In silico design and evaluation of multi-epitope dengue virus vaccines: a promising approach to combat global dengue burden
Bibliographic record
Abstract
Abstract Dengue virus, a pervasive mosquito-borne pathogen, imposes a substantial global health burden and is responsible for numerous fatalities annually globally, with tropical and sub-tropical regions particularly susceptible to dengue outbreaks. Despite decades of efforts, there has been no effective treatment or prevention for dengue, which makes it a life-threatening disease. Hence, this study proposes an innovative bioinformatics-driven approach to construct a vaccine targeting the dengue virus. The study involved a comprehensive analysis of conserved regions of dengue virus serotypes 1–4's non-structural proteins (NS1, NS3, and NS5) and structural protein (E) to predict the potential B & T-cell epitopes which were linked with appropriate adjuvants and linkers to generate four distinct vaccine candidates. The constructed vaccine models underwent rigorous evaluation, considering physicochemical attributes, structural integrity, population coverage, and immune system response through simulation. The results confirm that these vaccine candidates are non-allergenic, non-toxic, antigenic, and immunogenic. Additionally, they exhibit 99.70% world population coverage and 100% conservation across all dengue strains, which is crucial for vaccine efficacy. A Ramachandran plot showed that 95.6% of the amino acid residues of the candidates belong to the optimal zone, while around 4% are in additional allowed regions. Further, molecular docking and dynamic simulation of interaction with the human toll-like receptor 4, a fundamental component of innate immunity, was carried out to gain more insight into interaction dynamics. As a result of these analyses, the candidates' binding dynamics and structural stability were revealed. Overall, this study presents promising vaccine candidates for addressing dengue's global health burden. Their robust design and demonstrated immunogenicity make them attractive candidates for further experimental testing and development as potential vaccines against current strains and future variants.
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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 | 0.000 | 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 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".