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Article

Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in Clostridioides difficile and Prospects of Mimic-Based Vaccine Design: An In Silico Approach

by
Saleh Alshamrani
1,
Mutaib M. Mashraqi
1,*,
Ahmad Alzamami
2,
Norah A. Alturki
3,
Hassan H. Almasoudi
1,
Mohammed Abdulrahman Alshahrani
1 and
Zarrin Basharat
4,*
1
Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia
2
Clinical Laboratory Science Department, College of Applied Medical Science, Shaqra University, AlQuwayiyah 11961, Saudi Arabia
3
Clinical Laboratory Science Department, College of Applied Medical Science, King Saud University, Riyadh 11433, Saudi Arabia
4
Alpha Genomics (Private) Limited, Islamabad 45710, Pakistan
*
Authors to whom correspondence should be addressed.
Microorganisms 2023, 11(9), 2300; https://doi.org/10.3390/microorganisms11092300
Submission received: 1 July 2023 / Revised: 7 September 2023 / Accepted: 7 September 2023 / Published: 12 September 2023
(This article belongs to the Topic Microbiota-Immunity Axis in the Different Pathological Disorders)
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Abstract

:
Molecular mimicry, a phenomenon in which microbial or environmental antigens resemble host antigens, has been proposed as a potential trigger for autoimmune responses. In this study, we employed a bioinformatics approach to investigate the role of molecular mimicry in Clostridioides difficile-caused infections and the induction of autoimmune disorders due to this phenomenon. Comparing proteomes of host and pathogen, we identified 23 proteins that exhibited significant sequence homology and were linked to autoimmune disorders. The disorders included rheumatoid arthritis, psoriasis, Alzheimer’s disease, etc., while infections included viral and bacterial infections like HIV, HCV, and tuberculosis. The structure of the homologous proteins was superposed, and RMSD was calculated to find the maximum deviation, while accounting for rigid and flexible regions. Two sequence mimics (antigenic, non-allergenic, and immunogenic) of ≥10 amino acids from these proteins were used to design a vaccine construct to explore the possibility of eliciting an immune response. Docking analysis of the top vaccine construct C2 showed favorable interactions with HLA and TLR-4 receptor, indicating potential efficacy. The B-cell and T-helper cell activity was also simulated, showing promising results for effective immunization against C. difficile infections. This study highlights the potential of C. difficile to trigger autoimmunity through molecular mimicry and vaccine design based on sequence mimics that trigger a defensive response.

1. Introduction

Molecular mimicry refers to the phenomenon in which microbial or environmental antigens share structural or sequence similarities with host antigens [1]. This similarity can lead to a cross-reactive immune response, in which the immune system mistakenly targets self-tissues, resulting in autoimmune disorders [2]. The onset of autoimmune disorders due to molecular mimicry by pathogenic proteins or antigens presents an intriguing research area that investigates the possible connection between microbial infections and the onset of autoimmune responses [3,4]. Understanding the mechanisms by which molecular mimicry contributes to autoimmune disorders is crucial for developing targeted therapies and preventive strategies [5,6,7]. Numerous studies have investigated the molecular mimicry between human proteins associated with autoimmune disorders and pathogen-derived proteins [8,9,10,11,12,13]. These investigations have revealed several mechanisms that contribute to the initiation and perpetuation of autoimmune responses [14,15,16]. Specific pathogen proteins have been found to share sequence motifs with host proteins involved in autoimmune disorders, enabling the activation of autoreactive T cells [14,17]. Streptococcus pyogenes has been implicated in autoimmune diseases, such as rheumatic fever [18], glomerulonephritis [19], and multiple sclerosis [20]. Epstein–Barr virus has been connected with molecular-mimicry-mediated autoimmune disorders [21], like systemic lupus erythematosus [22,23], hepatitis [24], and multiple sclerosis [25]. These pathogens possess proteins that mimic self-antigens, leading to cross-reactivity with host tissues. Additionally, molecular mimicry has been observed in viral infections, such as the hepatitis C virus, in which viral proteins share sequence homology with host proteins involved in autoimmune liver diseases [26].
Techniques that can be employed to study molecular-mimicking peptides include phage display [27], and bioinformatics approaches include sequence alignment and molecular modeling [28], etc. Sequence alignment algorithms can identify regions of similarity or shared motifs between the two protein sequences [29]. Homology modeling and comparative protein structure prediction can be used to analyze the three-dimensional structures [30] of pathogen and host proteins. By comparing the structural features and folding patterns, potential mimicking regions can be identified. Predictive algorithms, such as NetMHC [31] and the Immune Epitope Database (IEDB) [32], can be utilized to identify potential epitopes within pathogen proteins that resemble host epitopes associated with autoimmune disorders [33]. To validate the cross-reactivity of pathogen and host proteins, enzyme-linked immunosorbent assay (ELISA) assays [34], Western blotting [35], and flow cytometry [36], etc., can be employed. These methods measure the binding of antibodies or T cells to specific antigens and can confirm the presence of molecular mimicry. Apart from these, animal models, such as transgenic mice expressing human proteins associated with autoimmune disorders, can be used to study the effects of pathogen infections and evaluate the development of autoimmune responses [37,38]. Disease models, such as in vitro models of tissue inflammation can also be employed to investigate the consequences of molecular mimicry [8,39]. By utilizing a combination of these techniques, researchers can gain insights into the occurrence and mechanisms of molecular mimicry, contributing to a better understanding of its role in autoimmune disorders and potentially guiding the development of therapeutic interventions.
Bioinformatics is a swift approach to identifying and characterizing the molecular-mimicry interactions between human proteins and pathogens [40,41,42]. Various bioinformatics tools and databases are utilized to analyze protein sequences, identify shared motifs or structural similarities, and predict antigenicity and immunogenicity [43]. Comparative genomics and proteomics approaches are employed to identify pathogen proteins that mimic host antigens associated with autoimmune disorders [44,45,46]. Previous studies have utilized bioinformatics approaches to uncover potential molecular-mimicry mechanisms between pathogens and host proteins by employing sequence alignment algorithms, structural modeling, and epitope prediction tools to assess the extent of mimicry and the potential immunological consequences [15,41,47]. Additionally, database-mining techniques have been used to establish links between identified mimicry interactions and autoimmune disorders [9,48,49,50]. Herein, we analyzed mimicry prediction and association with autoimmune disorders in Clostridioides difficile using in silico methods. By integrating bioinformatics analyses with experimental validation, this information contributes to our understanding of the complex interactions between pathogens and the human immune system, shedding light on the role of molecular mimicry in the development and progression of autoimmune disorders.

