Molecular Modelling and Biochemical Characterization of a DNA Repair Enzyme Binding to DNA
Bibliographic record
Abstract
Computational techniques of homology modelling, enzyme–substrate docking, and molecular dynamics were applied to elucidate the structure and substrate binding properties of the DNA repair enzyme polynucleotide kinase/phosphatase from the nematode C. elegans (CePNKP). PNKP is involved in the repair of DNA strand breaks, a form of DNA damage caused by reactive oxygen species, ionizing radiation and certain chemical mutagens. Mutations in human PNKP have been associated with the neurological disorders Microcephaly with Intractable Seizures (MCSZ) and Ataxia Oculomotor Apraxia 4 (AOA4). In addition, human PNKP has been identified as a potential drug target for the development of chemo- and radiosensitizing agents for cancer treatment. CePNKP is a useful model system for studying the human enzyme. The substrate preference for both human and C. elegans PNKP has been investigated, and found to be similar, with higher selectivity for recessed over blunt DNA ends. However, CePNKP exhibits a more exclusive preference for recessed DNA ends than human PNKP. To elucidate the reason for the unique substrate specificity of CePNKP, the structure of CePNKP in complex with its DNA substrate must be determined in atomistic detail by molecular modelling. The generated structural model is compared with our experimental results of kinase activity assays of wild-type CePNKP. Discipline: Biological Sciences/Chemistry Faculty Mentor: Dr. Nina Bernstein, Dr. Jorge Llano
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".