A small molecule antagonist for the Tudor domain of SMN disrupts the interaction between SMN and RNAP II
Bibliographic record
Abstract
Abstract Survival of motor neuron (SMN), a Tudor-domain-containing protein, plays an important role in diverse biological pathways via recognition of symmetrically dimethylated arginine (Rme2s) on proteins by its Tudor domain, and deficiency of SMN leads to the motor neuron degenerative disease spinal muscular atrophy (SMA). Here we report a potent and selective antagonist with a 4-iminopyridine scaffold targeting the Tudor domain of SMN. Our structural and mutagenesis studies indicate that the sandwich stacking interactions of SMN and compound 1 play a critical role in selective binding to SMN. Various on-target engagement assays support that compound 1 recognizes SMN specifically in a cellular context. In cell studies display that the SMN antagonist prevent the interaction of SMN with R1810me2s of DNA-directed RNA polymerase II subunit POLR2A and results in transcription termination and R-loop accumulation, mimicking depletion of SMN . Thus, in addition to the antisense, RNAi and CRISPR/Cas9 techniques, the potent SMN antagonist could be used as an efficient tool in understanding the biological functions of SMN and molecular etiology in SMA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".