Introduction: Vaccines play an important role in global health by preventing infection and transmission of multiple diseases across the globe. Ebola virus (EBOV) is an enveloped, non-segmented, negative-sense, single-stranded RNA virus of the family Filoviridae that causes severe hemorrhagic fever and is highly lethal. The disease signs and symptoms include anorexia, nausea, vomiting, abdominal and chest pain. Between 2014–2016, West Africa suffered the largest and most complex Ebola outbreak. Considering the importance of producing effective vaccines to prevent the epidemic of this disease, the purpose of this study is to investigate epitope-based peptide vaccine against the Ebola virus by in silico approach.
Methods: In this paper, we choose The Ebola VGP_EBOG4 Envelope glycoprotein. The sequence of this protein in FASTA format was retrieved from the National Center of Biotechnology Information (NCBI). For predicting peptide binding to MHC molecules, IEDB was used. This server is based on the artificial neural network (ANN) approach. This tool measures the binding affinity of a selected sequence to a definite MHC class I or II molecule. Antigenicity and toxicity of the chosen epitopes were then analyzed using vaxijen v2.0 for antigenicity and ToxlBTL for toxicity. One of the important problems with vaccines is their possible allergic reponse in humans. For this matter we analyzed the allergenicity of peptides using AllerTOP v2.0. We can finally choose the best epitope sequence by reviewing binding scores, antigenicity, and allergenicity. In order to show the binding affinity of the selected peptide and HLA, in silico molecular docking was used. Sequences of proposed epitopes that were selected were saved as a PDB file. In the next step, we retrieved receptor HLA-A*0201 (PDB ID: 4U6Y) from RCSB as a PDB file. After optimization and energy minimization, Molecular docking was performed and analyzed using Discovery Studio 3.5 Client software.
Results: We found that the best epitopes, including FFLYDRLAST, AFLILPQAKKDF, and VAFLILPQAKK, can trigger strong immune responses. The binding energy of the best-bound conformation and the ligand RMSD of AFLILPQAKKDF was -185.39 kcal/mol and 59.10 Å respectively. In molecular docking of this epitope with HLA-A*0201 (PDB ID: 4U6Y), we can see 4 hydrogen bonds by ARG97, ARG65, HIS70, and THR163.
Conclusion: Ebolavirus is one of the most dangerous global epidemics. Therefore, this study is devoted to serving as a platform to hasten vaccine development through the design of an epitope-based peptide vaccine against Ebolavirus using an immunoinformatic approach combined with molecular docking studies. Our study showed the selected epitope has a good affinity toward HLA-A*0201 as a receptor. Both in vivo and in vitro experiments are needed to support these findings.