2. Material and Methods

2.1. Homology Analysis

The entire set of proteins from the human and C. difficile S-0253 (reference strain ASM1888508v1) samples was obtained from Uniprot (https://www.uniprot.org/proteomes/UP000005640 (accessed on 31 May 2023)) and the NCBI database (GenBank accession: CP076401.1; accessed 31 May 2023), respectively. To identify potential homologous proteins, a local installation of BLAST was utilized, applying a threshold of ≥50% identity and ≥100-bit score to retain proteins for further analysis [16].

2.2. Mimic Region Identification

To identify potential regions involved in mimicry, the proteins were aligned to uncover regions of similarity with a minimum length of 10 amino acids [16]. RMSD was employed as a scoring metric to assess the structural similarity of the peptides. To obtain the 3D structures of these proteins, the state-of-the-art-predicted protein structures from the AlphaFold database were utilized [51,52]. To compare and align the obtained structures, both iPBA [53] and TM-align [54] algorithms were employed. These tools are widely recognized tools in the field of structural biology, known for their accuracy in comparing protein structures and determining alignment based on various structural features. iPBA is a sequence-independent method that uses a fragment-based approach (for capturing large protein fold changes) [53], while TM-align superimposes 3D coordinates and aligns protein structures by dynamic programming method (for capturing small fold changes) [54]. The superposed structures diagram was generated through the TM-align module of the RCSB PDB structural alignment tool (https://www.rcsb.org/alignment (accessed on 21 June 2023)).

2.3. Autoimmunity Elucidation

In order to identify homologous protein pathways associated with autoimmune disorders or infection, relevant databases, such as pathDIP [55] and PHAROS (https://pharos.nih.gov/targets/ (accessed on 15 June 2023)) [56], were surveyed, along with a thorough review of the literature. PHAROS provides preprocessed data from the Target Central Resource Database (TCRD) on the input of the human gene name, Uniprot ID, etc. Linked disorders can be manually checked for autoimmunity. For pathDIP, all databases were selected with a minimum confidence level set to 0.99. The data type selected was extended pathway associations. The protein interaction set considered for analysis included both experimentally detected and computationally predicted protein–protein interactions (PPIs) using the full IID dataset. pathDIP serves as a comprehensive reference for signaling cascades across various species, consolidating key pathways sourced from major curated pathway databases [57]. The associations in pathDIP are based on a combination of computational predictions, experimentally confirmed interactions, orthology mapping, and inference of physical protein interactions. This database provides a valuable resource for exploring and understanding signaling pathways associated with autoimmune disorders and infection. Apart from this, a literature search was conducted to identify infection or autoimmune pathways linked with these homologs. A BLAST search (≥90% homology) of epitopes was also carried out against IEDB [32], and the relevant literature was identified for the listed infection or autoimmune disorder in the database.

