The 5′ Binding MID Domain of Human Argonaute2 Tolerates Chemically Modified Nucleotide Analogues
Bibliographic record
Abstract
Small interfering RNAs (siRNAs) can trigger potent gene silencing through the RNA interference (RNAi) pathway. The RNA-induced silencing complex (RISC) is key to this targeted mRNA degradation, and the human Argonaute2 (hAGO2) endonuclease component of RISC is responsible for the actual mRNA cleavage event. During RNAi, hAGO2 becomes loaded with the siRNA guide strand, making several key nucleic acid-enzyme interactions. Chemically modified siRNAs are now widely used in place of natural double-stranded RNAs, and understanding the effects chemical modifications have on guide strand-hAGO2 interactions has become particularly important. Here, interactions between the 5' nucleotide binding domain of hAGO2, MID, and chemically modified nucleotide analogues are investigated. Measured dissociation constants reveal that hAGO2 does not discriminate between nucleotide analogues during binding, regardless of the preferred sugar conformation of the nucleotide analogues. These results correlate well with cell-based gene silencing results employing siRNAs with 5'-modified guide strands. Additionally, chemical modification with 2'-deoxy-2'-fluoroarabino nucleic acid (2'F-ANA) and 2'-deoxy-2'-fluororibonucleic acid (2'F-RNA) at the passenger strand cleavage site of siRNAs has been shown to prevent hAGO2-mediated strand cleavage, an observation that appears to have little impact on overall gene silencing potency.
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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".