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| Prediction of T/B cell epitopes specific to middle east respiratory syndrome coronavirus by immunoinformatics method |
| HUANG Zhuanqing1 SUN Qi1 YANG Sen1 LI Yuanyuan1 SHI Haoyuan1 ZHANG Ying1 GONG Hui1 XU Fenghua1 |
1.Pharmaceutical Sciences Research Division, Department of Pharmacy, Medical Supplies Centre, Chinese People’s Liberation Army General Hospital, Beijing 100853, China;
2.Medical School of Chinese People’s Liberation Army, Beijing 100853, China
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Abstract Objective To predict the T/B cell epitopes against middle east respiratory syndrome coronavirus (MERS-CoV) by immunoinformatics methods. Methods After obtaining S protein sequence from NCBI, MEGA11 was used to compare multiple sequences and construct phylogenetic tree. The physicochemical properties of S protein were analyzed by Expasy Protparam and its secondary structure was predicted by SOPMA. Subsequently, S protein was modeled and verified. Killer T cell (CTL) epitopes were predicted by NetCTL-1.2, NetMHC pan EL 4.1, and IEDB, while helper T cell (HTL) epitopes were predicted by NetMHCⅡpan-4.0, IFNepitope, and IL-4pred. ABCpred and BepiPred-2.0 predict linear B cell epitopes (LBL), and the ElliPro tool predicts conformational B cell epitopes (CBL). Finally, the antigenicity, sensitization, and toxicity of the predicted linear epitopes were predicted. Results The S protein sequence was highly conserved, and 100 MERS-CoV S proteins from different countries could fit into the same phylogenetic clade. The results of physicochemical analysis showed that the total mean value of hydrophilicity of S protein was -0.078, and the half-life of S protein in mammalian reticulocytes was about 30 h. The model verification results showed that the model of S protein was reasonable. Two CTL epitopes, two HTL epitopes, and 15 LBL epitopes with antigenicity, non-allergenicity and non-toxicity were predicted from S protein. The ElliPro tool predicted five CBL epitopes. Conclusion The S protein of MERS-CoV is a hydrophilic stable protein. T/B cell antigen epitopes can be predicted by combining various bioinformatics methods, which has important reference significance for the development of polypeptide vaccine against MERS-CoV.
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