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Record W2023165782 · doi:10.1021/pr301219h

Mapping PARP-1 Auto-ADP-ribosylation Sites by Liquid Chromatography–Tandem Mass Spectrometry

2013· article· en· W2023165782 on OpenAlexafffund
J. D. Chapman, Jean-Philippe Gagné, Guy G. Poirier, David R. Goodlett

Bibliographic record

VenueJournal of Proteome Research · 2013
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversité Laval
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsPoly ADP ribose polymeraseTandem mass spectrometryPhosphopeptideChemistryMass spectrometryCollision-induced dissociationTandemRiboseLiquid chromatography–mass spectrometryChromatographyPolymerasePeptideBiochemistryEnzyme

Abstract

fetched live from OpenAlex

We demonstrate a novel method for the identification of poly(ADP-ribose) polymerase-1 (PARP-1) autopoly(ADP-ribosyl)ation sites that is suited to collision induced dissociation (CID) tandem mass spectrometry. By employing phosphodiesterase to remove the majority of the poly(ADP-ribose) (pADPr) modification, we reduce the complexity of tandem mass spectrometric analysis of pADPr-modified tryptic peptides. The simplified ribose-5'-phosphate form of the peptides produce tandem mass spectra by CID that are readily interpreted and enable effective localization of the exact sites of PARP-1-catalyzed poly(ADP-ribosyl)ation. In conjunction with a phosphopeptide-like enrichment strategy that captures the ribose-5'-phosphate peptides, we identified eight novel sites of PARP-1 automodification, confirmed the localization of two sites previously reported, and provided evidence for two additional targeted peptides with ambiguous modification site assignments. Given the simplicity of the approach, the method is readily applicable to analysis of complex samples.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.082
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.380
Teacher spread0.311 · 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 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

Citations86
Published2013
Admission routes2
Has abstractyes

Explore more

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