The molecular basis of the neutralization breadth of the RBD-specific antibody CoV11
Bibliographic record
Abstract
SARS-CoV-2, the virus behind the COVID-19 pandemic, has changed over time to the extent that the current virus is substantially different from what originally led to the pandemic in 2019–2020. Viral variants have modified the severity and transmissibility of the disease and continue do so. How much of this change is due to viral fitness versus a response to immune pressure is hard to define. One class of antibodies that continues to afford some level of protection from emerging variants are those that closely overlap the binding site for angiotensin-converting enzyme 2 (ACE2) on the receptor binding domain (RBD). Some members of this class that were identified early in the course of the pandemic arose from the VH 3-53 germline gene (IGHV3-53*01) and had short heavy chain complementarity-determining region 3s (CDR H3s). Here, we describe the molecular basis of the SARS-CoV-2 RBD recognition by the anti-RBD monoclonal antibody CoV11 isolated early in the COVID-19 pandemic and show how its unique mode of binding the RBD determines its neutralization breadth. CoV11 utilizes a heavy chain VH 3-53 and a light chain VK 3-20 germline sequence to bind to the RBD. Two of CoV11’s four heavy chain changes from the VH 3-53 germline sequence, ThrFWR H128 to Ile and SerCDR H131 to Arg, and some unique features in its CDR H3 increase its affinity to the RBD, while the four light chain changes from the VK 3-20 germline sequence sit outside of the RBD binding site. Antibodies of this type can retain significant affinity and neutralization potency against variants of concern (VOCs) that have diverged significantly from original virus lineage such as the prevalent omicron variant. We also discuss the mechanism by which VH 3-53 encoded antibodies recognize spike antigen and show how minimal changes to their sequence, their choice of light chain, and their mode of binding influence their affinity and impact their neutralization breadth.
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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) | 0.002 | 0.001 |
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".