An interdomain bridge influences RNA binding of the human La protein
Bibliographic record
Abstract
La proteins are RNA chaperones that perform various functions depending on distinct RNA-binding modes and their subcellular localization. In the nucleus, they help process UUU-3'OH-tailed nascent RNA polymerase III transcripts, such as pre-tRNAs, whereas in the cytoplasm they contribute to translation of poly(A)-tailed mRNAs. La accumulation in the nucleus and cytoplasm is controlled by several trafficking elements, including a canonical nuclear localization signal in the extreme C terminus and a nuclear retention element (NRE) in the RNA recognition motif 2 (RRM2) domain. Previous findings indicate that cytoplasmic export of La due to mutation of the NRE can be suppressed by mutations in RRM1, but the mechanism by which the RRM1 and RRM2 domains functionally cooperate is poorly understood. In this work, we use electromobility shift assays (EMSA) to show that mutations in the NRE and RRM1 affect binding of human La to pre-tRNAs but not UUU-3'OH or poly(A) sequences, and we present compensatory mutagenesis data supporting a direct interaction between the RRM1 and RRM2 domains. Moreover, we use collision-induced unfolding and time-resolved hydrogen-deuterium exchange MS analyses to study the conformational dynamics that occur when this interaction is intact or disrupted. Our results suggest that the intracellular distribution of La may be linked to its RNA-binding modes and provide the first evidence for a direct protein-protein interdomain interaction in La proteins.
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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 | 0.001 |
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".