Structural basis of G‐tract recognition by hnRNP F: implication for alternative splicing
Bibliographic record
Abstract
HnRNP F regulates alternative splicing of many pre‐mRNAs, among which the Bcl‐x pre‐mRNA. HnRNP F contains three quasi RNA recognition motifs (qRRMs) that specifically recognizes Guanosine tract (G‐tract) RNA sequences that are crucial for splice site recognition. We solved the structure of the three qRRMs of HnRNP F in complex with a G‐tract RNA (AGGGAU) by NMR. The structures explain how qRRMs specifically recognize three consecutive guanosines. The RNA binding surface of the qRRM is very different than that of the classical RRM and consists of residues located in three loops and involves aromatic residues stacking the RNA and hydrogen bonds between main‐chain and positively charged side‐chains with the three guanosines. Mutagenesis experiments confirmed the importance of these residues in RNA binding. These structures define a novel RNA recognition mechanism. Our data show that G‐tract RNAs form stable G‐quadruplex structures that are destabilized by hnRNP F. We propose that hnRNP F regulates alternative splicing by modifying the RNA structure. Consistently, we show that single qRRMs of hnRNP F regulate splicing of the Bcl‐x pre‐mRNA with almost the same efficiency as the full‐length protein.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".