Rapid Systematic Screening of Bispecific Antibody Surrogate Geometries for T-Cell Engagement Using DNA Nanotechnology
Bibliographic record
Abstract
Bispecific antibodies (bsAbs) are emerging immune-therapeutics, and many formats exist that differ considerably in structure. However, little systematic data exist about how the spatial organization of their components influences activity, requiring innovative approaches combining empirical and quantitative frameworks. This study presents a modular DNA nanotechnology platform to generate numerous bsAbs with surrogate geometries that span the structural features of the BiTE, IgG-like, and IgG-conjugate platforms to screen for T-cell engagement. Results highlight interesting structure-activity relationships regarding bsAb potency and selectivity and raise questions regarding the molecular phenomena underlying activity. To elucidate some effects, the platform was paired with a simple mathematical model. This work is thus one of the first to systematically investigate and reveal the importance of the spatial organization of bsAb components on activity and equally provides an accessible and convenient tool for rapidly mapping out such trends for other combinations of target epitopes.
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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 | 0.001 |
| 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".