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Record W3113968967

Molecular Modelling and Biochemical Characterization of a DNA Repair Enzyme Binding to DNA

2017· article· en· W3113968967 on OpenAlexaff
Brandon Manary, Nina Bernstein, Jorge Llano

Bibliographic record

VenueStudent Research Proceedings · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsMacEwan University
Fundersnot available
KeywordsDNA repairDNADNA damageBiochemistryHomology modelingBiologyEnzymeGeneticsChemistry
DOInot available

Abstract

fetched live from OpenAlex

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

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.364
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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