Multi-Epitope Vaccine Design Using an Immunoinformatic Approach for SARS-CoV-2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Through 4 June 2021, COVID-19 has caused over 172.84 million cases of infection and 3.71 million deaths worldwide. Due to its rapid dissemination and high mutation rate, it is essential to develop a vaccine harboring multiple epitopes and efficacious against multiple variants to prevent the immune escape of SARS-CoV-2. An in silico approach based on the viral genome was applied to identify 19 high-immunogenic B-cell epitopes and 499 human leukocyte antigen (HLA)-restricted T-cell epitopes. Thirty multi-epitope peptide vaccines were designed by iNeo-Suite and manufactured by solid-phase synthesis. Docking analysis confirmed stable hydrogen bonds of epitopes with their corresponding HLA alleles. When four peptide candidates derived from the spike protein of SARS-CoV-2 were selected to immunize mice, a significantly larger amount of total IgG in serum, as well as an increase of CD19+ cells in the inguinal lymph nodes, were observed in the peptide-immunized mice compared to the control. The ratios of IFN-γ-secreting lymphocytes in CD4+ or CD8+ T-cells in the peptide-immunized mice were higher than those in the control mice. There were also a larger number of IFN-γ-secreting T-cells in the spleens of peptide-immunized mice. The peptide vaccines in this study successfully elicited antigen-specific humoral and cellular immune responses in mice. To further validate the safety and efficacy of this vaccine, animal studies using a primate model, as well as clinical trials in humans, are required.
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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 | 0.000 | 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) | 0.000 | 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 it