Bibliographic record
Abstract
The rise of proteomics has had tremendous influence on analysis and understanding of the role of post-translational modifications in biological processes. The covalent attachment of small proteins like ubiquitin, SUMO,(1) or other ubiquitin-like proteins (Ubls) is one class of post-translational modifications where proteomics has had notable impact. Various proteomics approaches, but in particular mass spectrometry-based analyses, have influenced the field and enabled significant advances over the past few years. The first meeting dedicated to proteomics of protein degradation and ubiquitin pathways showcased these advances and allowed a glimpse at future contributions of proteomics to this field. With its many attractive drug targets, the ubiquitin and proteasome system, as well as other proteolysis pathways, could offer new therapies for various human diseases including cancer and neurodegenerative disorders. The covalent linkage of ubiquitin to other proteins is catalyzed by the E1-E2-E3 cascade of enzymatic reactions whereby the many different E3 ubiquitin ligases provide substrate specificity to the process of protein ubiquitylation (1). Ubiquitylation is best known for targeting proteins for degradation by the proteasome, but other functions for ubiquitylation independent of proteolysis are also known. Likewise, modifications with SUMO or other Ubls generally do not regulate protein degradation but instead control subcellular localization, protein interactions, or change protein conformation and activity (2). The questions addressed by proteomics approaches to ubiquitylation and Ubl modifications are plentiful. They range from very specific, e.g. determination of the modified residue in a substrate protein, to complex, such as protein dynamics in proteome-wide ubiquitin (or Ubl) modification profiles (3). In either case, the rapid technological advancements (particularly in mass spectrometry instrumentation as well as quantitation and separation technologies) have allowed impressive progress, which was evident in the First Conference on Proteomics of Protein Degradation and Ubiquitin Pathways in Vancouver (http://ppdup.org/) (Fig. 1).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.276 | 0.270 |
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".