Novel neutralizing SARS-CoV-2-specific mAbs offer detection of RBD linear epitopes
Bibliographic record
Abstract
BACKGROUND: To stop the spread of the COVID-19 disease, it is crucial to create molecular tools to investigate and diagnose COVID-19. Current efforts focus on developing specific neutralizing monoclonal antibodies (NmAbs) elicited against the receptor-binding domain (RBD). METHODS: In the present study, recombinant RBD (rRBD) protein was produced in E. coli, followed by immunizing mice with purified rRBD. ELISA was applied to screen the hybridomas for positive reactivity with rRBD protein. The linear and conformational epitopes of the mAbs were subsequently identified using western blot. Finally, the reactivity, affinity, and neutralization activity of the purified mAbs were evaluated using ELISA. RESULTS: All mAbs exhibited similar reactivity trends towards both eukaryotic RBD and prokaryotic rRBD in ELISA. Among them, 2E7-D2 and 2B4-G8 mAbs demonstrated higher reactivity than other mAbs. Additionally, in western blot assays, these two mAbs could detect reducing and non-reducing rRBD, indicating recognition of linear epitopes. Notably, five mAbs effectively blocked rRBD- angiotensin-converting enzyme 2 (ACE2) interaction, while two high-affinity mAbs exhibited potent neutralizing activity against eukaryotic RBD. CONCLUSION: In the current study, we generated and characterized new RBD-specific mAbs using the hybridoma technique that recognized linear and conformational epitopes in RBD with neutralization potency. Our mAbs are novel candidates for diagnosing and treating SARS-CoV-2.
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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) | 0.001 | 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 | 0.001 | 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".