Generation and Characterization of Engineered Ubiquitin Variants to Modulate the Ubiquitin Signaling Cascade
Bibliographic record
Abstract
The ubiquitin signaling cascade plays a crucial role in human cells. Consistent with this, malfunction of ubiquitination and deubiquitination is implicated in the initiation and progression of numerous human diseases, including cancer. Therefore, the development of potent and specific modulators of ubiquitin signal transduction has been at the forefront of drug development. In the past decade, a structure-based combinatorial protein-engineering approach has been used to generate ubiquitin variants (UbVs) as protein-based modulators of multiple components in the ubiquitin-proteasome system. Here, we review the design and generation of phage-displayed UbV libraries, including the processes of binder selection and library improvement. We also provide a comprehensive overview of the general in vitro and cellular methodologies involved in characterizing UbV binders. Finally, we describe two recent applications of UbVs for developing molecules with therapeutic potential.
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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".