Antibodies to neutralising epitopes synergistically block the interaction of the receptor‐binding domain of SARS‐CoV‐2 to ACE 2
Bibliographic record
Abstract
OBJECTIVES: A major COVID-19 vaccine strategy is to induce antibodies that prevent interaction between the Spike protein's receptor-binding domain (RBD) and angiotensin-converting enzyme 2 (ACE2). These vaccines will also induce T-cell responses. However, concerns were raised that aberrant vaccine-induced immune responses may exacerbate disease. We aimed to identify minimal epitopes on the RBD that would induce antibody responses that block the interaction of the RBD and ACE2 as a strategy leading to an effective vaccine with reduced risk of inducing immunopathology. METHODS: We procured a series of overlapping 20-amino acid peptides spanning the RBD and asked which were recognised by plasma from COVID-19 convalescent patients. Identified epitopes were conjugated to diphtheria-toxoid and used to vaccinate mice. Immune sera were tested for binding to the RBD and for their ability to block the interaction of the RBD and ACE2. RESULTS: Seven putative vaccine epitopes were identified. Memory B-cells (MBCs) specific for one of the epitopes were identified in the blood of convalescent patients. When used to vaccinate mice, six induced antibodies that bound recRBD and three induced antibodies that could partially block the interaction of the RBD and ACE2. However, when the sera were combined in pairs, we observed significantly enhanced inhibition of binding of RBD to ACE2. Two of the peptides were located in the main regions of the RBD known to contact ACE2. Of significant importance to vaccine development, two of the peptides were in regions that are invariant in the UK and South African strains. CONCLUSION: COVID-19 convalescent patients have SARS-CoV-2-specific antibodies and MBCs, the specificities of which can be defined with short peptides. Epitope-specific antibodies synergistically block RBD-ACE2 interaction.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".