Evaluating the Substrate Selectivity of Alkyladenine DNA Glycosylase: The Synergistic Interplay of Active Site Flexibility and Water Reorganization
Bibliographic record
Abstract
Human alkyladenine DNA glycosylase (AAG) functions as part of the base excision repair (BER) pathway by cleaving the N-glycosidic bond that connects nucleobases to the sugar-phosphate backbone in DNA. AAG targets a range of structurally diverse purine lesions using nonspecific DNA-protein π-π interactions. Nevertheless, the enzyme discriminates against the natural purines and is inhibited by pyrimidine lesions. This study uses molecular dynamics simulations and seven different neutral or charged substrates, inhibitors, or canonical purines to probe how the bound nucleotide affects the conformation of the AAG active site, and the role of active site residues in dictating substrate selectivity. The neutral substrates form a common DNA-protein hydrogen bond, which results in a consistent active site conformation that maximizes π-π interactions between the aromatic residues and the nucleobase required for catalysis. Nevertheless, subtle differences in DNA-enzyme contacts for different neutral substrates explain observed differential catalytic efficiencies. In contrast, the exocyclic amino groups of the natural purines clash with active site residues, which leads to catalytically incompetent DNA-enzyme complexes due to significant reorganization of active site water. Specifically, water resides between the A nucleobase and the active site aromatic amino acids required for catalysis, while a shift in the position of the general base (E125) repositions (potentially nucleophilic) water away from G. Despite sharing common amino groups, the methyl substituents in cationic purine lesions (3MeA and 7MeG) exhibit repulsion with active site residues, which repositions the damaged bases in the active site in a manner that promotes their excision. Overall, we provide a structural explanation for the diverse yet discriminatory substrate selectivity of AAG and rationalize key kinetic data available for the enzyme. Specifically, our results highlight the complex interplay of many different DNA-protein interactions used by AAG to facilitate BER, as well as the crucial role of the general base and water (nucleophile) positioning. The insights gained from our work will aid the understanding of the function of other enzymes that use flexible active sites to exhibit diverse substrate specificity.
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 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 | 0.001 | 0.001 |
| 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".