArsdale, Eli Rothenberg

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversité de Montréal
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Cancer Institute
KeywordsPARP1Mechanism (biology)Poly ADP ribose polymeraseDNAChemistryComputational biologyBiophysicsCell biologyComputer scienceBiologyBiochemistryPolymerasePhysics

Abstract

fetched live from OpenAlex

PARP inhibitors (PARPi) have emerged as promising cancer therapeutics capable of targeting specific DNA repair pathways, but their mechanism of action with respect to PARP1-DNA retention remains unclear. Here, we developed single-molecule assays to directly monitor the retention of PARP1 on DNA lesions in real time. Our study reveals a two-step mechanism by which PARPi modulate the retention of PARP1 on DNA lesions, consisting of a primary step of catalytic inhibition via binding competition with NAD + followed by an allosteric modulation of bound PARPi. While clinically relevant PARPi exhibit distinct allosteric modulation activities that can either increase retention of PARP1 on DNA or induce its release, their retention potencies are predominantly determined by their ability to outcompete NAD + binding. These findings provide a mechanistic basis for improved PARPi selection according to their characteristic activities and enable further development of more potent inhibitors.

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 score0.310
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.309
Teacher spread0.292 · 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

Citations48
Published2022
Admission routes1
Has abstractyes

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