Molecular recognition of ubiquitin and Lys63-linked diubiquitin by STAM2 UIM-SH3 dual domain: the effect of its linker length and flexibility
Bibliographic record
Abstract
Multidomain proteins represent a broad spectrum of the protein landscape and are involved in various interactions. They could be considered as modular building blocks assembled in distinct fashion and connected by linkers of varying lengths and sequences. Due to their intrinsic flexibility, these linkers provide proteins a subtle way to modulate interactions and explore a wide range of conformational space. In the present study, we are seeking to understand the effect of the flexibility and dynamics of the linker involved in the STAM2 UIM-SH3 dual domain protein with respect to molecular recognition. We have engineered several constructs of UIM-SH3 with different length linkers or domain deletion. By means of SAXS and NMR experiments, we have shown that the modification of the linker modifies the flexibility and the dynamics of UIM-SH3. Indeed, the global tumbling of both the UIM and SH3 domain is different but not independent from each other while the length of the linker has an impact on the ps-ns time scale dynamics of the respective domains. Finally, the modification of the flexibility and dynamics of the linker has a drastic effect on the interaction of UIM-SH3 with Lys63-linked diubiquitin with a roughly eight-time weaker dissociation constant.
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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".