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.