Characterization of Nanobody Binding to Distinct Regions of the SARS-CoV-2 Spike Protein by Flow Virometry
Bibliographic record
Abstract
Nanobodies, or single-domain antibodies (VHHs) from camelid heavy-chain-only antibodies, offer significant advantages in therapeutic and diagnostic applications due to their small size and ability to bind cryptic protein epitopes inaccessible to conventional antibodies. In this study, we examined nanobodies specific to regions of the SARS-CoV-2 spike glycoprotein, including the receptor-binding domain (RBD), N-terminal domain (NTD), and subunit 2 (S2). Using flow virometry, a high-throughput technique for viral quantification, we achieved the efficient detection of pseudotyped viruses expressing the spike glycoprotein. RBD-targeting nanobodies showed the most effective staining, followed by NTD-targeting ones, while S2-specific nanobodies exhibited limited resolution. The simple genetic structure of nanobodies enables the creation of multimeric formats, improving binding specificity and avidity. Bivalent VHH-Fc constructs (VHHs fused to the Fc region of human IgG) outperformed monovalent formats in resolving viral particles from background noise. However, S2-specific monovalent VHHs demonstrated improved staining efficiency, suggesting their smaller size better accesses restricted antigenic sites. Furthermore, direct staining of cell supernatants was possible without virus purification. This versatile nanobody platform, initially developed for antiviral therapy against SARS-CoV-2, can be readily adapted for flow virometry applications and other diagnostic assays.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".