Confident Identification of Citrullination and Carbamylation Assisted by Peptide Retention Time Prediction
Bibliographic record
Abstract
The chromatographic behavior of peptides carrying citrulline and homocitrulline residues in proteomic two-dimensional (2D) liquid chromatography-mass spectrometry (LC-MS) experiments has been investigated. The primary goal of this study was to determine the chromatographic conditions that allow differentiating between arginine citrullination and deamidation of asparagine based on retention data, improving the confidence of MS-based identifications. Carbamylation was used as a reference point due to a high degree of similarity between modification products and anticipated changes in chromatographic behavior. We applied 2D LC-MS/MS (a high-pH-low-pH reversed phase (RP), hydrophilic interaction liquid chromatography (HILIC)-low-pH RP, and strong cation exchange (SCX)-low-pH RP) to acquire retention data for modified-nonmodified peptide pairs in the four separation modes. Modifications of a standard protein mixture were induced enzymatically (PAD-2) or chemically (urea) for citrullination and carbamylation, respectively. Deamidation occurs spontaneously. Similar retention shifts were observed for all three modifications in a high-pH RP (decrease) and a low-pH RP (increase), thus limiting the applicability of this 2D LC combination. HILIC on bare silica and strong cation exchange separations have been probed to amplify the effect of charge loss upon citrullination, with SCX demonstrating the most differentiating power: the elimination of basic residues upon citrullination/carbamylation results in an ∼58 mM KCl retention decrease, while retention of deamidated products decreases slightly.
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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 | 0.002 | 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) | 0.000 | 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".