Computational Investigation of Glycosylase and β-Lyase Activity Facilitated by Proline: Applications to FPG and Comparisons to hOgg1
Bibliographic record
Abstract
Quantum mechanical methods are used to investigate the chemical steps during the bifunctional (glycosylase and β-lyase) activity of bacterial FPG DNA glycosylase, which removes the major oxidation product (8-oxoguanine) from DNA as part of the base excision repair process. To facilitate investigation of all potential pathways, the smallest chemically relevant model is implemented, namely a modified OG nucleoside-3'-monophosphate and a truncated proline nucleophile. Potential energy surfaces are characterized with SMD-M06-2X/6-311+G(2df,2p)//PCM-B3LYP/6-31G(d) and compared to a previous study on the analogues human enzyme (hOgg1), which uses a lysine nucleophile (Kellie, J. L.; Wetmore, S. D. J. Phys. Chem. B 2012, 116, 10786-10797). Our large calculated barriers indicate that FPG must actively catalyze the three main phases of the overall reaction, namely, deglycosylation, (deoxyribose) ring-opening, and β-elimination, and provide clues about how this is achieved through comparison to accurate crystal structures. The main conclusions about key mechanistic steps hold true regardless of the nucleophile, suggesting that most major differences in the relative activity of FPG and hOgg1 are primarily due to other active site residues. Nevertheless, support for possible monofunctional (deglycosylation only) activity is only evident when lysine is the nucleophile. This finding agrees with experimental observations of monofunctional activity of hOgg1 and further supports the broadly accepted bifunctional activity of FPG.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".