Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral infectious agent that broke out in Wuhan, China, and rapidly spread from China to the world [1,2]. SARS-CoV-2 causes Coronavirus disease 2019 (COVID-19) and postinfectious syndromes [3]. On 11 March 2020, the World Health Organization (WHO) declared the COVID-19 pandemic [4]. Flu-like symptoms, such as fever, fatigue, dry cough, headache, as well as myalgia, sore throat, nausea, and diarrhea, were reported and considered as clinical presentations of COVID-19 [5]. The SARS-CoV-2 virus belongs to the beta-coronaviridae genus [6]. It is a single-stranded RNA virus and has a spherical morphology. Also, it has four structural and sixteen non-structural proteins [7]. SARS-CoV-2 infects cells via splitting the spikes protein into S1 and S2 by TMPRSS8. S1 binds to the ACE-2 receptor, and S2 ensures the fusion of the virus into the cell [8]. When the virus enters the cell, it activates pattern recognition receptors (PRRs), which results in the release of proinflammatory cytokines from the infected cell, leading to macrophage activation [9]. Activated macrophages secrete cytokines and chemokines, leading to vasodilation and increased capillary permeability [10,11]. The alveoli are compressed by the passage of plasma into the interstitial space. This process also disrupts surfactant synthesis in alveolar type 2 cells in the lung. As a result of these pathological processes, alveolar collapse develops in the lung, and gas exchange is impaired [12]. IL-1, IL-6, and TNF, which are pro-inflammatory cytokines released into the blood, reach the hypothalamus via the blood and cause fever [13]. In several cases of COVID-19, the shortness of breath caused hypoxemia [14,15]. Critical patients develop septic shock and hypoxemia-related multi-organ dysfunction [16]. Hypoxia, which occurs with the development of pneumonia, causes tachycardia by activating the sympathetic system. The abnormal immune response can cause septic shock and death [17]. In short, due to pneumonia, vasodilation that decreases effective blood volume (BV) and peripheral resistance (PR) can lead to hypotension, reduced perfusion rate of the heart, and multi-organ failure [18]. In COVID cases, severe and critical patients show immune dysregulation [19], elevated inflammatory markers [20], low number regulatory immune cells [21], high-level inflammatory Th-17 cells [21], and neutrophils [22]. Medications used in autoimmune diseases, such as tocilizumab [23] and tofacitinib, are also used in COVID-19 patients [24]. Viruses can cause Type II hypersensitivity reactions, including tissue damage caused by autoantibodies secondary to viral infections and inflammation. The aim of our study is to identify molecular mimicry between SARS-CoV-2 and the human proteome. We will also determine the immunological properties of sequences with molecular mimicry using immunoinformatics techniques.
Material-Method
Data Collection
Structural proteins of SARS-CoV-2 (Spike, Nucleocapsid, Membrane, Envelope) were obtained from the NCBI database. The structural proteins aminoacid sequences of SARS-CoV-2 were fragmented into 8 amino acid-long peptide fragments.
Peptide Matching
The peptide-match server is an online tool to reveal the amino acid sequence similarity between amino acid sequences and desired organism [25]. Fragmented eight amino acid length peptides uploaded peptide-match server. This server predicts similarity between 8mers and human sequences [25].
Antigenicity prediction
In order to generate immune responses based on molecular mimicry, the 8mers that we constructed from SARS-CoV-2 structural proteins must be antigenic [26]. Predicted similar epitopes assessed for antigenicity in Vaxijen v2.0 antigenicity prediction server [26].
Prediction of binding affinity of peptides to TAP
The compatibility of 8mers with the TAP protein must be met for them to be presented as epitopes. Similar epitopes were analyzed for TAP affinity. For the analysis, we use the TAPreg tool [27].
Allergenicity prediction
To elicit an autoimmune response through molecular mimicry, 8mers must not possess allergenicity as this would result in the activation of an IgE-mediated response.The predicted similar epitopes were analyzed for allergenicity. For analyses of allergenicity, we use the AllerTOP v2.0 tool [28].
Toxicity prediction
Similar epitopes were analyzed for toxicity, the toxicity prediction we use the ToxinPred tool [29].
IL-10, and IFN-gamma inducing prediction
Similar epitopes were analyzed for IL-10, and IFN-gamma induction. The prediction of IL-10 [30] inducing predicted in IL-10pred, and prediction of IFN-gamma inducing predicted in IFNepitope [31] server.
