An optimized fluorescence assay for screening novel PARP-1 inhibitors
Bibliographic record
Abstract
Poly(ADP-ribose) polymerase 1 (PARP-1) is an enzyme that catalyzes the formation of poly(ADP-ribose) (PAR) polymer chains from nicotinamide adenine dinucleotide (NAD+) onto target proteins. PARP-1 plays an important role as a first responder in DNA damage repair and has been an attractive target for the development of anti-cancer therapeutics. This has led to high interest in the development of novel small molecule therapeutics by researchers to target PARP-1 and other members of the PARP-family of enzymes. However, identifying ideal drug candidates can be problematic as current commercial PARP-1 screening assays are cost-prohibitive for many small drug discovery laboratories (particularly for academic groups) and may require specialized equipment or techniques that many labs are not equipped for. Herein, we present a robust, low-cost and optimized PARP-1 assay that quantifies leftover NAD+ substrate as a key readout for PARP-1 inhibition via conversion to a fluorophore, while simultaneously optimizing the assay for molecules that have high background fluorescence. The assay procedure has been validated with known PARP inhibitors and has proven to be a cost-effective fluorescence assay for use as a robust preliminary PARP-1 screen.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".