Background Small ruminant morbillivirus or peste des petits ruminants virus (PPRV) is an acute and highly contagious viral disease of goats, sheep, and other livestock. This study aimed at predicting an effective multiepitope vaccine against PPRV from the immunogenic proteins haemagglutinin (H), matrix (M), fusion (F), and nucleoprotein (N) using immunoinformatics tools. Materials and Methods The sequences of the immunogenic proteins were retrieved from GenBank of the National Center for Biotechnology Information (NCBI). BioEdit software was used to align each protein from the retrieved sequences for conservancy. Immune Epitope Database (IEDB) analysis resources were used to predict B and T cell epitopes. For B cells, the criteria for electing epitopes depend on the epitope linearity, surface accessibility, and antigenicity. Results Nine epitopes from the H protein, eight epitopes from the M protein, and ten epitopes from each of the F and N proteins were predicted as linear epitopes. The surface accessibility method proposed seven surface epitopes from each of the H and F proteins in addition to six and four epitopes from the M and N proteins, respectively. For antigenicity, only two epitopes 142PPERV146 and 63DPLSP67 were predicted as antigenic from H and M, respectively. For T cells, MHC-I binding prediction tools showed multiple epitopes that interacted strongly with BoLA alleles. For instance, the epitope 45MFLSLIGLL53 from the H protein interacted with four BoLA alleles, while 276FKKILCYPL284 predicted from the M protein interacted with two alleles. Although F and N proteins demonstrated no favorable interaction with B cells, they strongly interacted with T cells. For instance, 358STKSCARTL366 from the F protein interacted with five alleles, followed by 340SQNALYPMS348 and 442IDLGPAISL450 that interacted with three alleles each. The epitopes from the N protein displayed strong interaction with BoLA alleles such as 490RSAEALFRL498 that interacted with five alleles, followed by two epitopes 2ATLLKSLAL10 and 304QQLGEVAPY312 that interacted with four alleles each. In addition to that, four epitopes 3TLLKSLALF11, 356YFDPAYFRL364, 360AYFRLGQEM368, and 412PRQAQVSFL420 interacted with three alleles each. Conclusion Fourteen epitopes were predicted as promising vaccine candidates against PPRV from four immunogenic proteins. These epitopes should be validated experimentally through in vitro and in vivo studies.
Background Recently the global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated a significant need on identifying drugs or vaccines to prevent or reduce clinical infection of Coronavirus disease – 2019 (COVID-19). In this study, immuno-informatics tools were utilized to design a potential multi-epitopes vaccine against SARS-CoV-2 spike S protein. Structural analysis for SARS-CoV-2 spike S protein was also conducted. Method: SARS-CoV-2 spike S protein sequences were retrieved from the GeneBank of National Central Biotechnology Information (NCBI). Immune Epitope Database (IEDB) tools were used to predict B and T cell epitopes, to evaluate their allergenicity, toxicity and cross- reactivity and to calculate population coverage. Protparm sever was applied to determine protein characterization of spike protein and predicted epitopes. Molecular docking for the proposed MHCI epitopes were also achieved against Tall like Receptor (TLR8) receptors and HLA-B7 allele. Result Immuno-informatics analysis of S protein using IEDB identified only one B cell epitope 1054QSAPH1058 as linear, surface and antigenic. Although 1054QSAPH1058 was estimated as non-allergic and non-toxic, it showed protein instability. Moreover, around 45 discontinuous epitopes were also recognized as different exposed surface area. In MHCI methods, six conserved stable and safe epitopes (898FAMQMAYRF906, 258WTAGAAAYY266 and 2FVFLVLLPL10, 202 KIYSKHTPI210, 712IAIPTNFTI720 and 1060VVFLHVTYV1068) were identified. These epitopes showed strong interaction when docked with TLR8 and HLA-B7 allele especially 1060VVFLHVTYV1068 and 2FVFLVLLPL10 epitopes. Three epitopes were also predicted (898FAMQMAYRF906, 888FGAGAALQI896 and 342FNATRFASV350) using MHCII methods. Furthermore, the potential multi-epitopes were acquired by assessing allergenicity, toxicity and cross-reactivity to prevent autoimmunity. Conclusion The multi-epitopes vaccine was predicted based on Bioinformatics tools that may provide reliable results in a shorter time and at a lower cost. However, further in vivo and in vitro studies are required to validate their effectiveness.
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