The use of ubiquitin lysine mutants to characterize E2–E3 linkage specificity: Mass spectrometry offers a cautionary “tail”
Bibliographic record
Abstract
Oligomeric ubiquitin structures (i.e. ubiquitin "chains") may be formed through any of seven different lysine residues in the polypeptide, or via the amine group of Met 1. Different types of ubiquitin chains can confer very different biological outcomes to a protein substrate, yet the structural characteristics of E2s and E3s that determine ubiquitin linkage specificity remain poorly understood. In vitro autoubiquitylation assays combined with ubiquitin protein variants bearing individually mutated lysine residues ("K-to-R" mutants) have thus been widely used to characterize E2-E3 linkage specificity. However, how this type of assay compares to direct identification of ubiquitin linkage types using mass spectrometry (MS) has not been rigorously tested. Here, we characterize the linkage specificity of 12 different E2-E3 combinations using both approaches. The simple MS-based method described here is more robust, requires less material and is less prone to bias introduced by, e.g. the use of mutant proteins with unknown effects on E1, E2 or E3 recognition, antibodies with uncharacterized epitopes, the low dynamic range of X-ray film, and additional sources of experimental error. Indeed, our results suggest that the K-to-R assay be approached with some caution.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".