SARS-CoV-2 vaccine-breakthrough infections (VBIs) by Omicron (B.1.1.529) variant and consequences in structural and functional impact
Bibliographic record
Abstract
Abstract This study investigated the efficacy of existing vaccinations against hospitalization and infection due to the Omicron variant of COVID-19, particularly for those who received two doses of Moderna or Pfizer vaccines and one dose of a vaccine by Johnson & Johnson or who were vaccinated more than five months previously. A total of 36 variants in Omicron’s spike protein, targeted by all three vaccinations, have made antibodies less effective at neutralizing the virus. Genotyping of SARS-CoV-2 viral sequencing revealed clinically significant variants such as E484K in three genetic mutations (T95I, D614G, and del142-144). One woman displayed two of these mutations, indicating a potential risk of infection following successful immunization, as recently reported by Hacisuleyman (2021). We examined the effects of mutations on domains (NID, RBM, and SD2) found at the interfaces of spike domains Omicron B.1.1529, Delta/B.1.1529, Alpha/B.1.1.7, VUM B.1.526, B.1.575.2, and B.1.1214 (formerly VOI Iota). We tested the affinity of Omicron for hACE2 and found that the wild and mutant spike proteins were using atomistic molecular dynamics simulations. According to binding free energies calculated during mutagenesis, hACE2 bound Omicron spike more strongly than SARS-CoV-2 wild strain. T95I, D614G, and E484K are three substitutions that significantly contribute to the RBD, corresponding to hACE2 binding energies and a doubling of Omicron spike proteins’ electrostatic potential. Omicron appears to bind hACE2 with greater affinity, increasing its infectivity and transmissibility. The spike virus was designed to strengthen antibody immune evasion through binding while boosting receptor binding by enhancing IgG and IgM antibodies that stimulate human β -cell, as opposed to the wild strain, which has more vital stimulation of both antibodies.
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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 | 0.000 |
| 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".