Utility of 5′-<i>O</i>-2,7-Dimethylpixyl for Solid-Phase Synthesis of Oligonucleotides Containing Acid-Sensitive 8-Aryl-Guanine Adducts
Bibliographic record
Abstract
To study the structural and biological impact of 8-aryl-2'-deoxyguanosine adducts, an efficient protocol is required to incorporate them site-specifically into oligonucleotide substrates. Traditional phosphoramidite chemistry using 5'-O-DMT protection can be limiting because 8-aryl-dG adducts suffer from greater rates of acid-catalyzed depurination than dG and are sensitive to the acidic deblock conditions required to remove the DMT group. Herein we show that the 5'-O-2,7-dimethylpixyl (DMPx) protecting group can be used to limit acid exposure and improve DNA synthesis efficiency for DNA substrates containing 8-aryl-dG adducts. Our studies focus on 8-aryl-dG adducts with 8-substituents consisting of furyl ((Fur)dG), phenyl ((Ph)dG), 4-cyanophenyl ((CNPh)dG), and quinolyl ((Q)dG). These adducts differ in ring size and sensitivity to acid-promoted deglycosylation. A kinetic study for adduct hydrolysis in 0.1 M aqueous HCl determined that (Fur)dG was the most acid-sensitive (55.2-fold > dG), while (Q)dG was the most resistant (5.6-fold > dG). The most acid-sensitive (Fur)dG was chosen for optimization of solid-phase DNA synthesis. Our studies show that the 5'-O-DMPx group can provide a 4-fold increase in yield compared to 5'-O-DMT for incorporation of (Fur)dG into DNA substrates critical for determining adduct impact on DNA synthesis and repair.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".