Workflow for E3 Ligase Ligand Validation for PROTAC Development
Bibliographic record
Abstract
Proteolysis targeting chimeras (PROTACs) have gained considerable attention as a new modality in drug discovery. The development of PROTACs has been mainly focused on using CRBN (Cereblon) and VHL (Von Hippel-Lindau ligase) E3 ligase ligands. However, the considerable size of the human E3 ligase family, newly developed E3 ligase ligands, and the favorable druggability of some E3 ligase families hold the promise that novel degraders with unique pharmacological properties will be designed in the future using this large E3 ligase space. Here, we developed a workflow aiming to improve and streamline the evaluation of E3 ligase ligand efficiency for PROTAC development and the assessment of the corresponding "degradable" target space using broad-spectrum kinase inhibitors and the well-established VHL ligand VH032 as a validation system. Our study revealed VH032 linker attachment points that are highly efficient for kinase degradation as well as some of the pitfalls when using protein degradation as a readout. For instance, cytotoxicity was identified as a major mechanism leading to PROTAC- and VHL-independent kinase degradation. The combination of E3 ligase ligand negative controls, competition by kinase parent compounds, and neddylation and proteasome inhibitors was essential to distinguish between VHL-dependent and -independent kinase degradation events. We share here the findings and limitations of our study and hope that this study will provide guidance for future evaluations of new E3 ligase ligand systems for degrader development.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".