Proteomic Investigation of Phosphorylation Sites in Poly(ADP-ribose) Polymerase-1 and Poly(ADP-ribose) Glycohydrolase
Bibliographic record
Abstract
Phosphorylation is a very common post-translational modification event known to modulate a wide range of biological responses. Beyond the regulation of protein activity, the interrelation of phosphorylation with other post-translational mechanisms is responsible for the control of diverse signaling pathways. Several observations suggest that phosphorylation of poly(ADP-ribose) polymerase-1 (PARP-1) regulates its activity. There is also accumulating evidence to suggest the establishment of phosphorylation-dependent assembly of PARP-1-associated multiprotein complexes. Although it is relatively straightforward to demonstrate phosphorylation of a defined target, identification of the actual amino acids involved still represents a technical challenge for many laboratories. With the use of a combination of bioinformatics-based predictions tools for generic and kinase-specific phosphorylation sites, in vitro phosphorylation assays and mass spectrometry analysis, we investigated the phosphorylation profile of PARP-1 and poly(ADP-ribose) glycohydrolase (PARG), two major enzymes responsible for poly(ADP-ribose) turnover. Mass spectrometry analysis revealed the phosphorylation of several serine/threonine residues within important regulatory domains and motifs of both enzymes. With the use of in vivo microirradiation-induced DNA damage, we show that altered phosphorylation at specific sites can modify the dynamics of assembly and disassembly of PARP-1 at sites of DNA damage. By documenting and annotating a collection of known and newly identified phosphorylation sites, this targeted proteomics study significantly advances our understanding of the roles of phosphorylation in the regulation of PARP-1 and PARG.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".