Using mass spectrometry to identify ubiquitin and ubiquitin‐like protein conjugation sites
Bibliographic record
Abstract
Ubiquitin (Ub) and the ubiquitin-like proteins (Ubls) are polypeptides that are covalently conjugated to proteins and other biomolecules to modulate their turnover rate, localization, and/or function. The full range of Ubl functions is only beginning to be understood, and the wide variety of Ubl conjugates is only beginning to be identified. Moreover, how Ubl conjugation is regulated, and how Ubl conjugate populations change, e.g., throughout the cell cycle, in response to hormones, nutrients, or stress, or in various disease states, remains largely enigmatic. MS represents a powerful tool for the characterization of PTMs. However, standard sample preparation and data search methods are not amenable to the identification of many types of Ubl conjugates. Here, we describe the challenges of identifying Ub/Ubl conjugates, and propose an improved workflow for identification of Ub/Ubl conjugation sites. Considering the importance of Ubls in normal cellular physiology, and their roles in disease etiology and progression, it will be critical to develop improved high-throughput MS methods capable of efficiently identifying proteins and other biomolecules modified by these very interesting and important PTMs.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".