How Can One Metal Power Nucleic Acid Phosphodiester Bond Cleavage by a Nuclease? Multiscale Computational Studies Highlight a Diverse Mechanistic Landscape
Bibliographic record
Abstract
Despite the remarkable resistance of the nucleic acid phosphodiester backbone to degradation affording genetic stability, the P–O bond must be broken during DNA repair and RNA metabolism, among many other critical cellular processes. Nucleases are powerful enzymes that can enhance the uncatalyzed rate of phosphodiester bond cleavage by up to ∼10 17 -fold. Despite the most well accepted hydrolysis mechanism involving two metals (M A 2+ to activate a water nucleophile and M B 2+ to stabilize the leaving group), experimental evidence suggests that some nucleases can use a single metal to facilitate the chemical step, a controversial concept in the literature. The present perspective uses the case studies of four nucleases (I- Ppo I, APE1, and bacterial and human EndoV) to highlight how computational approaches ranging from quantum mechanical (QM) cluster models to molecular dynamics (MD) simulations and combined quantum mechanics-molecular mechanics (QM/MM) calculations can reveal the atomic level details necessary to understand how a nuclease can use a single metal to facilitate this difficult chemistry. The representative nucleases showcase how different amino acid residues (e.g., histidine, aspartate) can fulfill the role of the first metal (M A 2+ ) in the two-metal-mediated mechanisms. Nevertheless, differences in active site architectures afford diversity in the single-metal-mediated mechanism in terms of the metal–substrate coordination, the role of the metal, and the identities of the general acid and base. The greater understanding of the catalytic mechanisms of nucleases obtained from the body of work reviewed can be used to further explore the progression of diseases associated with nuclease (mis)activity and the development of novel nuclease applications such as disease diagnostics, gene engineering, and therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".