Kinetic and thermodynamic analysis defines roles for two metal ions in DNA polymerase specificity and catalysis
Bibliographic record
Abstract
Abstract We examined the roles of Mg 2+ ions in DNA polymerization by kinetic analysis of single nucleotide incorporation catalyzed by HIV reverse transcriptase and by molecular dynamics simulation of Mg 2+ binding. Binding of the Mg-nucleotide complex induces a conformational change of the enzyme from open to closed states in a process that is independent of free Mg 2+ concentration. Subsequently, the second Mg 2+ binds weakly to the closed state of the enzyme-DNA-Mg.dNTP complex with an apparent K d = 3.7 mM and facilitates the catalytic reaction. This weak binding of the catalytic Mg 2+ is important to maintain fidelity in that the Mg 2+ samples the correctly aligned substrate without perturbing the equilibrium at physiological Mg 2+ concentrations. The binding of the catalytic Mg 2+ increases nucleotide specificity ( k cat / K m ) by increasing the rate of the chemistry and decreasing the rate of enzyme opening allowing nucleotide release. Changing the free Mg 2+ concentration from 0.25 to 10 mM increased nucleotide specificity ( k cat / K m ) by 12-fold. Mg 2+ binds very weakly to the open state of the enzyme in the absence of nucleotide ( K d ≈ 34 mM) and competes with Mg.dNTP. Analysis based on publish crystal structures showed that HIV RT binds only two metal ions during incorporation of a correct base-pair. MD simulations support the kinetic studies suggesting weak binding of the catalytic Mg 2+ in open and closed states. They also support the two-metal ion mechanism, although the polymerase may bind a third metal ion in the presence of a mismatched nucleotide.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".