2.4. Mimic-Based Vaccine Design

Identified mimics were subjected to antigenic analysis using VaxiJen server [58]. Apart from this, properties like allergenicity, toxicity, and other parameters useful in finalizing peptides for vaccine design were studied. ProtParam was used for physicochemical profiling [59], while AllerCatPro (https://allercatpro.bii.a-star.edu.sg/, accessed on 16 June 2023) and ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred/multi_submit.php, accessed on 16 June 2023) were used for allergenicity and toxicity analysis, respectively. IEDB was used for immunogenicity prediction [32]. Mimics were prioritized for vaccine design based on these parameters, the ability to induce immune response, and conservation across the strains. Conservations was determined by the ConSurf webserver (https://consurf.tau.ac.il/consurf_index.php; accessed on 17 June 2023).
The vaccine construct was designed according to the previously described methodology [16], using suitable linkers, adjuvants, and binders. They were subjected to another round of evaluation according to properties like antigenicity, toxicity, allergenicity, etc. The best antigenic and non-allergenic, non-toxic construct was tested for immune reaction incitation and cloned in a pET-28(a)+ vector (available at https://www.snapgene.com/plasmids/pet_and_duet_vectors_(novagen)/pET-28a(%2B); accessed on 18 June 2023) after reverse translation and codon optimization through the JCat tool [60]. C-ImmSimm [61] was used for the simulation of immune reaction. The parameters were as follows: Simulation_volume = 10; Num_steps = 1000; HLA = A0101, A0102, B0702, B0704, DRB1_0101, DRB1_0102; No_of_injections = 3; Time of injection (in days) = 1, 30); Adjuvant = 100. The first two injections on day 1 and 30 were of the vaccine. At day 240, the proteins phosphoribosylaminoimidazolecarboxamide formyltransferase and adenylosuccinate lyase were injected to test the reaction.

2.5. Immune Receptor Binding Study

To elicit protection, the vaccine protein should bind the immune receptors with good affinity [62,63]. To analyze this property, we constructed a 3D model of the vaccine construct with SWISS-MODEL [64], AlphaFold [51], and I-TASSER [65]. The best model was selected based on Ramachandran plot statistics from the assess module of SWISS-MODEL (https://swissmodel.expasy.org/assess (accessed on 20 June 2023)) and energy-minimized using Molecular Operating Environment (MOE) v2016 software. It was then docked with immune receptors of importance like TLR-4 receptor (PDB ID: 3FXI), HLA-A (PDB ID: 3OX8), and HLA-B (PDB ID: 4JQX), using the ClusPro server [66]. ClusPro focuses on predicting the overall shape and orientation of the protein–protein complex [67]. Prodigy [68] was used to predict the thermodynamic properties and binding affinities of the obtained docked complexes. An experimentally determined protein–protein interacting complex (PDB ID:4GIQ) was employed as a control to compare predicted values. This comparison allowed us to assess whether the binding scores were superior or inferior to the control, providing valuable insights into the efficacy and specificity of our vaccine design approach.

3. Results

3.1. Homologous Sequence Identification

In total, 23 proteins were obtained with significant similarity between human and C. difficile. F0F1 ATP synthase subunit beta had the highest number of peptide mimics (n = 11), followed by F0F1 ATP synthase subunit alpha (n = 7) and heat-shock protein DnaK (n = 6) (Table 1). These were superposed (Figure 1), and RMSD after structural superposition varied between iPBA and TM-align prediction. A possible reason for this is the algorithm difference, in which iPBA is tailored for flexible proteins or regions within a protein. Thus, it gave overall lower RMSD values compared to TM-align. However, both servers gave the lowest RMSD of 0.51 for the ATP-dependent Clp endopeptidase proteolytic subunit ClpP. This suggests a high degree of structural similarity between the human and bacterial homologs of this protein.

3.2. Autoimmunity Prediction

PHAROS and PATHDIP database scan revealed several autoimmune diseases linked with the human homologous sequences of C. difficile (Table 2). ATP-dependent Clp protease proteolytic subunit and elongation factor Tu had two copies, so homologs were removed from database mining. For the rest of the homologs, the most commonly identified infection was tuberculosis (for DnaK, V-type ATP synthase, and ClpP) and the most commonly identified autoimmune disease was rheumatoid arthritis (for chaperones DnaK and GroEL, elongation factor tu, Translation elongation factor 4, 3-oxoacid CoA-transferase subunit B, V-type proton ATPase, phosphopyruvate hydratase, Phosphoribosylaminoimidazolecarboxamide formyltransferase, and NifU).

3.3. Sequence Mimics and Vaccine Design

Out of 68 mimics ≥ 10 amino acids in length, 31 were antigenic (Table 3), with four being allergenic. Among the 31 antigenic sequences, 14 mimics were identified as IL-4 inducers (RTTPSVVAFT, DHGKSTLADRL, GGAGYIGSHT, DGTGVRDYIHV, LGIYPAVDPL, IKEGDIVKRTG, CIYVAIGQKRST, IETQAGDVSAYIPTNVISITDGQI, EGHPDKICDQISD, TKVDRSAAYAAR, GAGQQSRIHCTRLAG, GCGSAIASSS, RGVKGTTGTQASFL, YKRNPMRSER). Only three peptides (PQIEVTFDIDANGIV, CIYVAIGQKRST, GAGQQSRIHCTRLAG), belonging to DnaK, F0F1 ATP synthase subunit alpha, and Adenylosuccinate lyase, were predicted as non-inducers of IL-6, while all the rest were inducers. Mimics LLLDVTPLSLGIET, DGTGVRDYIHV, VGERTREGNDLY, EGHPDKICDQISD, and RGVKGTTGTQASFL, belonging to DnaK, GalE, F0F1 ATP synthase subunit beta, methionine adenosyltransferase, and adenylosuccinate lyase, respectively, were predicted as inducers of IL-10.
Two sequence mimics (GAGQQSRIHCTRLAG and RGVKGTTGTQASFL) belonging to the phosphoribosylaminoimidazolecarboxamide formyltransferase and adenylosuccinate lyase proteins, respectively, were selected for vaccine design based on antigenicity and other values. RGVKGTTGTQASFL was predicted as an inducer of IL-4, IL-6, and IL-10, while GAGQQSRIHCTRLAG was predicted as only an IL-4 inducer. Evolutionary analysis revealed majority of residues of both sequence mimics are highly conserved (Figure 2).
Mimic-based vaccine design is an innovative approach that utilizes synthetic peptides or proteins to mimic specific antigens of pathogens [16]. By presenting these mimics to the immune system, it can generate targeted immune responses against the actual pathogen. Here, in total, nine constructs were made, and the non-allergenic ones were retained for analysis (Supplementary Table S1). Among these, a stable, highly antigenic one, i.e., construct C2 was chosen for further downstream processing. It was reverse-translated and cloned in a pET-28(a)+ vector (Figure 3).

