期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 60, 期 1, 页码 421-433出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.9b01051
关键词
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类别
资金
- CSIR
- DST-PURSE
- DST-SERB
- University of Delhi
- UGC
Acute encephalitis syndrome outbreak has emerged as a major health concern on both national and international scales. Brain inflammation/infections caused by Japanese encephalitis virus (JEV) can lead to death. The cases are growing in numbers globally, and this emergent health concern requires an effective and viable vaccine to strengthen the body's immune system against this deadly virus. Proteomic analyses of JEV revealed the envelope protein as a potential target for vaccine development by patient samples analysis. Hence, in this study, we aimed to design a multiepitope subunit vaccine for acute encephalitis using the advanced structural biology and immunoinformatics approaches. We report the multiepitope subunit vaccine consisted of the putative T-cell epitope (MHC-1 and MHC-2 restricted) and B-cell epitope and with high antigenicity and immunogenicity. The TAP affinity epitopes along with adjuvants were engineered to the vaccine, to ensure the ease transportation inside the host and elicitation of a strong immune response. The specificity of vaccine construct was evaluated by molecular docking with major histocompatibility complex (MHC) receptors and host membrane receptor TLR2. High docking scores and a close interaction to the binding groove of receptors confirmed the potency and specificity of the vaccine. Also, molecular dynamics simulation studies confirmed the stable interaction of vaccine with TLR2 for a long run (100 ns), which showed the prolonged elicitation of the strong immune response. Peptide dynamics studies showed the flexible, strong, and stable binding of vaccine with minimal deviation in root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and secondary structure estimation (SSE) plots till 100 ns simulation run. The in silico immune simulation approach based on the position-specific scoring matrix and machine learning methods resulted in the strong immune response reinforcement statistics of immune cells (T-cells, B-cells population, and memory cells) in response to vaccine candidate. The favorable results and well-correlated data of varied in silico techniques paved for a potent multiepitope vaccine and helped us to propose the mechanism of action of designed vaccine and generation of the immune response against acute encephalitis syndrome.
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