Is Metal Stabilization of the Leaving Group Required or Can Lysine Facilitate Phosphodiester Bond Cleavage in Nucleic Acids? A Computational Study of EndoV
Bibliographic record
Abstract
Endonuclease V (EndoV) is a single-metal-dependent enzyme that repairs deaminated DNA nucleobases in cells by cleaving the phosphodiester bond, and this enzyme has proven to be a powerful tool in biotechnology and medicine. The catalytic mechanism used by EndoV must be understood to design new disease detection and therapeutic solutions and further exploit the enzyme in interdisciplinary applications. This study has used a mixed molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) approach to compare eight distinct catalytic pathways and provides the first proposed mechanism for bacterial EndoV. The calculations demonstrate that mechanisms involving either direct or indirect metal coordination to the leaving group of the substrate previously proposed for other nucleases are unlikely for EndoV, regardless of the general base (histidine, aspartate, and substrate phosphate moiety). Instead, distinct catalytic pathways are characterized for EndoV that involve K139 stabilizing the leaving group, a metal-coordinated water stabilizing the transition structure, and either H214 or a substrate phosphate group activating the water nucleophile. In silico K139A and H214A mutational results support the newly proposed roles of these residues. Although this is a previously unseen combination of general base, general acid, and metal-binding architecture for a one-metal-dependent endonuclease, our proposed catalytic mechanisms are fully consistent with experimental kinetic, structural, and mutational data. In addition to substantiating a growing body of literature, suggesting that one metal is enough to catalyze P–O bond cleavage in nucleic acids, this new fundamental understanding of the catalytic function will promote the exploration of new and improved applications of EndoV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".