3.4. Immune Response Simulation

Immune response simulation analysis utilized Parker’s propensity scale to predict potential epitopes within the vaccine sequence [125], which may be recognized by the immune system, particularly by T cells. Six B-cell epitopes (EQIG, STRGRKCCRRKKEA, AGGGSRGVKGTTGT, AGGGSGAGQ, GGSHEY, AGGGS) were identified using the Parker propensity scale. For MHC-I, no binding epitope was present for the A0101 and B0702 allele, while two binding epitopes (IINTLQKYY and AGGGGSHEY) were identified for the A0102 allele and one (RVRGGRCAV) was identified for B0704. For MHC-II binding, seven epitopes were predicted for DRB1_0101 (YCRVRGGRC, FVAAWTLKA, WTLKAAAGG, LKAAAGGGS, VKGTTGTQA, FLGGGSAKF, LERAGAKFV) and two were predicted for DRB1_0102 (LKAAAGGGS and LERAGAKFV).
The IgM + IgG population was 140,000 cells/mm3, and only a slight difference was observed after the second injection (Figure 4A). This suggests that the primary immune response, characterized by the production of IgM antibodies followed by IgG antibodies [126,127], was already established after the first injection. The second injection did not result in a significant increase in the overall IgM + IgG population. IgG1 + IgG2 count was below 80,000 cells/mm3 but increased to more than 90,000 cells/mm3 after the second injection. This indicates that the secondary immune response, mediated by IgG antibodies [128], was robustly triggered by the second injection. The overall immune cell counts plateaued after around 200 days, indicating stabilization of the immune response. The B-cell population remained active after the vaccine injection, up to 100 cells/mm3 (Figure 4B). Their sustained presence suggests ongoing immune surveillance and the potential for long-term immune memory [129]. T-helper (TH) cells increased after each vaccine injection and remained active with a count of 4000 cells/mm3 even after 300 days (Figure 4C). TH cells play a crucial role in coordinating the immune response by facilitating communication between various immune cells, and their persistent presence indicates their continued involvement in supporting and regulating the immune response [130,131]. The count of cytotoxic T (TC) non-memory cells fluctuated, possibly indicating their active participation in eliminating target cells (Figure 4D). In contrast, TC memory cells remained consistently higher than 100 cells/mm3, suggesting the establishment of immunological memory. Memory cells enable a rapid and specific response upon re-exposure to the antigen, contributing to long-term immunity [132]. Natural killer (NK) cells have a role in innate immune defense [133], and their population remained at more than 300 cells/mm3 for the whole period (Figure 4E). No significant changes were observed in response to the stress of bacterial proteins. This suggests that the immune system reached a state of equilibrium and was no longer strongly influenced by the presence of bacterial proteins.

3.5. Vaccine Interaction

The 3D structure of the vaccine construct was modeled using three tools, in which SWISS-MODEL achieved the highest percentage (89.60%), indicating a larger portion of residues in favorable conformation compared to I-TASSER (44.32%) and AlphaFold (62.75%) (Supplementary Table S2). The QMEANDisCo Global score was additionally used to assess the global quality of the protein structure, with a lower score suggesting better overall quality. This metric is used to estimate the quality of a protein tertiary structure by taking distance constraints into account [134]. Again, SWISS-MODEL achieved a score of 0.64 ± 0.07, followed by AlphaFold (0.35 ± 0.07) and I-TASSER (0.32 ± 0.07).
The best-modeled structure by SWISS-MODEL (Supplementary Figure S1A) was used to map interactions with HLA and TLR-4 receptor (Figure 5). Docking revealed that HLA-A and HLA-B complexes had relatively lower binding scores compared to the TLR-4 complex, implying stronger binding affinities between the vaccine construct and HLA receptors (Supplementary Table S3). The PRODIGY server [68] was used to map thermodynamic changes in these complexes, where ΔG (kcal mol−1) represents the change in free energy associated with the formation of the protein–protein complex, while Kd (M) provided the equilibrium dissociation constant at 25 °C. ΔG is studied to measure the stability of the complex, while Kd is studied to measure the binding affinity, with more negative values suggesting a stronger interaction [135,136]. ΔG and Kd are better predictors of binding than docking score [137] and were, therefore, employed for validation. HLA-B and TLR-4 had a highly negative ΔG value, indicating stable and stronger interaction compared to the control. TLR-4 indicated the lowest Kd value, suggesting a strong binding affinity and a favorable binding interaction in comparison with the control. This suggests that the interactions between the vaccine construct and TLR-4 receptor are likely to be more favorable and specific. This also shows that the ClusPro modeling method performed well in predicting the binding of the HLA and TLR-4 complex with vaccine construct and is a reliable approach to determine interactions.

