Kinetic characterization of human mRNA guanine-N7 methyltransferase
Bibliographic record
Abstract
The 5'-mRNA-cap formation is a conserved process in protection of mRNA in eukaryotic cells, resulting in mRNA stability and efficient translation. In humans, two methyltransferases, RNA cap guanine-N7 methyltransferase (hRNMT) and cap-specific nucleoside-2'-O-methyltransferase 1 (hCMTr1) methylate the mRNA resulting in cap0 (N7mGpppN-RNA) and cap1 (N7mGpppN2'-Om-RNA) formation, respectively. Coronaviruses mimic this process by capping their RNA to evade human immune systems. The coronaviral nonstructural proteins, nsp14 and nsp10-nsp16, catalyze the same reactions as hRNMT and hCMTr1, respectively. These two viral enzymes are important targets for development of inhibitor-based antiviral therapeutics. However, assessing the selectivity of such inhibitors against human corresponding proteins is crucial. Human RNMTs have been implicated in proliferation of cancer cells and are also potential targets for development of anticancer therapeutics. Here, we report the development and optimization of a radiometric assay for hRNMT, full kinetic characterization of its activity, and optimization of the assay for high-throughput screening with a Z-factor of 0.79. This enables selectivity determination for a large number of hits from various screening of coronaviral methyltransferases, and also screening hRNMT for discovery of inhibitors and chemical probes that potentially could be used to further investigate the roles RNMTs play in cancers.
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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 | 0.001 |
| 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) | 0.001 | 0.001 |
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".