Development a novel multi-epitope DNA vaccine against human SARS coronavirus-2: an immunoinformatics designing study
Bibliographic record
Abstract
Human SARS coronavirus 2 (SARS-CoV-2) causes the current global COVID-19 pandemic. The production of an efficient vaccine against COVID-19 is under heavy investigation. In this study, we have designed a novel multiepitope DNA vaccine against SARS-CoV-2 using reverse vaccinology and DNA vaccine approaches. Applying these strategies led to reduce the time and costs of vaccine development and also improve the immune protective characteristics of the vaccine. For this purpose, epitopes of nucleocapsid, membrane glycoprotein, and ORF8 proteins of SARS-CoV-2 chose as targets for B and T-cell receptors. Accordingly, DNA sequences of selected epitopes have optimized for protein expression in the eukaryotic system. To this end, the Kozak and tissue plasminogen activator sequences were added into the epitope sequences for proper protein expression and secretion, respectively. Furthermore, interleukin-2 and beta-defensin 1 preproprotein sequences were incorporated to the designed DNA vaccine as an adjuvant. Modeling and refinement of fused protein composed of SARS-CoV-2 multiepitope antigens (fuspMA) have performed based on homology modeling of orthologous peptides, then constructed 3D model of fuspMA was more investigated during 50 ns of molecular dynamics simulation. Further bioinformatics predictions demonstrated that fuspMA is a stable protein with acceptable antigenic features and no allergenicity or toxicity characteristics. Finally, the affinity of fuspMA to the MHC I and II and TLRs molecules validated by the molecular docking procedure. In conclusion, it seems the designed multiepitope DNA vaccine could have a chance to be introduced as an efficient vaccine against COVID-19 after more in vivo evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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 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".