4. Discussion

C. difficile can cause infections, primarily in the colon or large intestine [138,139]. Infection usually occurs in the immunocompromised [140] and in people who have received antibiotic therapy, when the natural balance of bacteria in the colon is disrupted [138]. This allows the bacterium to multiply and produce toxins that cause inflammation and damage to the intestinal lining. Molecular mimicry allows C. difficile toxin A to bind glycosphingolipids [141]. Mindur et al. have reported cross-reactive epitopes of myelin basic protein in the surface layer protein of a sub-species of C. difficile [142]. Peptide EQSLITVEGDKASM from the toxin B protein of the species has also been implicated in an autoimmune disease, namely primary biliary cirrhosis. Alam et al. have reported a collagen triple-helix repeat family protein in C. difficile as a mimic of the type II collagen protein of humans [143]. The protein is implicated in reactive arthritis, septic arthritis, and rheumatic symptoms. However, the sequence identity was less than 45%. This is why this protein was missed by our analysis, as we followed stringent criteria of identity value ≥50%.
The molecular mimics at the whole proteome scale for C. difficile and their involvement in autoimmune disorders have not yet been mapped. A bioinformatics-based approach is a useful method to exploit the publicly available data for this purpose. Mapping the molecular-mimicry mechanism employed by C. difficile can provide insights into the virulence and pathogenesis, as well as offer potential targets for the development of therapeutic interventions, such as vaccines or drugs that can disrupt the interaction between the bacterial mimics and the host receptors, thereby preventing or reducing the severity of C. difficile infections. For this purpose, we obtained the proteome of the reference strain of C. difficile S-0253 (n = 3732 proteins). Among these, only 23 proteins were homologous to humans, having ≥50% sequence identity. The structural superposition of these proteins revealed several regions with organizational and fold similarity (Table 1). The ATP-dependent Clp endopeptidase proteolytic subunit ClpP exhibited the lowest RMSD value, indicating a high degree of structural similarity between the human and bacterial homologs. It plays a crucial role in maintaining protein homeostasis in conjunction with chaperones by degrading misfolded or damaged proteins. The peptide sequence QIERDTERDRFLSAPEAV of E. coli ClpP has been previously implicated in autoimmune biliary liver cirrhosis [144]. The highest number of peptide mimics were observed in energy-generating F0F1 ATP synthase and heat-shock protein DnaK. Zhang et al. have reported increased activity of ATP synthase in the autoimmune neuromyelitis optica spectrum disorder [145]. DnaK has previously been implicated in molecular mimicry of other pathogenic bacteria like Streptococcus pneumoniae [16] and Salmonella typhi [15]. DnaK and other molecular chaperones like GroEL have been implicated in multiple autoimmune disorders [116,146,147,148,149,150,151]. DnaK has been associated with autoimmune atrophic gastritis caused by H. pylori [152], while Qeshmi et al. have reported its presence in multiple sclerosis as well [153]. Overall, rheumatoid arthritis, Alzheimer’s disease, psoriasis, Huntington’s disease, and Parkinson’s disease emerged as the primary autoimmune disease associated with multiple homologs.
Among infectious disease mapping, tuberculosis was the most common infection linked to the homologous proteins of C. difficile, suggesting a potential role of these proteins in the immune response against mycobacterial infections. A varying fraction of C. difficile infection in tuberculosis patients has been reported previously, ranging from ~3 cases per 1000 adults in Korea [154] to ~70 cases per 1000 individuals in South Africa [155]. Obuch-Woszczatyński reported C. difficile-mediated diarrhea in tuberculosis patients when rifampicin was used as part of their treatment regimen [156]. Rifampicin can contribute to the development of resistance against this antibiotic in C. difficile, which in turn poses a risk to the effectiveness of tuberculosis treatment. The rate of C. difficile infection in tuberculosis patients tends to be higher in aged people compared to younger adults [157]. This bacterium has also been identified as one of the predominant pathogens causing diarrheal illness in HIV-seropositive individuals, with two times higher prevalence compared to HIV-seronegative people [158].
Mimic-based vaccine design is an innovative approach that utilizes synthetic peptides or proteins to mimic specific antigens of pathogens [16]. By presenting these mimics to the immune system, targeted immune responses against the actual pathogen can be generated [159,160]. Hence, a stable and highly antigenic vaccine construct was designed using two peptide mimics identified in this study. It was cloned into a pET-28(a)+ vector and immune response was assessed using in silico simulations. The primary immune response, characterized by IgM production followed by IgG production, was established after the first vaccine injection. The secondary immune response, mediated by IgG antibodies, was robustly triggered by the second injection. The immune cell counts plateaued after approximately 200 days, indicating stabilization of the immune response. B cells remained active, suggesting ongoing immune surveillance and potential long-term immune memory. TH cells increased after each vaccine injection and remained active even after 300 days, indicating their continued involvement in supporting and regulating the immune response. TC cells showed fluctuating counts, possibly indicating their active participation in eliminating target cells, while TC memory cells remained consistently higher, indicating the establishment of immunological memory. NK cells, involved in innate immune defense, maintained a stable population throughout the study. Hence, a dynamic and robust immune response occurred following the vaccine