A chemical biology toolbox to investigate in-cell target engagement and specificity of PRMT5-inhibitors
Bibliographic record
Abstract
Abstract Increasing evidence suggests the protein arginine methyltransferase PRMT5 as a contributor to tumorigenesis in various cancer types and several inhibitors have entered clinical trials. Robust assays to determine cellular target engagement and selectivity are an important asset for the optimisation of inhibitors and the design of relevant in vivo studies. Here we report a suite of chemical biology assays enabling quantitative assessment of PRMT5 inhibitor in-cell target engagement and global selectivity profiling using a representative set of inhibitors. With the help of a bespoke cellular probe, we assess inhibitor target occupancy in cells in relation to biochemical and functional cellular assays. Investigating the influence of SAM, the natural cofactor of PRMT5, our results support the hypothesis that SAM positively contributes to the engagement of substrate-competitive inhibitors via a PRMT5:SAM:inhibitor ternary complex. Extensive proteomic profiling studies by drug affinity chromatography and thermal profiling further indicate high specificity of the clinical PRMT5 inhibitor GSK3326595 (pemrametostat). Graphical abstract
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".