Efficient Sampling of PROTAC-Induced Ternary Complexes
Bibliographic record
Abstract
Abstract Proteolysis targeting chimeras (PROTACs) are bifunctional small molecules that recruit an E3 ligase to a target protein, leading to ubiquitin transfer and subsequent proteasomal degradation. The formation of ternary complexes is a crucial step in PROTAC-induced protein degradation, and gaining structural insights is essential for rational PROTAC design. In this study, we present a novel approach for efficiently sampling PROTAC-induced ternary complexes, which has been validated using 40 co-crystallized ternary complex structures. In comparison to protein-protein docking-based integrative approaches, our method achieved an impressive success rate of 97% and 50% retrospectively, measured by C α -RMSD to the crystal structure within 10 and 4 Å, respectively, with an average CPU time of 4 hours. Notably, utilizing unbound protein structures, the C α -RMSD values between the predicted and experimental structures were consistently within 7 Å across six WDR5-PROTAC-VHL ternary structures. Our open-source software enables the modeling of ternary structures in a single step and holds promise for enhancing PROTAC design efforts. TOC
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".