injections. The presence of specific antibody populations sustained B-cell activity, and a stable count of T cells, NK cells, and EP cells indicated an effective immune response against the target antigen. The establishment of immunological memory and the plateauing of immune cell counts suggests a stable and functional immune system capable of long-term protection. No significant changes were observed in response to bacterial protein stress, suggesting that the immune system reached an equilibrium state and was no longer strongly influenced by the presence of bacterial proteins. However, computational predictions are not without limitations, and although mimic-based vaccine design and immune response simulation helps accelerate the vaccine-development process, providing insights into immune responses and generating hypotheses for further experimental investigations, it also has limitations. The foremost limitation is their accuracy and adverse response mapping due to inadequate input of variables and complexities of the immune system [16]. The local tissue microenvironment and factors such as blood flow, physical barriers, and cellular interactions can influence immune responses but may be overlooked or simplified in simulations. Additionally, the pathogens tend to mutate, and they may not be workable in the real-world scenario due to the altered genetics of the microbe. To overcome this, we have tried to focus on conserved epitopes of the antigenic proteins, but the immune evasion mechanism may be altered with time and the epitope may fail to generate an immune response. Thus, the in silico vaccine design is a valuable tool for narrowing down potential candidates and reducing research costs and timelines, but it is just the initial step in the vaccine-development process. Real laboratory-based testing is essential to validate and refine these designs, ensuring that the vaccine candidates are safe and effective in a real-world setting. Moreover, the in silico outcomes may not necessarily mirror those in a real laboratory as they typically do not consider the environmental factors the laboratory experiments take into account and can be overly optimistic or pessimistic predictions. Biological systems can also have unexpected interactions and feedback loops that are difficult to predict computationally. These interactions may only become apparent through real-world experimentation. The limitations of in silico modeling highlight the need for a comprehensive and rigorous approach to vaccine development that combines computational methods with empirical testing.
In summary, using bacterial peptides as structural templates for vaccine design is a valid approach, but the risk of triggering an autoimmune reaction prevails [43]. It is crucial to assess the risk associated with the potential induction of autoimmune response after administering the vaccine. It is also important to acknowledge that factors such as prior exposure to antigens and the presence of known autoantibodies, genetic predisposition of individuals, and other variables may contribute to the initiation of cross-reactive responses [161]. However, the immune system is normally fortified with multiple layers of protective mechanisms, which work in concordance to prevent the occurrence of autoimmunity [162] in response to vaccines. The mammalian immune system has also evolved an intricate repertoire of mechanisms to discern self- from non-self-antigens, primarily through the establishment of central immune tolerance [163,164], thus acting as a safeguard against autoimmunity. Moreover, molecular mimics, such as those utilized in vaccine design, tend to confer a reduced risk of provoking autoimmune reactions. This reduced risk can be attributed to the relatively lower immunological pressure imposed by these mimics when compared to actual pathogens, which inherently exert additional immune pressure due to the manifestation of the disease itself.
The regulatory T cells (Tregs) also play a pivotal role in modulating the immune response on encountering the antigen (from the vaccine), ensuring its proportionality and averting the development of autoimmunity [161]. These regulatory processes are further reinforced by natural checkpoints orchestrated by cytokines and other signaling molecules. These checkpoints serve as crucial regulators, fine-tuning the intensity and duration of the immune response [165], thus mitigating the risk of overly aggressive reactions that could harm the body tissues, instead of just the pathogen. Furthermore, cells can release immunosuppressive signals, including TGF-beta and IL-10, effectively dampening immune responses and preventing unwarranted reactivity against self-antigens [166,167]. Antigen-presenting cells (APCs) are another player in the immune system that present foreign antigens to immune cells, while self-antigens are less likely to incite a response [168]. As a result, the potential for cross-reactivity leading to autoimmune triggers is notably diminished in the context of mimic-based vaccine design, thus underscoring the safety and efficacy of this approach.
Additionally, the prudent approach of pre-vaccination testing for genetically predisposed individuals and the adoption of nanocarriers as alternatives to lipid adjuvants hold promise in mitigating the risk of cross-reactivity and triggering autoimmunity [163,169]. Furthermore, comparative evaluations of diverse vaccine formulations, concerning their capacity to induce or exacerbate pathology in relevant models, can yield valuable baseline data about the efficacy and safety of these vaccines. The inclusion of comprehensive immunological investigations, including autoimmune serology, within phases I to III of clinical trials is warranted to holistically assess vaccine responses. Hence, mimic-based innovative vaccine design, alongside the risk assessment and consideration of the inherent protective mechanisms of the immune system, offers a promising pathway toward vaccines that can effectively combat pathogens while sparing the human self-tissues from harm.

