Infectious Diseases Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Cellular and Molecular Research Center, Qazvin University of Medical Sciences, Qazvin, Iran.
Infect Genet Evol. 2019 Nov;75:103953. doi: 10.1016/j.meegid.2019.103953. Epub 2019 Jul 5.
In spite of numerous studies on vaccination for various species of Leishmania, research on the development of an effective vaccine for L. tropica is very scarce. In silico epitope prediction is a new way to survey the best vaccine candidates. Here, we predicted the best epitopes of six L. tropica antigens with vaccine capability against this pathogen, using highly frequent HLA-I alleles. Based on the frequent HLA alleles, the protein sequences were screened individually using four different MHC prediction applications, namely SYFPEITHI, ProPredI, BIMAS, and IEDB. Several in silico assays including clustering, human similarity exclusion, epitope conservancy prediction, investigating in experimental records, immunogenicity prediction, and prediction of population coverage were performed to narrow the results and to find the best epitopes. The selected epitopes and their restricted HLA-I alleles were docked and the final epitopes with the lowest binding energy (the highest binding affinity) were chosen. Finally, the stability and the binding properties of the best epitope-HLA-I combinations were analyzed using molecular dynamics simulation studies. We found ten potential peptides with strong binding affinity to highly frequent HLA-I alleles that can be further evaluated as vaccine targets against L. tropica.
尽管针对各种利什曼原虫物种的疫苗已经进行了大量研究，但针对利什曼原虫的有效疫苗的研究却非常匮乏。基于计算机的表位预测是一种调查最佳疫苗候选物的新方法。在这里，我们使用高频率 HLA-I 等位基因预测了具有针对这种病原体的疫苗能力的六种利什曼原虫抗原的最佳表位。根据常见的 HLA 等位基因，使用四种不同的 MHC 预测应用程序（即 SYFPEITHI、ProPredI、BIMAS 和 IEDB）分别筛选蛋白质序列。进行了一些基于计算机的分析，包括聚类、人类相似性排除、表位保守性预测、实验记录调查、免疫原性预测和人群覆盖率预测，以缩小结果范围并找到最佳表位。选择的表位及其限制的 HLA-I 等位基因进行对接，并选择结合能（最高结合亲和力）最低的最终表位。最后，使用分子动力学模拟研究分析了最佳表位-HLA-I 组合的稳定性和结合特性。我们发现了十个与高频率 HLA-I 等位基因具有强结合亲和力的潜在肽，可进一步评估为针对利什曼原虫的疫苗靶标。
