ERp57 Does Not Require Interactions with Calnexin and Calreticulin to Promote Assembly of Class I Histocompatibility Molecules, and It Enhances Peptide Loading Independently of Its Redox Activity
Bibliographic record
Abstract
ERp57 is a thiol oxidoreductase that catalyzes disulfide formation in heavy chains of class I histocompatibility molecules. It also forms a mixed disulfide with tapasin within the class I peptide loading complex, stabilizing the complex and promoting efficient binding of peptides to class I molecules. Since ERp57 associates with the lectin chaperones calnexin and calreticulin, it is thought that ERp57 requires these chaperones to gain access to its substrates. To test this idea, we examined class I biogenesis in cells lacking calnexin or calreticulin or that express an ERp57 mutant that fails to bind to these chaperones. Remarkably, heavy chain disulfides formed at the same rate in these cells as in wild type cells. Moreover, ERp57 formed a mixed disulfide with tapasin and promoted efficient peptide loading in the absence of interactions with calnexin and calreticulin. These findings suggest that ERp57 has the capacity to recognize its substrates directly in addition to being recruited through lectin chaperones. We also found that calreticulin could be recruited into the peptide loading complex in the absence of interactions with both ERp57 and substrate oligosaccharides, demonstrating the importance of its polypeptide binding site in substrate recognition. Finally, by inactivating the redox-active sites of ERp57, we demonstrate that its enzymatic activity is dispensable in stabilizing the peptide loading complex and in supporting efficient peptide loading. Thus, ERp57 appears to play a structural rather than catalytic role within the peptide loading complex.
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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 | 0.001 |
| 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".