Combined Enzymatic and Data Mining Approaches for Comprehensive Phosphoproteome Analyses
Bibliographic record
Abstract
Protein phosphorylation is a central cell signaling event that underlies a broad spectrum of key physiological processes. Advances in affinity chromatography and mass spectrometry are now providing the ability to identify and quantitate thousands of phosphorylation sites simultaneously. Comprehensive phosphoproteome analyses present sizable analytical challenges in view of suppression effects of phosphopeptides and the variable quality of MS/MS spectra. This work presents an integrated enzymatic and data mining approach enabling the comprehensive detection of native and putative phosphopeptides following alkaline phosphatase digestion of titanium dioxide (TiO2)-enriched cell extracts. The correlation of retention times of more than 750 phospho- and dephosphopeptide pairs from J774 macrophage cell extracts indicated that removal of the phosphate groups can impart a gain or a loss in hydrophobicity that is partly explained by the formation of a salt bridge with proximal amino groups. Dephosphorylation also led to an average 2-fold increase in MS sensitivity that facilitated peptide sequencing. More importantly, alkaline phosphatase digestion enhanced the overall population of putative phosphopeptides from TiO2-enriched cell extracts providing a unique approach to profile multiphosphorylated cognates that would have remained otherwise undetected. The application of this approach is demonstrated for differential phosphoproteome analyses of mouse macrophages exposed to interferon-gamma for 5 min. TiO2 enrichment enabled the identification of 1143 phosphopeptides from 432 different proteins of which 125 phosphopeptides showed a 2-fold change upon interferon-gamma exposure. The use of alkaline phosphatase nearly doubled the number of putative phosphopeptides assignments leading to the observation of key interferon-gamma signaling events involved in vesicle trafficking, production of reactive oxygen species, and mRNA translation.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".