Recent developments in mass spectrometry-based quantitative phosphoproteomicsThis paper is one of a selection of papers published in this Special Issue, entitled CSBMCB — Systems and Chemical Biology, and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
Protein phosphorylation is a reversible post-translational modification that is involved in virtually all eukaryotic cellular processes and has been studied in great detail in recent years. Many developments in mass spectrometry (MS)-based proteomics have been successfully applied to study protein phosphorylation in highly complicated samples. Furthermore, the emergence of a variety of enrichment strategies has allowed some of the challenges associated with low phosphorylation stoichiometry and phosphopeptide copy number to be overcome. The dynamic nature of protein phosphorylation complicates its analysis; however, a number of methods have been developed to successfully quantitate phosphorylation changes in a variety of cellular systems. The following review details some of the most recent breakthroughs in the study of protein phosphorylation, or phosphoproteomics, using MS-based approaches. The majority of the focus is placed on detailing strategies that are currently used to conduct MS-based quantitative phosphoproteomics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".