A covalent opsonization approach to enhance synthetic immunity against viral escape variants
Why this work is in the frame
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Bibliographic record
Abstract
The sensitivity of therapeutic antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral "escape" mutations has inspired efforts to develop treatment strategies that are still effective in the face of rapidly mutating viral surface proteins. Here, we demonstrate a chemical strategy that enforces viral opsonization by natural serum antibodies. This strategy uses chimeric molecules that we call covalent viral opsonizers, which covalently label viral surface proteins, with synthetic antibody-binding ligands. As a proof of concept, we develop covalent viral opsonizers that covalently label the spike protein on SARS-CoV-2 using a "mutation-proof" small-molecule-binding ligand for anti-dinitrophenyl serum antibodies. In model assays, we observe that covalent viral opsonizers can rapidly and selectively covalently label the receptor-binding domain of both native and mutant spike proteins, leading to antibody opsonization. Opsonization mediated by this strategy is able to efficiently block the key binding domain interactions, in contrast to non-covalent analogs. We also show that covalent viral opsonizers enact targeted anti-viral phagocytotic immune function. This strategy has potential general utility for the rapid deployment of anti-viral synthetic immunotherapeutics at the onset of a new pandemic to reinforce vaccination and antibody engineering efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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) | 0.000 | 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 it