Iodination on Tyrosine Residues During Oxidation with Sodium Periodate in Solid Phase Extraction of N-linked Glycopeptides
Bibliographic record
Abstract
Solid-phase extraction of N-linked glycopeptides (SPEG) using hydrazide-modified supports has become a common sample preparation procedure in glycoproteomic experiments. We demonstrate that iodination of tyrosine residues occur in SPEG as a side reaction during an oxidation step with sodium periodate. MS/MS analysis of oxidized bovine serum albumin and carbonic anhydrase digests revealed a characteristic shift of m/z 125.9 on all y and b fragment ions containing the modified tyrosine residues. Selected reaction monitoring (SRM) measurements showed that the peak intensity from of the iodinated peptides increased during the course of oxidation. After an hour of oxidation, SRM analysis revealed that the strongest signal from an iodinated peptide was approximately one-tenth of the intensity of the corresponding unmodified peptide. Iodinated tyrosine residues were also identified in serum samples subjected to SPEG and analyzed by LC-ESI-MS/MS. We recommend assessing this side reaction by including iodotyrosine as a variable modification when performing database searches on SPEG experiments. For SRM-based acquisitions, we encourage the avoidance of tyrosine-containing glycopeptides or, if this is not practical, monitoring transitions that contain the potential modified iodinated tyrosine residue to monitor the presence of the iodinated form of the glycopeptide.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".