Designing an Appropriate Computational Model for DNA Nucleoside Hydrolysis: A Case Study of 2′-Deoxyuridine
Bibliographic record
Abstract
This study uses a variety of computational models and detailed, systematic potential energy surface scans to examine the hydrolysis of 2'-deoxyuridine. First, the unimolecular cleavage was studied using a model that only includes the nucleoside. Although comparison of experimental and (PCM-B3LYP/6-31+G(d)) calculated (Gibbs energy) barriers confirms that hydrolysis occurs via a fully dissociative (S(N)1) mechanism with a rate-limiting step of glycosidic bond dissociation, this model does not provide a complete picture of the hydrolysis mechanism. When the model is expanded to include one explicit water nucleophile, gas-phase optimizations are unable to model charge separation in the reaction intermediate, while optimizations that implicitly incorporate the effects of bulk solvent do not accurately model the second reaction step (nucleophilic attack following dissociation) due to insufficient (water) nucleophile activation and (uracil anion) leaving group stabilization. Further expansion of the model to include three explicit water molecules allows for discrete proton transfer from the water nucleophile to the uracil anion, and thereby generates smooth reaction surfaces for both the dissociative (S(N)1) and concerted (S(N)2) pathways. Furthermore, for the first time, this computational model for the uncatalyzed hydrolysis of the N-glycosidic bond in a nucleoside predicts that the dissociative mechanism is more favorable than the concerted pathway, which supports experimental findings. It is also found that although (implicit) solvent-phase single-point calculations on gas-phase geometries can yield similar energies to solvent-phase optimizations, the geometries can be very different and not all potential reaction routes can be fully characterized. Therefore, care must be taken when interpreting mechanistic information obtained from gas-phase structures. This work provides a template for generating other nucleoside or nucleotide hydrolysis models including those relevant to both uncatalyzed and enzyme-catalyzed reactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".