An essential RNA-binding lysine residue in the Nab3 RRM domain undergoes mono and trimethylation
Bibliographic record
Abstract
Abstract The Nrd1-Nab3-Sen1 (NNS) complex integrates molecular inputs to direct termination of noncoding transcription in budding yeast. NNS is positively regulated by methylation of histone H3 lysine-4 as well as through Nrd1 binding to the initiating form of RNA PolII. These cues collaborate with Nrd1 and Nab3 binding to target RNA sequences in nascent transcripts through their RRM RNA recognition motifs. In this study, we identify nine lysine residues distributed amongst Nrd1, Nab3, and Sen1 that are mono-, di-, or trimethylated, suggesting novel molecular inputs for NNS regulation. One of these methylated residues, Nab3 lysine-363 (K363), resides within its RRM, and is known to physically contact target RNA. Although mutation of Nab3-K363 to arginine (Nab3-K363R) causes a severe growth defect, it nevertheless produces a stable protein that is incorporated into the NNS complex, suggesting that RNA binding through Nab3-K363 is crucial for NNS function. Consistent with this hypothesis, K363R mutation decreases RNA binding of the Nab3 RRM in vitro and causes transcription termination defects in vivo. These findings reveal crucial roles for Nab3-K363 and suggest that methylation of this residue may modulate NNS activity through its impact on Nab3 RNA binding.
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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) | 0.001 | 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".