Mimicry of molecular glues using dual covalent chimeras
Bibliographic record
Abstract
A special class of proximity inducing bifunctional molecules/chimeras called molecular glues leverage positive co-operativity to stabilize ternary complex formation and induce a biological response. Despite their utility, molecular glues remain challenging to rationally design. This is particularly true in the context of inducing cell-cell proximity which involve ternary complexes that comprise non- or negatively interacting proteins. In this work, we develop a dual proximity labeling strategy enabling a chimera to covalently crosslink a non-interacting serum antibody to a tumor surface protein, within a ternary complex. The resultant resistance to dissociation, including in the presence of competitor binding ligands, mimics molecular glue stabilization. We demonstrate these covalent glue mimics (CGMs) can induce cell-cell proximity in three distinct model systems of tumor-immune recognition, leading to significant functional enhancements. Collectively, this work underscores the utility of dual proximal covalent labeling as a potential general strategy to stabilize ternary complexes comprising non-interacting proteins and enforce cell-cell interactions. The authors report the development of dual covalent proximity inducing molecules capable of selectively cross-linking two non-interacting proteins. Through the selective proximity-based activation of two SuFEx electrophiles, a functional ternary complex of interest can be trapped kinetically which mimics the thermodynamic stabilization associated with molecular glues. This work further demonstrates that “covalent glue mimics” can efficiently and selectively crosslink tumor immunotherapeutically relevant proteins to stabilize and promote cell-cell interactions enacting tumor cell clearance.
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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 | 0.001 |
| Research integrity | 0.001 | 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".