A Single Mms2 “Key” Residue Insertion into a Ubc13 Pocket Determines the Interface Specificity of a Human Lys63 Ubiquitin Conjugation Complex
Bibliographic record
Abstract
Human Ubc13 and Mms2 (or its homolog, Uev1) form a unique ubiquitin-conjugating enzyme (Ubc) complex that generates atypical Lys(63)-linked ubiquitin conjugates. Such conjugates are attached to specific targets that modulate the activity of various cellular processes including DNA repair, mitotic progression, and nuclear factor-kappaB signaling. Whereas Ubc13 is a typical Ubc, Mms2 is a non-catalytic Ubc variant. Substantial biochemical evidence has revealed a mechanism whereby Mms2 properly orients ubiquitin to allow for Lys(63) conjugation by Ubc13; however, how this specific Ubc13-Mms2 complex is formed and why Mms2 does not form a complex with other Ubcs have not been reported. In order to address these questions, we used a structure-based approach to design mutations and characterize the human Ubc13-Mms2 interface. We used the yeast two-hybrid assay, glutathione S-transferase pull-downs, and surface plasmon resonance to test in vivo and in vitro binding. These experiments were paired with functional complementation and ubiquitin conjugation studies to provide in vivo and in vitro functional data. The results in this study allowed us to identify important residues of the Ubc13-Mms2 interface, determine a correlation between heterodimer formation and function, and conclude why Mms2 forms a specific complex with Ubc13 but not other Ubc proteins.
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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) | 0.001 | 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".