Enhanced Specificity of the Viral Suppressor of RNA Silencing Protein p19 toward Sequestering of Human MicroRNA-122
Bibliographic record
Abstract
Tombusviruses express a 19 kDa protein (p19) that, as a dimeric protein, suppresses the RNAs silencing pathway during infection by binding short-interfering RNA (siRNA) and preventing their association with the RNA-induced silencing complex (RISC). The p19 protein can bind to both endogenous and synthetic siRNAs with a high degree of size selectivity but with little sequence dependence. It also binds to other endogenous small RNAs such as microRNAs (miRNAs) but with lower affinity than to canonical siRNAs. It has become apparent, however, that miRNAs play a large role in gene regulation; their influence extends to expression and processing that affects virtually all eukaryotic processes. In order to develop new tools to study endogenous small RNAs, proteins that suppress specific miRNAs are required. Herein we describe mutational analysis of the p19 binding surface with the aim of creating p19 mutants with increased affinity for miR-122. By site-directed mutagenesis of a single residue, we describe p19 mutants with a nearly 50-fold increased affinity for miR-122 without altering the affinity for siRNA. Upon further mutational analysis of this site, we postulate that the higher affinity relies on hydrogen-bonding interactions but can be sterically hindered by residues with bulky side chains. Finally, we demonstrate the effectiveness of a mutant p19, p19-T111S, at sequestering miR-122 in human hepatoma cell lines, as compared to wild-type p19. Overall, our results suggest that p19 can be engineered to enhance its affinity toward specific small RNA molecules, particularly noncanonical miRNAs that are distinguishable based on locations of base-pair mismatches. The p19-T111S mutant also represents a new tool for the study of the function of miR-122 in post-transcriptional silencing in the human liver.
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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 | 0.000 |
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".