5. Conclusions

Investigation of autoimmune pathways associated with the identified human homologs of C. difficile revealed interesting connections to autoimmune diseases. The identified associations with autoimmune diseases, particularly rheumatoid arthritis, warrant further investigation into the underlying mechanisms of autoimmunity and the specific roles of these homologous proteins in disease pathogenesis. The structural similarity between human and C. difficile homologs suggests the possibility of using these bacterial proteins as structural templates for vaccine design and development. Understanding the conserved regions and functional motifs in these proteins may also aid in the design of therapeutics targeting C. difficile and related human diseases. We fabricated a vaccine construct using conserved, safe, and immunogenic mimics. It demonstrated good response in silico, but computational predictions have limitations, and we imply experimental research to complement or refute our findings.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms11092300/s1, Figure S1: (A) 3D structure of vaccine construct using Swiss-Model (B) Ramachandran plot of the model; Table S1: Non-allergenic vaccine constructs against C. difficile; Table S2: 3D structure statistics of the vaccine construct, using various tools; Table S3: HLA and TLR receptor interaction statistics with the designed vaccine construct. Non-bonded contacts can involve attractive forces, such as van der Waals interactions and hydrophobic interactions, or repulsive forces, such as steric clashes. No disulphide bond was detected in any interaction.

Author Contributions

S.A., Z.B., M.M.M. and A.A. conceptualized the study. Z.B., M.A.A. and H.H.A. performed the experiments. N.A.A., M.M.M. and S.A. curated the data. S.A., Z.B., M.M.M. and A.A. wrote and edited the original draft. H.H.A., N.A.A. and M.A.A. reviewed and edited the draft. A.A., N.A.A. and M.A.A. visualized the study. S.A., Z.B., M.M.M. and H.H.A. obtained the funding. S.A., Z.B. and M.M.M. administered the project. M.M.M. supervised the study. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Deanship of Scientific Research at Najran University Research Support Program under the grant program code NU/DRP/MRC/12/5.

Data Availability Statement

All the data used or generated in this study are provided as an accession number or relevant information as tables in the manuscript.

Acknowledgments

The authors are also thankful to the Deanship of Scientific Research at Najran University for funding this work, under the General Research Funding program grant code NU/DRP/MRC/12/5.

Conflicts of Interest

The authors declare that they have no conflict of interest.

