Immunoinformatics and reverse vaccinology approach in designing a novel highly immunogenic multivalent peptide-based vaccine against the human monkeypox virus
Bibliographic record
Abstract
Background: Monkeypox is a highly infectious zoonotic disease, often resulting in complications ranging from respiratory illnesses to vision loss. The escalating global incidence of its cases demands prompt attention, as the absence of a proven post-exposure treatment underscores the criticality of developing an effective vaccine. Methods: Interactions of the viral proteins with TLR2 and TLR4 were investigated to assess their immunogenic potentials. Highly immunogenic proteins were selected and subjected to epitope mapping for identifying B-cell and MHC class I and II epitopes. Epitopes with high antigenicity were chosen, considering global population coverage. A multi-target, multi-epitope vaccine peptide was designed, incorporating a beta-defensin 2 adjuvant, B-cell epitopes, and MHC class I and II epitopes. Results: The coordinate structure of the engineered vaccine was modeled and validated. In addition, its physicochemical properties, antigenicity, allergenicity, and virulence traits were evaluated. Molecular docking studies indicated strong interactions between the vaccine peptide and the TLR2 receptor. Furthermore, molecular dynamics simulations and immune simulation studies reflected its potent cytosolic stability and robust immune response dynamics induced by the vaccine. Conclusion: This study explored an innovative structure-guided approach in the use of immunoinformatics and reverse vaccinology in pursuit of a novel multi-epitope vaccine against the highly immunogenic monkeypox viral proteins. The simulation studies indicated the engineered vaccine candidate to be promising in providing prophylaxis to the monkeypox virus; nevertheless, further in vitro and in vivo investigations are required to prove its 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".