Computational studies of DNA repair: Insights into the function of monofunctional DNA glycosylases in the base excision repair pathway
Bibliographic record
Abstract
Abstract The information contained within DNA as a sequence of nucleobases is required for life of most organisms, yet can get altered when the nucleobases are damaged upon exposure to many internal (hormones) and external (ultraviolet sunlight, pollutants) sources. As a result, repair pathways exist to combat the potentially detrimental effects of DNA damage. Nonbulky nucleobase damage (nucleobase oxidation, alkylation and deamination) is commonly removed by the base excision repair (BER) pathway, which involves several enzymes. The first BER enzymes are the DNA glycosylases, which are responsible for identifying the damaged base, flipping the base into the enzyme active site and removing the damaged nucleobase from the sugar–phosphate backbone. Due to the stability of many forms of damaged DNA, the DNA glycosylases must achieve great catalytic power. Understanding the mechanistic details associated with DNA glycosylases is essential for developing detection and treatment strategies for many diseases as abnormal glycosylase function has been associated with cancers, metabolic dysfunctions, neurodegeneration and epigenetic programming during embryo development. Molecular level insight into the function of a wide range of DNA glycosylases has been obtained from computational chemistry, including quantum mechanical cluster calculations, combined quantum mechanics‐molecular mechanics approaches and molecular dynamics simulations. By discussing some of the modeling that has been performed to date on monofunctional DNA glycosylases, the key contributions of the field of computational chemistry to broadening our understanding of the function of this important enzyme family, as well as the critical interplay between traditional biochemical experiments and computer calculations, is highlighted. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Structure and Mechanism > Computational Biochemistry and Biophysics Electronic Structure Theory > Combined QM/MM Methods
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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 | 0.003 |
| 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) | 0.004 | 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".