Exploring the Benefits of Trimethylation Enhancement using Diazomthane (TrEnDi) in Mass Spectropmetry-Based Phosphoproteomics
Bibliographic record
Abstract
This study provides a comprehensive investigation of TrEnDi for phosphopeptide analysis using mass spectrometry (MS).The goal is to broaden the scope of TrEnDi in phosphopeptide research, emphasizing its versatility and efficacy in peptide analysis.The initial phase involved applying TrEnDi to synthetic peptides, FLEEpXK (X= S, T or Y) and STEALMYCAR, to assess its impact on phosphate and sulfur-containing residues.Through iterative optimization, involving the synthesis of different phosphopeptides and LC-MS parameters, enhanced sensitivity and peptide retention were achieved.The successful application of TrEnDi to synthetic peptides prompted its evaluation on myoglobin and stathmin.TrEnDi-modified peptides demonstrated improved ionization efficiency, retention times, and sensitivity, confirming the successful derivatization.The study highlights future avenues, including exploring N-terminal phosphorylated residues, extending TrEnDi to diverse phosphoproteins, establishing a dedicated database, and integrating it with other methodologies.This research highlights TrEnDi's potential as a valuable tool for comprehensive phosphoproteomic analysis in conjunction with MS.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".