Electrophilic proximity-inducing synthetic adapters enhance universal T cell function by covalently enforcing immune receptor signaling
Bibliographic record
Abstract
Proximity-induction of cell-cell interactions via small molecules represents an emerging field in basic and translational sciences. Covalent anchoring of these small molecules represents a useful chemical strategy to enforce proximity; however, it remains largely unexplored for driving cell-cell interactions. In immunotherapeutic applications, bifunctional small molecules are attractive tools for inducing proximity between immune effector cells like T cells and tumor cells to induce tumoricidal function. We describe a two-component system composed of electrophilic bifunctional small molecules and paired synthetic antigen receptors (SARs) that elicit T cell activation. The molecules, termed covalent immune recruiters (CIRs), were designed to affinity label and covalently engage SARs. We evaluated the utility of CIRs to direct anti-tumor function of human T cells engineered with three biologically distinct classes of SAR. Irrespective of the electrophilic chemistry, tumor-targeting moiety, or SAR design, CIRs outperformed equivalent non-covalent bifunctional adapters, establishing a key role for covalency in maximizing functionality. We determined that covalent linkage enforced early T cell activation events in a manner that was dependent upon each SARs biology and signaling threshold. These results provide a platform to optimize universal SAR-T cell functionality and more broadly reveal new insights into how covalent adapters modulate cell-cell proximity-induction.
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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) | 0.001 | 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".