Targeted identification of phosphorylated peptides by off‐line HPLC–MALDI‐MS/MS using LC retention time prediction
Bibliographic record
Abstract
Protein phosphorylation is a type of posttranslational modification which plays an important role in cell regulation and signal transduction. Because of its biological relevance, a considerable amount of interest has been paid to the development of efficient techniques for phosphopeptide analysis. Although advances in MS control have enabled the high-throughput discovery of proteins from limited amounts of sample, automated selection of MS/MS precursor ions based on intensity alone can significantly hamper the detection of low-abundance phosphopeptides. On the basis of the observation that the introduction of a phosphate moiety does not dramatically change peptide retention time in reverse-phase chromatography, phosphopeptide specific MS/MS fragmentation attempts based on LC retention time and m/z were evaluated using a standard protein mixture, then using in vitro phosphorylated myelin basic protein. Results indicated that the majority (98%) of phosphopeptides identified eluted within a +/- 4-min window of the predicted LC elution time. While studies presented here are primarily proof of concept in nature, data suggest that the use of LC retention time prediction could be a valuable constraint for the identification of phosphopeptides within a set of off-line LC deposited sample spots. It is expected that the development of these methods will not only permit the targeted identification of protein phosphorylation sites but also allow the in-depth analysis of the dynamic events linked to the posttranslational modification.
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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 | 0.000 |
| 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 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".