Identification of HLA-A*0201-restricted CTL epitopes from the receptor-binding domain of MERS-CoV spike protein using a combinatorial in silico approach

Abstract

A novel SARS-like illness called Middle East respiratory syndrome coronavirus (MERS-CoV), caused by an emerging coronavirus, has been a recent cause for concern due to its fatality and pandemic potential. Developing a peptide-based vaccine could be helpful in fighting against the virus. Since the experimental procedure is time-consuming and expensive, computational analysis can play an important role in accelerating the process. Therefore, the aim of this study was to computationally identify cytotoxic T-lymphocyte epitopes presented by the human leukocyte antigen (HLA)-A*0201, as this is the most frequent HLA class I allele among Middle Eastern populations. The receptor-binding domain of the spike glycoprotein of MERS-CoV, by which the virus binds to its entry receptor to further infect host cells, is a potential candidate used here for running our in silico epitope identification process. The results include predicted epitopes together with their interaction properties with major histocompatibility complex (MHC) molecules and also the binding behavior of MHC-epitope complexes to human T-cell receptor. Predicted epitopes with the most preferable binding properties are beneficial for vaccine development. Therefore, the huge experimental workload for epitope-based vaccine design will be minimized.