Proteome‐wide identification of mycobacterial pupylation targets
Bibliographic record
Abstract
Mycobacteria use a unique system for covalently modifying proteins based on the conjugation of a small protein, referred to as prokaryotic ubiquitin-like protein (PUP). In this study, we report a proteome-wide analysis of endogenous pupylation targets in the model organism Mycobacterium smegmatis. On affinity capture, a total of 243 candidate pupylation targets were identified by two complementary proteomics approaches. For 41 of these protein targets, direct evidence for a total of 48 lysine-mediated pupylation acceptor sites was obtained by collision-induced dissociation spectra. For the majority of these pupylation targets (38 of 41), orthologous genes are found in the M. tuberculosis genome. Interestingly, approximately half of these proteins are involved in intermediary metabolism and respiration pathways. A considerable fraction of the remaining targets are involved in lipid metabolism, information pathways, and virulence, detoxification and adaptation. Approximately one-third of the genes encoding these targets are located in seven gene clusters, indicating functional linkages of mycobacterial pupylation targets. A comparison of the pupylome under different cell culture conditions indicates that substrate targeting for pupylation is rather dynamic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".