A covalent opsonization approach to enhance synthetic immunity against viral escape variants
Bibliographic record
Abstract
CoVID-19 has highlighted the need for rapidly deployable therapeutics in the event of future pandemics. Monoclonal Antibodies (mAbs) represent an effective anti-viral immunotherapy, operating through multiple potential mechanisms of action, namely viral entry blockade and immune engagement. Mutations in viral surface proteins however can lead to “escape mutants” no longer recognized by therapeutic antibodies which are intrinsically highly specific. This inspired our efforts to design an off-the-shelf synthetic immunity strategy, that enables efficient viral opsonization by natural serum antibodies, and with enhanced resiliency to viral mutations. In this strategy, we hypothesized chimeric molecules could be designed to bind and subsequently covalently label viral surface proteins, with high affinity antibody binding ligands inert to mutation. Here, covalency is used to strategically enforce immune engagement of lower affinity viral protein mutants. In this study, we report on the development of SARS-COV-2 covalent viral opsonizer molecules (CVOs). CVOs are equipped with both a high affinity immune binding ligand and an affinity labeling motif that directs a fast SuFEx bio-conjugation reaction with spike RBD variants. CVOs were observed to rapidly and selectively covalently label the Receptor Binding Domain (RBD) of both native and mutant spike proteins, with a high affinity hapten recognized by anti-dinitrophenyl (DNP) serum antibodies. In model assays, we demonstrate the CVO is able to efficiently block the RBD:ACE2 interaction via antibody recruitment in contrast to non-covalent (N)CVO analogs. Finally, we show the CVO is able to enact targeted anti-viral opsonizing immune function in antibody dependent cellular phagocytosis assays (ADCP). We envision 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. Funding Information: This work was funded by NSERC, ANNA Biosciences and Cancer Research Society, Declaration of Interests: None to declare.
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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 | 0.001 |
| 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".