The impact of Spike mutations on SARS-CoV-2 neutralization
Bibliographic record
Abstract
Abstract Multiple SARS-CoV-2 vaccines have shown protective efficacy, which is most likely mediated by neutralizing antibodies recognizing the viral entry protein, Spike. Antibodies from SARS-CoV-2 infection neutralize the virus by focused targeting of Spike and there is limited serum cross-neutralization of the closely-related SARS-CoV. As new SARS-CoV-2 variants are rapidly emerging, exemplified by the B.1.1.7, 501Y.V2 and P.1 lineages, it is critical to understand if antibody responses induced by infection with the original SARS-CoV-2 virus or the current vaccines will remain effective against virus variants. In this study we evaluate neutralization of a series of mutated Spike pseudotypes including a B.1.1.7 Spike pseudotype. The analyses of a panel of Spike-specific monoclonal antibodies revealed that the neutralizing activity of some antibodies was dramatically reduced by Spike mutations. In contrast, polyclonal antibodies in the serum of patients infected in early 2020 remained active against most mutated Spike pseudotypes. The majority of serum samples were equally able to neutralize the B.1.1.7 Spike pseudotype, however potency was reduced in a small number of samples (3 of 36) by 5–10-fold. This work highlights that changes in the SARS-CoV-2 Spike can alter neutralization sensitivity and underlines the need for effective real-time monitoring of emerging mutations and their impact on vaccine efficacy.
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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 | 0.001 |
| 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 | 0.000 |
| 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".