Discussion
COVID-19 is a novel pandemic that has had significant global health consequences. The clinical course of COVID-19 is similar to systemic autoimmune diseases in some patients. Biological and chemical anti-inflammatory medications are used in the treatment of autoimmunity in severe and critical patients with COVID-19 [134,135]. In some cases, the immune response to COVID-19 resembles the immune response that develops in patients with systemic autoimmunity [136]. This situation can cause multi-organ to represent presentation in both COVID-19 and autoimmune patients [137]. The SARS-CoV-2 virus can disturb the self-tolerance of host antigens at least in part through molecular mimicry. Indeed, the development of autoantibodies and sometimes organ-specific (e.g., GBS) or systemic (e.g., SLE-like disease) autoimmunity has been observed in COVID-19. We used immunoinformatics and proteomics tools to explain the autoimmune conditions that may develop during and after COVID-19 infection and the previously unknown pathophysiology of neurological events in COVID-19. In vitro and in vivo studies have been carried out in previous studies in the literature to determine the molecular mimicry between organisms that can cause autoimmune diseases and the human proteome. With the development of machine learning and artificial intelligence-based bioinformatics tools, molecular mimicries between organisms and the human proteome can be detected using the methodology we use. As a limitation of our study, like every algorithm, bioinformatics tools have a margin of error. Therefore, the DEDDSEPV amino acid sequence, which we determined as a potential molecular mimicry with 100% amino acid sequence similarity in our study, may need to be confirmed in vitro or in vivo. In the study of Churilov et al., using in silico techniques, spike protein and autoantigens of type-1 diabetes-related and Addison autoimmunities were defined as molecular mimicry [138]. Nunez-Castilla et al. described potential molecular mimicry using structural bioinformatics techniques in their study [139]. In their study, Nunez-Castilla et al. contributed to the elucidation of the pathophysiology of autoimmune thyroiditis due to SARS-CoV-2 infection by identifying the presence of molecular mimicry between the TQLPP amino acid sequence in the Spike protein and human thyroid peroxidase [139]. In our study, we defined the molecular mimicry between SARS-CoV-2 and the human proteome using immunoinformatic techniques. Obviously, by using both techniques together, more accurate results can be obtained and it is easy to understand the autoimmunities between which organisms and the human proteome.