Immunogenic SARS-CoV-2 Epitopes: In Silico Study Towards Better Understanding of COVID-19 Disease—Paving the Way for Vaccine Development

Abstract

The emergence of the COVID-19 outbreak at the end of 2019, caused by the novel coronavirus SARS-CoV-2, has, to date, led to over 13.6 million infections and nearly 600,000 deaths. Consequently, there is an urgent need to better understand the molecular factors triggering immune defense against the virus and to develop countermeasures to hinder its spread. Using in silico analyses, we showed that human major histocompatibility complex (MHC) class I cell-surface molecules vary in their capacity for binding different SARS-CoV-2-derived epitopes, i.e., short sequences of 8-11 amino acids, and pinpointed five specific SARS-CoV-2 epitopes that are likely to be presented to cytotoxic T-cells and hence activate immune responses. The identified epitopes, each one of nine amino acids, have high sequence similarity to the equivalent epitopes of SARS-CoV virus, which are known to elicit an effective T cell response in vitro. Moreover, we give a structural explanation for the binding of SARS-CoV-2-epitopes to MHC molecules. Our data can help us to better understand the differences in outcomes of COVID-19 patients and may aid the development of vaccines against SARS-CoV-2 and possible future outbreaks of novel coronaviruses.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

“…Using the same search terms in Google Scholar on 8 September 2020, we gathered an additional 34 publications, giving a total of 65 SARS-CoV-2 in silico epitope prediction studies ( Table 1 ). These studies can be broadly grouped into two classes based on their rationale for epitope prediction: those that predict SARS-CoV-2 epitopes using SARS-CoV immunological data by exploiting the genetic similarity between the two viruses (Ahmed et al [ 20 ], Lee et al [ 21 ], Grifoni et al [ 22 ], and Ranga et al [ 23 ]), and those that apply peptide-HLA binding prediction methods (the remaining 61 studies). We review and discuss each of these approaches in the following.…”

“… Study label a HLA-I epitope prediction b HLA-II epitope prediction c Immunogenicity d IFN-γ production e Conservation Allergenicity f Toxicity g Autoimmunity Vaccine construct 1 Ahmed2020 [ 20 ] Using SARS-CoV immunological data Using SARS-CoV immunological data - - Y - - - - 2 Grifoni2020 h [ 22 ] Using SARS-CoV immunological data, NetMHCpan-4.0 Using SARS-CoV immunological data, Tepitool - - - - - - - 3 Ranga2020 [ 23 ] Using SARS-CoV immunological data, NetCTL-1.2 - - - - - - - - 4 Lee2020 h [ 21 ] Using SARS-CoV immunological data, NetMHCpan-4.0 Using SARS-CoV immunological data iPred - - - - - - 5 Baruah2020 [ 112 ] NetCTL-1.2, NetChop, CTLPred - - IFNepitope - - - - - 6 Crooke2020 [ 96 ] NetCTL-1.2, NetMHCpan-4.0 NetMHCIIpan-3.2 …”

“…Studies that used SARS-CoV immunological data for prediction [ [1] , [2] , [3] , [4] ] are shown in bold font, while all other studies that used peptide-HLA binding prediction methods are in regular font. In silico studies [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] ] were obtained from PubMed, while the remaining studies [ [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , ...…”

“…In fact, the genetic similarity of SARS-CoV-2 and SARS-CoV was found to be quite high in the structural proteins (~76% in S and >90% in N, M, and E proteins) [ 20 ], which were known to induce robust and long-lasting T cell immunity against SARS-CoV [ 26 ]. Motivated by the high genetic similarity of SARS-CoV with SARS-CoV-2, multiple in silico studies [ [20] , [21] , [22] , [23] ] used the information of T cell epitopes available from previous SARS-CoV immunological studies to predict likely targets of SARS-CoV-2 T cell responses ( Table 1 ). This approach is well motivated, with cross-reactive T cell epitopes being reported previously for genetically similar viruses [ 27 ], including zika virus, dengue virus, and other flaviviruses [ [28] , [29] , [30] , [31] ].…”

“…For identifying the SARS-CoV T cell epitopes likely to generate cross-reactive immune responses against SARS-CoV-2, Ahmed et al [ 20 ] scanned all available SARS-CoV T cell epitopes in the ViPR database [ 32 ] that had been determined previously by using experimental positive HLA binding or T cell assays, and identified epitopes that had an exact match in the SARS-CoV-2 sequences available at that time. Subsequent studies by Lee et al [ 21 ], Grifoni et al [ 22 ], and Ranga et al [ 23 ] used the SARS-CoV epitopes as well as peptide-HLA binding prediction methods (discussed in the next subsection) and proposed the common ones as potential SARS-CoV-2 epitopes.…”