Defining the SUMO-modified Proteome by Multiple Approaches in Saccharomyces cerevisiae*
Bibliographic record
Abstract
SUMO, or Smt3 in Saccharomyces cerevisiae, is a ubiquitin-like protein that is post-translationally attached to multiple proteins in vivo. Many of these substrate modifications are cell cycle-regulated, and SUMO conjugation is essential for viability in most eukaryotes. However, only a limited number of SUMO-modified proteins have been definitively identified to date, and this has hampered study of the mechanisms by which SUMO ligation regulates specific cellular pathways. Here we use a combination of yeast two-hybrid screening, a high copy suppressor selection with a SUMO isopeptidase mutant, and tandem mass spectrometry to define a large set of proteins (>150) that can be modified by SUMO in budding yeast. These three approaches yielded overlapping sets of proteins with the most extensive set by far being those identified by mass spectrometry. The two-hybrid data also yielded a potential SUMO-binding motif. Functional categories of SUMO-modified proteins include SUMO conjugation system enzymes, chromatin- and gene silencing-related factors, DNA repair and genome stability proteins, stress-related proteins, transcription factors, proteins involved in translation and RNA metabolism, and a variety of metabolic enzymes. The results point to a surprisingly broad array of cellular processes regulated by SUMO conjugation and provide a starting point for detailed studies of how SUMO ligation contributes to these different regulatory mechanisms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".