Tryptic digestion of ubiquitin standards reveals an improved strategy for identifying ubiquitinated proteins by mass spectrometry
Bibliographic record
Abstract
Ubiquitination plays an essential role in maintaining cellular homeostasis by regulating a multitude of essential processes. The ability to identify ubiquitinated proteins by MS currently relies on a strategy in which ubiquitinated peptides are identified by a 114.1 Da diglycine (GG) tag on lysine residues, which is derived from the C-terminus of ubiquitin, following trypsin digestion. In the following study, we report a more comprehensive approach for mapping ubiquitination sites by trypsin digestion and MS/MS analysis. We demonstrate that ubiquitination sites can be identified by signature peptides containing a GG-tag (114.1 Da) and an LRGG-tag (383.2 Da) on internal lysine residues as well as a GG-tag found on the C-terminus of ubiquitinated peptides. Application of this MS-based approach enabled the identification of 96 ubiquitination sites from proteins purified from human MCF-7 breast cancer cells, representing a 2.4-fold increase in the number of ubiquitination sites that could be identified over standard methods. Our improved MS-based strategy will aid future studies which aim to identify and/or characterize ubiquitinated proteins in human cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".