Overcoming Ligand Discovery Challenges: Developing Peptide-Based Tracers for SPSB2
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Developing new E3 ligase ligands for the design of heterobivalent molecules, such as PROteolysis TArgeting Chimeras (PROTACs), requires careful evaluation of target engagement (TE). Characterizing protein–protein interactions (PPIs) is therefore essential in drug discovery, as it enables the assessment of ligand binding to sites that are often difficult to target. Degrons, peptide motifs recognized by E3 ligases, may serve as valuable starting points for designing E3 ligands. However, many degrons are highly polar and lack intrinsic membrane permeability, requiring alternative strategies for efficient cellular delivery. In this study, we used the SPRY domain-containing SOCS box protein 2 (SPSB2) E3 ligase as a model system to develop TE strategies in vitro and in cellulo using polar degron-based peptides. By conjugating various polycationic cell-penetrating peptides (CPPs) to the degron sequence, we present a study demonstrating cellular delivery. We obtained a high-resolution crystal structure and used various biophysical techniques to assess the influence of each modification, while confocal microscopy and BRET-based assays confirmed successful cellular delivery as well as potent TE.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".