Phosphoproteomics—finally fulfilling the promise?
Bibliographic record
Abstract
Networks of protein-protein and protein-metabolite interactions are commonly found in biological systems where signals must be passed from one location or component within a cell to another, such as from a receptor on the plasma membrane to a transcription factor in the nucleus. Regulation of such networks, or signal transduction pathways, is often achieved by transient, reversible modification of the components involved. Several types of post-translational modifications of proteins are employed in signal transduction including ubiquitylation of lysines and palmitoylation of cysteines, but by far the best appreciated and apparently the most important involves phosphorylation of serine, threonine and tyrosine residues. Whilst protein phosphorylation has long been recognized as functionally important, low stoichiometry has ultimately impeded global analyses (phosphoproteomics). Recent developments in the application of metal oxide chromatography and advanced mass spectrometric techniques have enabled phosphoproteomics to move beyond mere proof-of-principle experiments, to the stage where it can successfully address complex biological questions. Here we cover the development of phosphopeptide/protein analysis by mass spectrometry and the various techniques used to enrich phosphopeptides/proteins. We also speculate on the future of phosphoproteomic research, now that the goal of generating global phosphoproteomic datasets has been realized.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".