The stoichiometry of protein phosphorylation in adipocyte lipid droplets: Analysis by N‐terminal isotope tagging and enzymatic dephosphorylation
Bibliographic record
Abstract
Most phosphoproteomic studies to date have been limited to the identification of phosphoproteins and their phosphorylation sites, and have not assessed the stoichiometry of protein phosphorylation, a critical parameter reflecting the dynamic equilibrium between phosphorylated and non-phosphorylated pools of proteins. Here, we used a method for measuring phosphorylation stoichiometry through isotope tagging and enzymatic dephosphorylation of tryptic peptides. Using this method, protein digests are divided into two equal aliquots that are modified with either light or heavy isotope tags. One aliquot is dephosphorylated by alkaline phosphatase. Finally, the peptide mixtures are recombined and LC-MS/MS analysis is performed. With this method, we studied adipocytes of mice stimulated with CL316,243, a beta-3 adrenergic agonist known to induce lipolysis and marked phosphorylation changes in proteins of the lipid droplet surface. In lipid droplet preparations, CL316,243 administration increased phosphorylation of proteins related to regulation of signaling, metabolism and intracellular trafficking in white adipose tissue, including hormone-sensitive lipase which was 80% phosphorylated at the previously reported site, Ser-559, and the lipid surface protein perilipin, which was phosphorylated by approximately 60 and approximately 40% at previously unreported sites, Ser-410 and Ser-460.
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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 | 0.000 |
| 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".