Unlocking the chemistry facilitated by enzymes that process nucleic acids using quantum mechanical and combined quantum mechanics–molecular mechanics techniques
Bibliographic record
Abstract
The diverse cellular functions of nucleic acids are made possible by enzymes that catalyze cleavage of glycosidic (nucleobase-sugar) and phosphodiester bonds. Despite advancements in experimental biochemical methods, critical information about such enzyme-catalyzed reactions is difficult to obtain from traditional experiments. However, computational quantum mechanical (QM) methods can provide atomic level details of catalytic pathways that are complementary to experimental data. This perspective highlights various QM techniques used to advance our understanding of enzymes that process nucleic acids. First, select DNA glycosylases are discussed to showcase how QM calculations on nucleoside/tide and small molecule complexes uncover roles of active site interactions and the preferred order of reaction steps along DNA repair pathways. Furthermore, the ability of calculations on nucleic acid-enzyme complexes that combine QM methods with molecular mechanics (MM) force fields to challenge traditional views of enzyme function and lead to consensus for mechanistic pathways is illustrated. Subsequently, QM-based studies of select nucleases are discussed to highlight how this methodology can discern the various strategies enzymes use to cleave nucleic acid backbones. Overall, this contribution underscores the value in combining QM-based computational work with experimental studies to uncover enzyme-facilitated nucleic acid chemistry to be harnessed in future medicinal, biotechnological and materials applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".