SARS-CoV-2-specific CD4 and CD8 T cells have been shown to be present in individuals with acute, mild, and asymptomatic Coronavirus disease (COVID-19). Toward the development of diagnostic and therapeutic tools to fight COVID-19, it is important to predict and characterize T cell epitopes expressed by SARS-CoV-2. Here, we use RosettaMHC, a comparative modeling approach which leverages existing structures of peptide/MHC complexes available in the Protein Data Bank, to derive accurate 3D models for putative SARS-CoV-2 CD8 epitopes. We outline an application of our method to model 8–10 residue epitopic peptides predicted to bind to the common allele HLA-A * 02:01, and we make our models publicly available through an online database ( https://rosettamhc.chemistry.ucsc.edu ). We further compare electrostatic surfaces with models of homologous peptide/HLA-A * 02:01 complexes from human common cold coronavirus strains to identify epitopes which may be recognized by a shared pool of cross-reactive TCRs. As more detailed studies on antigen-specific T cell recognition become available, RosettaMHC models can be used to understand the link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the context of SARS-CoV-2 infection.
【저자키워드】 SARS-CoV-2, Rosetta, Epitope-based vaccine, T cell epitopes, MHC-I, epitope cross-reactivity, 【초록키워드】 COVID-19, Structure, SARS-COV-2 infection, diagnostic, peptide, CD4, CD8, database, Epitopes, T cell, Asymptomatic, therapeutic, Mild, homologous, epitope, disease, predict, T cell epitope, strain, CD8 T cell, cross-reactive, Protein Data Bank, HLA-A, individual, complex, residue, common cold coronavirus, TCRs, approach, shown, predicted, identify, can be used, expressed, complexes, electrostatic, 3D model, common allele, 【제목키워드】 modeling,