Hydrolytic Glycosidic Bond Cleavage in RNA Nucleosides: Effects of the 2′-Hydroxy Group and Acid–Base Catalysis
Bibliographic record
Abstract
Despite the inherent stability of glycosidic linkages in nucleic acids that connect the nucleobases to sugar–phosphate backbones, cleavage of these bonds is often essential for organism survival. The current study uses DFT (B3LYP) to provide a fundamental understanding of the hydrolytic deglycosylation of the natural RNA nucleosides (A, C, G, and U), offers a comparison to DNA hydrolysis, and examines the effects of acid, base, or simultaneous acid–base catalysis on RNA deglycosylation. By initially examining HCOO – ···H 2 O mediated deglycosylation, the barriers for RNA hydrolysis were determined to be 30–38 kJ mol –1 higher than the corresponding DNA barriers, indicating that the 2′-OH group stabilizes the glycosidic bond. Although the presence of HCOO – as the base (i.e., to activate the water nucleophile) reduces the barrier for uncatalyzed RNA hydrolysis (i.e., unactivated H 2 O nucleophile) by ∼15–20 kJ mol –1, the extreme of base catalysis as modeled using a fully deprotonated water molecule (i.e., OH – nucleophile) decreases the uncatalyzed barriers by up to 65 kJ mol –1 . Acid catalysis was subsequently examined by selectively protonating the hydrogen-bond acceptor sites of the RNA nucleobases, which results in an up to ∼80 kJ mol –1 barrier reduction relative to the corresponding uncatalyzed pathway. Interestingly, the nucleobase proton acceptor sites that result in the greatest barrier reductions match sites typically targeted in enzyme-catalyzed reactions. Nevertheless, simultaneous acid and base catalysis is the most beneficial way to enhance the reactivity of the glycosidic bonds in RNA, with the individual effects of each catalytic approach being weakened, additive, or synergistic depending on the strength of the base (i.e., degree of water nucleophile activation), the nucleobase, and the hydrogen-bonding acceptor site on the nucleobase. Together, the current contribution provides a greater understanding of the reactivity of the glycosidic bond in natural RNA nucleosides, and has fundamental implications for the function of RNA-targeting enzymes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".