Insights into High Affinity Small Ubiquitin-like Modifier (SUMO) Recognition by SUMO-interacting Motifs (SIMs) Revealed by a Combination of NMR and Peptide Array Analysis
Bibliographic record
Abstract
The small ubiquitin-like modifiers (SUMOs) regulate many essential cellular functions. Only one type of SUMO-interacting motif (SIM) has been identified that can extend the β-sheet of SUMO as either a parallel or an antiparallel strand. The molecular determinants of the bound orientation and paralogue specificity of a SIM are unclear. To address this question, we have conducted structural studies of SUMO1 in complex with a SUMO1-specific SIM that binds to SUMO1 with high affinity without post-translational modifications using nuclear magnetic resonance methods. In addition, the SIM sequence requirements have been investigated by peptide arrays in comparison with another high affinity SIM that binds in the opposing orientation. We found that antiparallel binding SIMs tolerate more diverse sequences, whereas the parallel binding SIMs prefer the more strict sequences consisting of (I/V)DLT that have a preference in high affinity SUMO2 and -3 binding. Comparison of two high affinity SUMO1-binding SIMs that bind in opposing orientations has revealed common SUMO1-specific interactions needed for high affinity binding. This study has significantly advanced our understanding of the molecular determinants underlining SUMO-SIM recognition.
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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".