A New Method of Purification of Proteasome Substrates Reveals Polyubiquitination of 20 S Proteasome Subunits
Bibliographic record
Abstract
The 26 S proteasome is implicated in the control of many major biological functions but a reliable method for the identification of its major substrates, i.e. polyubiquitin (Ub) conjugates, is still lacking. Based on the steps present in cells, i.e. recognition and deubiquitination, we developed an affinity matrix-based purification of polyUb conjugates suitable for any biological sample. Ub-conjugates were first purified from proteasome inhibitor-treated C2C12 cells using the Ub binding domains of the S5a proteasome subunit bound to an affinity matrix and then deubiquitinated by the catalytic domain of the USP2 enzyme. This two step purification of proteasome substrates involving both protein-protein interactions and enzyme-mediated release allowed highly specific isolation of polyUb 26 S proteasome substrates, which were then resolved on two-dimensional gels post-deubiquitination. To establish our method, we focused on a gel area where spots were best resolved. Surprisingly, spot analysis by mass spectrometry identified alpha2, alpha6, alpha7, beta2, beta3, beta4, and beta5 20 S proteasome subunits as potential substrates. Western blots using an anti-beta3 proteasome subunit antibody confirmed that high molecular weight forms of beta3 were present, particularly in proteasome inhibitor-treated cells. Sucrose gradients of cell lysates suggested that the proteasome was first disassembled before subunits were polyubiquitinated. Altogether, we provide a technique that enables large scale identification of 26 S proteasome substrates that should contribute to a better understanding of this proteolytic machinery in any living cell and/or organ/tissue. Furthermore, the data suggest that proteasome homeostasis involves an autoregulatory mechanism.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".