HLA docking calculation
Matched best peptide (identical, antigenic, non-allergenic, non-toxic, IL-4 inducer, and IFN-gamma inducer, non-IL-10 inducer, and high affinity to TAP) docked to Class-I and Class-II MHC molecules. Class-I HLA docking simulated in CABS-dock server [32]. Structures of HLA-I molecules were retrieved from the RCSB-PDB server (4NQV, 4UQ3, 3RL2, 1X7Q, 5HGA, 5EO1, 3SPV, 1OGT, 3LKN, 1E27, 5INC). The docked models were analyzed for ΔG (kcal mol-1) and Kd (M) at 37.0 °C values. For this analysis, the PRODIGY server was used [33,34]. The Epidock server was used for DEDDSEPV peptide docking to the MHC-II receptor.
Results
Peptide Matching Results
Fragmented peptides imported to the peptide match server. We found six identical amino acid sequences (VNSVLLFL, VFLLVTLA, KKDKKKKA, SRSSSRSR, RRARSVAS, DEDDSEPV) to the human proteome. Table-1 shows the UniProt codes and protein names of similar proteins with identical amino acid sequences.
Antigenicity prediction Results
Identical sequences must be antigenic because non-antigenic sequences cannot induce cross reactivity. Table-2 shows antigenicity and calculated antigenicity scores of identical peptides.
Prediction of binding affinity of peptides to TAP
Peptides must be transported to the endoplasmic reticulum before they can be presented to CD4+ cells by MHC-I. TAP protein carries out the transport process, so the TAP affinities of the eight amino acid length peptides were evaluated and shown in Table-3.
Allergenicity prediction results
The identical peptides allergenicity predicted in AllerTOP v2.0 bioinformatic tool and shown in Table-3. Identical epitopes causing autoimmunity should not necessarily be allergic.
Toxicity prediction results
The identical peptides must be non-toxic to the human body. For prediction of toxicity, the ToxinPred tool used for toxicity prediction results is shown in Table-3.
IL-10, and IFN-gamma inducing peptides prediction results
IFN-gamma cytokine levels increased and IL-10 levels decreased in autoimmunity. Table-4 shows IFN-gamma and IL-10 induction prediction.
HLA docking results
Matched best peptide (identical, antigenic, non-allergenic, non-toxic, IL-4 inducer, and IFN-gamma inducer, non-IL-10 inducer, and high affinity to TAP) docked for affinity to Class-I and Class-II MHC molecules. The MHC-I docking results show DEDDSEPV sequence has a high affinity to HLA-A*0101, HLA-A*0201, HLA-A*2402, HLA-B*0702, HLA-B*2705, HLA-B*5101, and HLA-B*5801. Table-5 shows Gibbs free energy calculation and K
d values of docking results. Figure-1 shows docked models of DEDDSEPV and HLA subtypes.
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 [35,36]. In some cases, the immune response to COVID-19 resembles the immune response that develops in patients with systemic autoimmunity [37]. This situation can cause multi-organ to represent presentation in both COVID-19 and autoimmune patients [38]. 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 [39]. Nunez-Castilla et al. described potential molecular mimicry using structural bioinformatics techniques in their study [40]. 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 [40]. 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.
Conclusion
SARS-CoV-2 eight amino acid length peptides matched the human proteome and found six identical peptides. The peptide sequence with the highest probability of cross-reactivity was selected and analyzed. Selected peptide docked MHC-I and MHC-II receptors, these docking results show DEDDSEPV sequence can cause cross-reactivity to human myosin-16. Myosin-16 is a motor protein and is highly expressed in the central nervous system. Myosin-16 protein is also highly expressed in endocrine tissues (especially the pituitary gland), liver, and gallbladder. In our study, we proved by using in-silico methods that the DEDDSEPV peptide sequence can cause cross-reactivity. Auto-antibodies that may develop against myosin-16 may explain the neurological symptoms and diseases that may develop due to COVID-19. This could explain the pathophysiologic symptoms and diseases that may develop due to COVID-19 of the endocrine system, which affects the entire physiology of the human body, such as the immune system. Liver function tests are high in critical and severe COVID-19 patients. Since myosin-16 is expressed at a high level in the liver tissue, it can explain the irregularity in liver function tests. Our study is the first in the literature to try to define the molecular mimicry between SARS-CoV-2 and the human proteome using immunoinformatic techniques. We think that in the future it will illuminate the similarity between other organisms and the human proteome using the methodology in our study. It may contribute to elucidating the pathophysiology of autoimmune diseases by elucidating the similarity between other organisms and the human proteome. When molecular mimicries are illuminated and which organism triggers which autoimmunity, removal of the triggering organism or reduction of its antigenic load can open a new era in the treatment of autoimmune diseases.