Designing a polyvalent vaccine targeting multiple strains of varicella zoster virus using integrated bioinformatics approaches
Bibliographic record
Abstract
The Varicella Zoster Virus (VZV) presents a global health challenge due to its dual manifestations of chickenpox and shingles. Despite vaccination efforts, incomplete coverage, and waning immunity lead to recurrent infections, especially in aging and immunocompromised individuals. Existing vaccines prevent chickenpox but can trigger the reactivation of shingles. To address these limitations, we propose a polyvalent multiepitope subunit vaccine targeting key envelope glycoproteins of VZV. Through bioinformatics approaches, we selected six glycoproteins that are crucial for viral infection. Epitope mapping led to the identification of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B cell linear (LBL) epitopes. Incorporating strong immunostimulants, we designed two vaccine constructs, demonstrating high antigenicity, solubility, stability, and compatibility with Toll-like receptors (TLRs). Molecular docking and dynamics simulations underscored the stability and affinity of the vaccine constructs with TLRs. These findings lay the foundation for a comprehensive solution to VZV infections, addressing the challenges of incomplete immunity and shingles reactivation. By employing advanced immunoinformatics and dynamics strategies, we have developed a promising polyvalent multiepitope subunit vaccine candidate, poised to enhance protection against VZV and its associated diseases. Further validation through in vivo studies is crucial to confirm the effectiveness and potential of the vaccine to curb the spread of VZV. This innovative approach not only contributes to VZV control but also offers insights into tailored vaccine design strategies against complex viral pathogens.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".