An Immunoinformatics Approach to Design a Potent Multi-Epitope Vaccine against Asia-1 Genotype of Crimean–Congo Haemorrhagic Fever Virus Using the Structural Glycoproteins as a Target
Bibliographic record
Abstract
Crimean-Congo haemorrhagic fever (CCHF), caused by Crimean-Congo haemorrhagic fever virus (CCHFV), is a disease of worldwide importance (endemic yet not limited to Asia, Middle East, and Africa) and has triggered several outbreaks amounting to a case fatality rate of 10-40% as per the World Health Organization. Genetic diversity and phylogenetic data revealed that the Asia-1 genotype of CCHFV remained dominant in Pakistan, where 688 confirmed cases were reported between the 2012-2022 period. Currently, no approved vaccine is available to tackle the viral infection. Epitope-based vaccine design has gained significant attention in recent years due to its safety, timeliness, and cost efficiency compared to conventional vaccines. In the present study, we employed a robust immunoinformatics-based approach targeting the structural glycoproteins G1 and G2 of CCHFV (Asia-1 genotype) to design a multi-epitope vaccine construct. Five B-cells and six cytotoxic T-lymphocytes (CTL) epitopes were mapped and finalized from G1 and G2 and were fused with suitable linkers (EAAAK, GGGS, AAY, and GPGPG), a PADRE sequence (13 aa), and an adjuvant (50S ribosomal protein L7/L12) to formulate a chimeric vaccine construct. The selected CTL epitopes showed high affinity and stable binding with the binding groove of common human HLA class I molecules (HLA-A*02:01 and HLA-B*44:02) and mouse major histocompatibility complex class I molecules. The chimeric vaccine was predicted to be an antigenic, non-allergenic, and soluble molecule with a suitable physicochemical profile. Molecular docking and molecular dynamics simulation indicated a stable and energetically favourable interaction between the constructed antigen and Toll-like receptors (TLR2, TLR3, and TLR4). Our results demonstrated that innate, adaptive, and humoral immune responses could be elicited upon administration of such a potent muti-epitope vaccine construct. These results could be helpful for an experimental vaccinologist to develop an effective vaccine against the Asia-1 genotype of CCHFV.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".