Stepwise assembly of multiple Lin28 proteins on the terminal loop of let-7 miRNA precursors
Bibliographic record
Abstract
Lin28 inhibits the biogenesis of let-7 miRNAs through direct interactions with let-7 precursors. Previous studies have described seemingly inconsistent Lin28 binding sites on pre-let-7 RNAs. Here, we reconcile these data by examining the binding mechanism of Lin28 to the terminal loop of pre-let-7g (TL-let-7g) using biochemical and biophysical methods. First, we investigate Lin28 binding to TL-let-7g variants and short RNA fragments and identify three independent binding sites for Lin28 on TL-let-7g. We then determine that Lin28 assembles in a stepwise manner on TL-let-7g to form a stable 1:3 complex. We show that the cold-shock domain (CSD) of Lin28 is responsible for remodelling the terminal loop of TL-let-7g, whereas the NCp7-like domain facilitates the initial binding of Lin28 to TL-let-7g. This stable binding of multiple Lin28 molecules to the terminal loop of pre-let-7g extends to other precursors of the let-7 family, but not to other pre-miRNAs tested. We propose a model for stepwise assembly of the 1:1, 1:2 and 1:3 pre-let-7g/Lin28 complexes. Stepwise multimerization of Lin28 on pre-let-7 is required for maximum inhibition of Dicer cleavage for a least one member of the let-7 family and may be important for orchestrating the activity of the several factors that regulate let-7 biogenesis.
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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 | 0.001 |
| 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".