bioPROTACs as versatile modulators of intracellular therapeutic targets: Application to proliferating cell nuclear antigen (PCNA)
Bibliographic record
Abstract
ABSTRACT Targeted degradation approaches have recently generated much excitement as a paradigm shift to address human disease in unprecedented ways. Amongst these, small molecule based approaches such as Proteolysis targeting chimeras (PROTACs) have attracted the lion’s share of attention due to their potential to tackle historically intractable targets and achieve greater potency, efficacy, and specificity over traditional small molecule inhibitors. Despite their promise, the identification of high-affinity ligands that can serve as starting points for PROTAC strategies remains challenging. As a complementary approach, we describe herein a class of intracellular biologics termed bioPROTACs. The substrate binding component of these fusion proteins consists of a peptide or an antibody-mimetic which allows for an unprecedented diversity of protein targets that can be addressed. The high-affinity binder is linked directly to an E3 ubiquitin ligase to harness the power of targeted degradation. Using GFP-tagged proteins as model substrates, we show that there is considerable flexibility in both the choice of substrate binders (binding positions, scaffold-class) and the E3 ligases. Indeed, 9 out of 16 binder-E3 combinations tested resulted in greater than 70% target clearance. Through a systematic approach, we then identified a highly effective bioPROTAC against an oncology target, proliferating cell nuclear antigen (PCNA), a sliding DNA clamp with critical roles in DNA replication and repair. The bioPROTAC, termed Con1-SPOP, elicited rapid and robust PCNA degradation and associated effects on DNA synthesis and cell cycle progression. Compared to RNAi-based approaches which typically take days to manifest, PCNA knockdown using Con1-SPOP was evident within 4 h. The advantage of degradation versus stoichiometric inhibition was also clearly demonstrated with bioPROTAC strategies. Combining superior pharmacological inhibition and relative ease of development, bioPROTACs are powerful tools for interrogating the degradability of a substrate, for guiding the identification of the fittest E3 ligase, for studying the functional consequences associated with target protein down-regulation, and potentially for making therapeutic impacts.
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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) | 0.000 | 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".