Immunobiology Laboratory, Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, India.
Cellular Immunology Laboratory, Department of Zoology, The University of Burdwan, Purba Bardhhaman, India.
J Biomol Struct Dyn. 2022;40(21):10578-10591. doi: 10.1080/07391102.2021.1945495. Epub 2021 Jul 5.
Despite several extensive and exhaustive efforts, search for potential therapy against leishmaniasis has not made much progress. In the present work, we have employed mining strategy to screen proteome for identification of promising vaccine candidate. We have screened 21 potential antigenic proteins from 7960 total protein of , based on the presence of signal peptide, GPI anchor, antigenicity prediction and substractive proteomic approach. Secondly, we have also performed comprehensive immunogenic epitope prediction from the screened 21 proteins, using IEDB-AR tools. Out of the 21 antigenic proteins, we obtained 11 immunogenic epitopes from 9 proteins. The final results revealed that four predicted epitopes namely; , , and , have significantly better binding potential with respective alleles and could elicits immune responses. Docking analysis using PATCHDOCK server and molecular dynamic simulation using GROMACS revealed the potential of the sequences as immunogenic epitopes. In silico studies also suggested that the epitopes occupied almost same binding cleft with the respective alleles, when compared with the reference peptides. It is also suggested from the molecular dynamic simulation data that the peptides were intact in the pocket for longer periods of time. Our study was designed to select MHC class I restricted epitopes for the activation of CD8 T cells using immunoinformatics for the prediction of probable vaccine candidate against parasites. Communicated by Ramaswamy H. Sarma.
尽管进行了多次广泛而彻底的努力，但针对利什曼病的潜在疗法的研究并没有取得太大进展。在本工作中，我们采用挖掘策略筛选蛋白质组，以鉴定有前途的疫苗候选物。我们根据信号肽、GPI 锚、抗原性预测和减法蛋白质组学方法，从 7960 种总蛋白中筛选出 21 种潜在的抗原蛋白。其次，我们还使用 IEDB-AR 工具对筛选出的 21 种蛋白质进行了全面的免疫原性表位预测。在 21 种抗原蛋白中，我们从 9 种蛋白中获得了 11 种免疫原性表位。最终结果表明，四个预测的表位，即、、和，与各自的等位基因具有显著更好的结合潜力，可以引发免疫反应。使用 PATCHDOCK 服务器进行对接分析和使用 GROMACS 进行分子动力学模拟揭示了这些序列作为免疫原性表位的潜力。计算机研究还表明，与参考肽相比，表位占据了几乎相同的结合裂隙。分子动力学模拟数据还表明，这些肽在口袋中保持完整的时间更长。我们的研究旨在使用免疫信息学选择 MHC Ⅰ类限制的表位，以激活 CD8 T 细胞，从而预测针对 寄生虫的可能疫苗候选物。由 Ramaswamy H. Sarma 交流。
