A Universal Strategy for Proteomic Studies of SUMO and Other Ubiquitin-like Modifiers
Bibliographic record
Abstract
Post-translational modification by the conjugation of small ubiquitin-like modifiers is an essential mechanism to affect protein function. Currently, only a limited number of substrates are known for most of these modifiers, thus limiting our knowledge of their role and relevance for cellular physiology. Here, we report the development of a universal strategy for proteomic studies of ubiquitin-like modifiers. This strategy involves the development of stable transfected cell lines expressing a double-tagged modifier under the control of a tightly negatively regulated promoter, the induction of the expression and conjugation of the tagged modifier to cellular proteins, the tandem affinity purification of the pool of proteins covalently modified by the tagged modifier, and the identification of the modified proteins by LC and MS. By applying this methodology to the proteomic analysis of SUMO-1 and SUMO-3, we determined that SUMO-1 and SUMO-3 are stable proteins exhibiting half-lives of over 20 h, demonstrated that sumoylation with both SUMO-1 and SUMO-3 is greatly stimulated by MG-132 and heat shock treatment, demonstrated the preferential usage of either SUMO-1 or SUMO-3 for some known SUMO substrates, and identified 122 putative SUMO substrates of which only 27 appeared to be modified by both SUMO-1 and SUMO-3. This limited overlapping in the subset of proteins modified by SUMO-1 and SUMO-3 supports that the SUMO paralogues are likely to be functionally distinct. Three of the novel putative SUMO substrates identified, namely the polypyrimidine tract-binding protein-associated splicing factor PSF, the structural microtubular component alpha-tubulin, and the GTP-binding nuclear protein Ran, were confirmed as authentic SUMO substrates. The application of this universal strategy to the identification of the pool of cellular substrates modified by other ubiquitin-like modifiers will dramatically increase our knowledge of the biological role of the different ubiquitin-like conjugations systems in the cell.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".