A High Throughput Scintillation Proximity Imaging Assay for Protein Methyltransferases
Bibliographic record
Abstract
Protein methyltransferases (PMTs) orchestrate epigenetic modifications through post-translational methylation of various protein substrates including histones. Since dysregulation of this process is widely implicated in many cancers, it is of pertinent interest to screen inhibitors of PMTs, as they offer novel target-based opportunities to discover small molecules with potential chemotherapeutic use. We have thus developed an enzymatic screening strategy, which can be adapted to scintillation proximity imaging assay (SPIA) format, to identify these inhibitors. We took advantage of S-adenosyl-L-[3H-methyl]-methionine availability and monitored the enzymatically catalyzed [3H]-methyl addition on lysine residues of biotinylated peptide substrates. The radiolabeled peptides were subsequently captured by streptavidin coated SPA imaging PS beads. We applied this strategy to four PMTs: SET7/9, SET8, SETD2, and EuHMTase1, and optimized assay conditions to achieve Z' values ranging from 0.48 to 0.91. The robust performance of this SPIA for the four PMTs was validated in a pilot screen of approximately 7,000 compounds. We identified 80 cumulative hits across the four targets. NF279, a suramin analogue, was found to specifically inhibit SET7/9 and SETD2 with IC50 values of 1.9 and 1.1 μM, respectively. Another identified compound, Merbromin, a topical antiseptic, was classified as a pan-active inhibitor of the four PMTs. These findings demonstrate that our proposed SPIA strategy is generic for multiple PMTs and can be successfully implemented to identify novel and specific inhibitors of PMTs. The specific PMT inhibitors may constitute a new class of anti-proliferative agents for potential therapeutic use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".