Coronaviruses are highly transmissible and pathogenic viruses for humans and animals. The vast quantity of information collected about SARS-CoV-2 during the pandemic helped to unveil details of the mechanisms behind the infection, which are still largely elusive. Recent research demonstrated that different class I/II human leukocyte antigen (HLA) alleles might define an individual susceptibility to SARS-CoV-2 spreading, contributing to the differences in the distribution of the infection through different populations; additional studies suggested that the homolog of the HLA in cats, the feline leukocyte antigen (FLA), plays a pivotal role in the transmission of viruses. With these premises, this study aimed to exploit a bioinformatic approach for the prediction of the transmissibility potential of two distinct feline coronaviruses (FCoVs) in domestic cats (feline enteric coronavirus (FeCV) and feline infectious peritonitis virus (FIPV)) using SARS-CoV-2 as the reference model. We performed an epitope mapping of nonapeptides deriving from SARS-CoV-2, FeCV, and FIPV glycoproteins and predicted their affinities for different alleles included in the three main loci in class I FLAs (E, H, and K). The predicted complexes with the most promising affinities were then subjected to molecular docking and molecular dynamics simulations to provide insights into the stability and binding energies in the cleft. Results showed the FLA proteins encoded by alleles in the FLA-I H (H*00501 and H*00401) and E (E*01001 and E*00701) loci are largely responsive to several epitopes deriving from replicase and spike proteins of the analyzed coronaviruses. The analysis of the most affine epitope sequences resulting from the prediction can stimulate the development of anti-FCoV immunomodulatory strategies based on peptide drugs.
Keywords: MHC; coronavirus; immunity system; peptides.