Formation and Occurrence of Iodinated Tyrosyl Dipeptides in Disinfected Drinking Water
Bibliographic record
Abstract
Iodinated disinfection byproducts (I-DBPs) are highly toxic, but few precursors of I-DBPs have been investigated. Tyrosine-containing biomolecules are ubiquitous in surface water. Here we investigated the formation of I-DBPs from the chloramination of seven tyrosyl dipeptides (tyrosylglycine, tyrosylalanine, tyrosylvaline, tyrosylhistidine, tyrosylglutamine, tyrosylglutamic acid, and tyrosylphenylalanine) in the presence of potassium iodide. High resolution mass spectrometry and tandem mass spectrometry (MS/MS) analyses of the benchtop reaction solutions found that all seven precursors formed both I- and Cl-substituted tyrosyl dipeptide products. Iodine substitutions occurred on the 3- and 3,5-positions of the tyrosyl-phenol ring while chlorine substituted on the free amino group. To reach the needed sensitivity to detect iodinated tyrosyl dipeptides in authentic waters, we developed a high performance liquid chromatography (HPLC)-MS/MS method with multiple reaction monitoring mode and solid phase extraction. HPLC-MS/MS analysis of tap and corresponding raw water samples, collected from three cities, identified four iodinated peptides, 3-I-/3,5-di-I-Tyr-Ala and 3-I-/3,5-di-I-Tyr-Gly, in the tap waters but not in the raw waters. The corresponding precursors, Tyr-Ala and Tyr-Gly, were also detected in the same tap and raw water samples. This study demonstrates that iodinated dipeptides exist as DBPs in drinking water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".