Enzymological and structural studies of the mechanism of promiscuous substrate recognition by the oxidative DNA repair enzyme AlkB
Bibliographic record
Abstract
Promiscuous substrate recognition, the ability to catalyze transformations of chemically diverse compounds, is an evolutionarily advantageous, but poorly understood phenomenon. The promiscuity of DNA repair enzymes is particularly important, because it enables diverse kinds of damage to different nucleotide bases to be repaired in a metabolically parsimonious manner. We present enzymological and crystallographic studies of the mechanisms underlying promiscuous substrate recognition by Escherichia coli AlkB, a DNA repair enzyme that removes methyl adducts and some larger alkylation lesions from endocyclic positions on purine and pyrimidine bases. In vitro Michaelis-Menten analyses on a series of alkylated bases show high activity in repairing N1-methyladenine (m1A) and N3-methylcytosine (m3C), comparatively low activity in repairing 1,N(6)-ethenoadenine, and no detectable activity in repairing N1-methylguanine or N3-methylthymine. AlkB has a substantially higher k(cat) and K(m) for m3C compared with m1A. Therefore, the enzyme maintains similar net activity on the chemically distinct substrates by increasing the turnover rate of the substrate with nominally lower affinity. Cocrystal structures provide insight into the structural basis of this "k(cat)/K(m) compensation," which makes a significant contribution to promiscuous substrate recognition by AlkB. In analyzing a large ensemble of crystal structures solved in the course of these studies, we observed 2 discrete global conformations of AlkB differing in the accessibility of a tunnel hypothesized to control diffusion of the O(2) substrate into the active site. Steric interactions between a series of protein loops control this conformational transition and present a plausible mechanism for preventing O(2) binding before nucleotide substrate binding.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".