Electrospray Ionization Mass Spectrometry Characterization of Interactions of Newly Identified Water Disinfection Byproducts Halobenzoquinones with Oligodeoxynucleotides
Bibliographic record
Abstract
Four halobenzoquinones, 2,6-dibromo-1,4-benzoquinone, 2,6-dichloro-1,4-benzoquinone, 2,6-dichloro-3-methyl-1,4-benzoquinone, and 2,3,6-trichloro-1,4-benzoquinone, were recently identified as drinking water disinfection byproducts. Understanding their interactions with biomolecules could provide useful insights into their potential toxic effects. We report here electrospray ionization mass spectrometry characterization of the interactions between these new halobenzoquinone disinfection byproducts and oligodeoxynucleotides. The study demonstrates that 2,6-dibromo-1,4-benzoquinone exhibits much stronger binding to single- and double-stranded oligodeoxynucleotides than chlorobenzoquinones. The binding affinity of 2,6-dibromo-1,4-benzoquinone to oligodeoxynucleotides is similar to that of ethidium bromide, a well-known intercalator and carcinogen. Tandem mass spectrometry characterization confirms the formation of 1:1 and 2:1 complexes of 2,6-dibromo-1,4-benzoquinone binding to oligodeoxynucleotides. Collision-induced dissociation analysis of these adducts demonstrates neutral loss and charge separation, suggesting that 2,6-dibromo-1,4-benzoquinone binds to oligodeoxynucleotides through partial intercalation and H-bonding modes. The three chlorobezoquinones also form 1:1 adducts with the oligodeoxynucleotides, but their binding to the oligodeoxynucleotides was much weaker compared to that of 2,6-dibromo-1,4-benzoquinone. The relative binding affinity of the studied disinfection byproducts to oligodeoxynucleotides is in the order of 2,6-dibromo-1,4-benzoquinone≫2,6-dichloro-1,4-benzoquinone > 2,6-dichloro-3-methyl-1,4-benzoquinone ∼ 2,3,6-trichloro-1,4-benzoquinone, indicating potential structural effects on the interactions of halobenzoquinones with oligodeoxynucleotides.
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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) | 0.000 | 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 | 0.000 |
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".