Identification of HLA-I restricted epitopes in six vaccine candidates of Leishmania tropica using immunoinformatics and molecular dynamics simulation approaches.

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.