Defining substrate specificities of human <scp>RNA</scp> capping methyltransferases through quantitative assessment of independent yet cooperative activities
Bibliographic record
Abstract
Human RNA capping is critical for mRNA splicing, protection of RNA from 5' exonucleases in the cytoplasm, and targeting to the ribosome. Human RNMT, CMTR1, and CMTR2 are RNA methyltransferases involved in the RNA capping process. They play a significant role in the proliferation and differentiation of embryonic stem cells and have been implicated in cancer. Substrate specificities of human RNA capping methyltransferases have been somewhat explored in a few studies. Here, we report on a comprehensive, systematic, and quantitative assessment of their substrate specificities along with SARS-CoV-2 counterparts, nsp14 and nsp16. We discovered novel cooperative activities of human enzymes. We designed and synthesized various RNA substrates with defined patterns of methylation to systematically assess the dependency or cooperativity of their activities using radiometric assays followed by mass spectrometry to verify RNA methylation status. We have tested all five enzymes in parallel against these substrates and determined kinetic parameters. Our data not only indicate that the catalytic activities of human RNMT, CMTR1, and CMTR2 are distinct and nonoverlapping, but also provide a novel quantitative assessment of their activities, indicating how and to what extent these proteins affect each other's function. Unlike nsp14 and nsp16, their functions are not necessarily sequential, but show significant cooperativity. Altogether, our data provide a comprehensive understanding of substrate specificities of human RNA capping methyltransferases, enabling the development of potential future anticancer therapeutics and assessment of antiviral therapeutics' selectivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".