Leveraging Covalency to Stabilize Ternary Complex Formation For Cell–Cell “Induced Proximity”
Bibliographic record
Abstract
Recent advances in the field of translational chemical biology use diverse “proximity-inducing” synthetic modalities to elicit new modes of “event driven” pharmacology. These include mechanisms of targeted protein degradation and immune clearance of pathogenic cells. Heterobifunctional “chimeric” compounds like Proteolysis TArgeting Chimeras (PROTACs) and Antibody Recruiting Molecules (ARMs) leverage these mechanisms, respectively. Both systems function through the formation of reversible “ternary” or higher-order biomolecular complexes. Critical to function are key parameters, such as bifunctional molecule affinity for endogenous proteins, target residence time, and turnover. To probe the mechanism and enhance function, covalent chemical approaches have been developed to kinetically stabilize ternary complexes. These include electrophilic PROTACs and Covalent Immune Recruiters (CIRs), the latter designed to uniquely enforce cell–cell induced proximity. Inducing cell–cell proximity is associated with key challenges arising from a combination of steric and/or mechanical based destabilizing forces on the ternary complex. These factors can attenuate the formation of ternary complexes driven by high affinity bifunctional/proximity inducing molecules. This Account describes initial efforts in our lab to address these challenges using the CIR strategy in antibody recruitment or receptor engineered T cell model systems of cell–cell induced proximity. ARMs form ternary complexes with serum antibodies and surface protein antigens on tumor cells that subsequently engage immune cells via Fc receptors. Binding and clustering of Fc receptors trigger immune cell killing of the tumor cell. We applied the CIR strategy to convert ARMs to covalent chimeras, which “irreversibly” recruit serum antibodies to tumor cells. These covalent chimeras leverage electrophile preorganization and kinetic effective molarity to achieve fast and selective covalent engagement of the target ternary complex protein, e.g., serum antibody. Importantly, covalent engagement can proceed via diverse binding site amino acids beyond cysteine. Covalent chimeras demonstrated striking functional enhancements compared to noncovalent ARM analogs in functional immune assays. We revealed this enhancement was in fact due to the increased kinetic stability and not concentration, of ternary complexes. This finding was recapitulated using analogous CIR modalities that integrate peptidic or carbohydrate binding ligands with Sulfur(VI) Fluoride Exchange (SuFEx) electrophiles to induce cell–cell proximity. Mechanistic studies in a distinct model system that uses T cells engineered with receptors that recognize covalent chimeras or ARMs, revealed covalent receptor engagement uniquely enforces downstream activation signaling. Finally, this Account discusses potential challenges and future directions for adapting and optimizing covalent chimeric/bifunctional molecules for diverse applications in cell–cell induced proximity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 teacher head, 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".