Computational studies of DNA repair: Insights into the function of monofunctional DNA glycosylases in the base excision repair pathway
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it