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Record W2891773955 · doi:10.1021/acs.jpcb.8b05053

Effects of Intense Electric Fields on the Double Proton Transfer in the Watson–Crick Guanine–Cytosine Base Pair

2018· article· en· W2891773955 on OpenAlexafffund
Alya A. Arabi, Chérif F. Matta

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Nucleic Acid Chemistry
Canadian institutionsMount Saint Vincent UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsCanada Foundation for InnovationMount Saint Vincent University
KeywordsMolecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic AcidGuanineCytosineBase pairWatsonBase (topology)PhysicsElectric fieldProtonChemistryDNANucleotideQuantum mechanicsMathematicsComputer scienceBiochemistry

Abstract

fetched live from OpenAlex

The double proton transfer reaction in the guanine–cytosine (GC) base pair is studied, using density functional theory, to understand the chances of mutations under the effect of uniform electric fields in the order of 10 8 to 10 9 V m –1 . On the basis of potential energy surfaces, reaction Gibbs energies, equilibrium constants, imaginary frequencies, forward and reverse barrier heights, tunneling-corrected rate constants, half-lives of the forward and reverse reactions, percent tautomerization, and Boltzmann distributions, it was found that fields ≥+3.60 × 10 9 V m –1 facilitate the mutation in the GC base pair and reduce the rectification of point mutations. Fields applied along the double proton transfer in the − x (defined in the C to G direction) direction favor the canonical over the rare tautomers. Tunneling-corrected rate constants of the forward reaction increase exponentially with stronger fields in the − x direction and follow a Gaussian curve for the reverse reaction.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations46
Published2018
Admission routes2
Has abstractyes

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