A Proteomic Approach To Identify Candidate Substrates of Human Adenovirus E4orf6-E1B55K and Other Viral Cullin-Based E3 Ubiquitin Ligases
Bibliographic record
Abstract
It has been known for some time that the human adenovirus serotype 5 (Ad5) E4orf6 and E1B55K proteins work in concert to degrade p53 and to regulate selective export of late viral mRNAs during productive infection. Both of these functions rely on the formation by the Ad5 E4orf6 protein of a cullin 5-based E3 ubiquitin ligase complex containing elongins B and C. E1B55K is believed to function as the substrate recognition module for the complex and, in addition to p53, Mre11 and DNA ligase IV have also been identified as substrates. To discover additional substrates we have taken a proteomic approach by using two-dimensional difference gel electrophoresis to detect cellular proteins that decrease significantly in amount in p53-null H1299 human lung carcinoma cells after expression of E1B55K and E4orf6 using adenovirus vectors. Several species were detected and identified by mass spectroscopy, and for one of these, integrin alpha3, we went on in a parallel study to confirm it as a bone fide substrate of the complex (F. Dallaire et al., J. Virol. 83:5329-5338, 2009). Although the system has some limitations, it may still be of some general use in identifying candidate substrates of any viral cullin-based E3 ubiquitin ligase complex, and we suggest a series of criteria for substrate validation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".