Virtual Discovery of Immune-Stimulating Epitopes in Chikungunya Virus for Vaccine Design

Abstract

Epitope identification is a key step in vaccine development, and this can be achieved much faster and less expensively with in silico methods, compared to traditional methods for vaccine production. In silico methods applied in this research utilised both bioinformatics and immunoinformatics approaches for chikungunya virus vaccine design, which involved the retrieval of sequences from databases, and identification of conserved regions within the sequences by multiple sequence alignment on the MEGA X software (Pennsylvania State University, State College, USA). The epitopes in the conserved regions were selected, and various immunological predictions and screenings were carried out by employing immunological databases and tools. This process identifies epitopes such as conservation of cytotoxic T lymphocyte, helper T lymphocytes, and B cell epitopes. The primary, secondary, and tertiary structure of the vaccine was also predicted using structure predicting servers, and finally, the vaccine candidate was docked to toll-like receptor 4 to study its binding affinity and configuration. A total of 125 conserved antigenic epitopes were selected from capsid, 6K, and E1 proteins, which were found to be non-allergens and conform to acceptable physicochemical standards, as reported by other authors with similar work. The epitopes were predicted to be capable of inducing cytotoxic T lymphocytes, helper T lymphocytes, and B cell production. Construction of secondary structure was done using the Self-Optimized Prediction Method with Alignment (SOPMA), which predicted 17.96% α-helices, and 4.69% β-turns, among others. Predicting the tertiary structure provided five models, of which Model 1 was selected on the bases of its confidential score of 0.59, estimated TM-score of 0.79±0.09, and root mean square deviation of 8.0±4.4Å. Validity analysis revealed a Ramachandran plot where 97.2% of the vaccine residue was within the favoured region, and the peptide showed a Z-score of -1.52. The predicted peptide effectively docked with toll-like receptor 4 with a binding energy of -1,072.8. From the data obtained, it was revealed that the selected epitopes are highly immunogenic, non-allergenic, conform to native protein, and form a peptide capable of vaccine application. The authors can conclude this is a promising candidate for vaccine design and development.