The Role of Inelastic Proton Tunneling in Generating Point Mutations and Genetic Diversity in DNA
Bibliographic record
Abstract
A quantum threat to the fidelity of information transfer in transcription of DNA molecule, is the fluctuation of protons on the base pairs. Point mutations can arise when a hydrogen-bonded proton tunnels between donor and acceptor sites in DNA, transiently creating tautomeric forms that mispair during replication. Despite the elastic way of proton tunneling, we analyze inelastic proton tunneling accompanied by energy exchange with local vibrational/electronic modes and the environment. Within an open-quantum-system framework, we derive Born–Markov master equations for a two-state (double-well) Hamiltonian parameterized by hydrogen-bond geometry and environmental spectral properties. Quantum-chemical parameters are estimated by DFT (B3LYP/6311G, Gaussian09) with water as solvent. We examine temperature depen dence and kinetic isotope effects (H/D). It is shown show that inelastic tunneling can extend tautomer lifetimes and enhance mispairing probabilities. These results provide a quantitative route to connect microscopic proton dynamics with biologically relevant mutation pathways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".