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Record W1970758107 · doi:10.1586/epr.11.63

Mass spectrometry-based functional proteomics of poly(ADP-ribose) polymerase-1

2011· review· en· W1970758107 on OpenAlexafffund
Émilie Pic, Jean‐Philippe Gagné, Guy G. Poirier

Bibliographic record

VenueExpert Review of Proteomics · 2011
Typereview
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersCanadian Institutes of Health Research
KeywordsPoly ADP ribose polymeraseChromatinProteomicsProteomeBiologyComputational biologyCell biologyChromatin remodelingDNA repairStable isotope labeling by amino acids in cell cultureDNA damagePolymeraseGeneticsDNAGene

Abstract

fetched live from OpenAlex

PARP-1 is an abundant nuclear protein that plays an essential role in the regulation of many genome integrity and chromatin-based processes, such as DNA repair, replication or transcriptional regulation. PARP-1 modulates the function of chromatin and nuclear proteins through several poly(ADP-ribose) (pADPr)-dependent pathways. Aside from the clearly established role of PARP-1 in the maintenance of genome stability, PARP-1 also emerged as an important regulator that links chromatin functions with extranuclear compartments. pADPr signaling has notably been found to be responsible for PARP-1-mediated mitochondrial dysfunction and cell death. Defining the mechanisms that govern the intrinsic functions of PARP-1 is fundamental to the understanding of signaling networks regulated by pADPr. The emergence of mass spectrometry-based proteomics and its broad applications in the study of biological systems represents an outstanding opportunity to widen our knowledge of the functional spectrum of PARP-1. In this article, we summarize various PARP-1 targeted proteomics studies and proteome-wide analyses that shed light on its protein interaction partners, expression levels and post-translational modifications.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.680
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.373
Teacher spread0.301 · 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 designSystematic review
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

Citations16
Published2011
Admission routes2
Has abstractyes

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