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Microorganisms 11 02300 g001
Figure 1. Superposed structures of the C. difficile and human homologous proteins. (A) Molecular chaperone DnaK; (B) Translation elongation factor 4; (C) Uracil-DNA glycosylase; (D) Acetyl-CoA C-acetyltransferase; (E) 3-oxoacid CoA-transferase subunit B; (F) UDP-glucose 4-epimerase GalE; (G) V-type proton ATPase subunit B; (H) V-type ATP synthase catalytic unit A; (I) Phosphopyruvate hydratase; (J) ATP-dependent Clp endopeptidase proteolytic subunit ClpP; (K) ATP-dependent Clp endopeptidase proteolytic subunit ClpP; (L) F0F1 ATP synthase subunit beta. Due to space constraints, the first 12 (Table 1) of the 23 proteins are shown here. Human homologs are shown in brown and bacterial proteins are shown in blue.
Figure 1. Superposed structures of the C. difficile and human homologous proteins. (A) Molecular chaperone DnaK; (B) Translation elongation factor 4; (C) Uracil-DNA glycosylase; (D) Acetyl-CoA C-acetyltransferase; (E) 3-oxoacid CoA-transferase subunit B; (F) UDP-glucose 4-epimerase GalE; (G) V-type proton ATPase subunit B; (H) V-type ATP synthase catalytic unit A; (I) Phosphopyruvate hydratase; (J) ATP-dependent Clp endopeptidase proteolytic subunit ClpP; (K) ATP-dependent Clp endopeptidase proteolytic subunit ClpP; (L) F0F1 ATP synthase subunit beta. Due to space constraints, the first 12 (Table 1) of the 23 proteins are shown here. Human homologs are shown in brown and bacterial proteins are shown in blue.
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Microorganisms 11 02300 g002
Figure 2. Conservation of sequence mimics from (A) phosphoribosylaminoimidazolecarboxamide formyltransferase and (B) adenylosuccinate lyase used for vaccine design underlined by red (and star symbol). Yellow color indicates insufficient data for conservation inference.
Figure 2. Conservation of sequence mimics from (A) phosphoribosylaminoimidazolecarboxamide formyltransferase and (B) adenylosuccinate lyase used for vaccine design underlined by red (and star symbol). Yellow color indicates insufficient data for conservation inference.
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Microorganisms 11 02300 g003
Figure 3. The 5083 bp cloned vector of the vaccine construct.
Figure 3. The 5083 bp cloned vector of the vaccine construct.
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Figure 4. Immune system cells released after the C2 vaccine and C. difficile protein (phosphoribosylaminoimidazolecarboxamide formyltransferase and adenylosuccinate lyase) stress, including (A) immunoglobulins, (B) B cells, (C) TH cells, (D) TC cells, and (E) NK cells.
Figure 4. Immune system cells released after the C2 vaccine and C. difficile protein (phosphoribosylaminoimidazolecarboxamide formyltransferase and adenylosuccinate lyase) stress, including (A) immunoglobulins, (B) B cells, (C) TH cells, (D) TC cells, and (E) NK cells.
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Figure 5. Vaccine construct (shown in green) interaction with (A) HLA-A, (B) HLA-B, and (C) TLR-4. Receptors are shown in cyan. (D) Control Tumor necrosis factor ligand superfamily member 11 (RANK-L) and 11A (RANK) from Mus musculus.
Figure 5. Vaccine construct (shown in green) interaction with (A) HLA-A, (B) HLA-B, and (C) TLR-4. Receptors are shown in cyan. (D) Control Tumor necrosis factor ligand superfamily member 11 (RANK-L) and 11A (RANK) from Mus musculus.
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Table 1. RMSD of C. difficile homologs in humans. Molecular mimics with length ≥ 10 are also shown.
Table 1. RMSD of C. difficile homologs in humans. Molecular mimics with length ≥ 10 are also shown.
Serial no.NameUniProt ID of Human HomologNCBI Accession of Bacterial HomologBacterial Protein Structure AlphaFold IDNo. of Similar Peptides (Length ≥ 10)Molecular Mimic Region (Length ≥ 10)Superposed Protein RMSD
(iPBA)		TM-Align RMSD
1Molecular chaperone DnaKP38646QWS53804.1Q182E86GIDLGTTNSCVAV
RTTPSVVAFT
INEPTAAALAYG
LLLDVTPLSLGIET
RNTTIPTKKSQ
PQIEVTFDIDANGIV		1.132.0
2Translation elongation factor 4Q8N442QWS53810.1Q182F43DHGKSTLADRL
LNLIDTPGHVDF
VLAKCYGGDI		1.251.55
3Uracil-DNA glycosylaseP13051QWS53824.1Q182G90-1.011.13
4Acetyl-CoA C-acetyltransferaseQ9BWD1QWS54012.1Q18AR01NASGINDGAA0.700.75
53-oxoacid CoA-transferase subunit BE9PDW2QWS54014.1Q183B10-1.001.74
6UDP-glucose 4-epimerase GalEQ14376QWS54050.1Q183E83GGAGYIGSHT
VFSSSATVYG
DGTGVRDYIHV		0.790.79
7V-type proton ATPase subunit BP21281QWS54278.1Q184E32YAEALREVSAA
THPIPDLTGYITEGQI		0.810.91
8V-type ATP synthase catalytic unit AP38606QWS54279.1Q184E72MPVAAREASIYTGIT
MADSTSRWAEALRE		0.981.03
9Phosphopyruvate hydrataseP09104QWS54488.1Q181T53LDSRGNPTVEV
QEFMILPVGA
VGDEGGFAPN		0.871.39
10ATP-dependent Clp endopeptidase proteolytic subunit ClpPQ16740QWS54701.1Q180F01VVEQTGRGER0.510.51
11ATP-dependent Clp endopeptidase proteolytic subunit ClpPQ16740QWS54730.1Q180J60-0.560.56
12F0F1 ATP synthase subunit betaP06576QWS54823.1Q184E311TGIKVVDLLAPY
KGGKIGLFGGAGVGKTVLI
G VGERTREGNDLY
GQMNEPPGAR
DNIFRFTQAGSEVSALLGR
PSAVGYQPTLAT
TKKGSITSVQA
YVPADDLTDPAPATTF
LGIYPAVDPL
LQDIIAILGMDELS
RARKIQRFLSQ		2.112.73
13F0F1 ATP synthase subunit alphaP25705QWS54825.1Q184E77IKEGDIVKRTG
PIGRGQRELIIGDRQTGKTSI
CIYVAIGQKRST
YTIVVSATAS
YDDLSKQAVAYR
MSLLLRRPPGREAYPGDVFYLHSRLLERAAK
IETQAGDVSAYIPTNVISITDGQI		1.522.69
14Elongation factor TuP49411QWS55098.1Q18CE43GTIGHVDHGKTTLTAAITK
DCPGHADYVKNMITG
DGPMPQTREH		0.981.12
15Elongation factor TuP49411QWS55112.1Q18CE43GTIGHVDHGKTTLTAAITK
DCPGHADYVKNMITG
DGPMPQTREH		0.981.12
16Methionine adenosyltransferaseP31153QWS55174.1Q18CL74EGHPDKICDQISD
RFVIGGPQGD
HGGGAFSGKD
TKVDRSAAYAAR		0.720.93
17Chaperonin GroELP10809QWS55239.1Q18CT53AGDGTTTATVLA
VVAVKAPGFGD
DALNATRAAVEEGIV		1.223.61
18Isocitrate/isopropylmalate dehydrogenase family proteinP50213QWS55807.1Q18A335VTLIPGDGIGPE
VMPNLYGDILSDL
AGDGTTTATVLA
VVAVKAPGFGD
DALNATRAAVEEGIV		0.891.48
19Phosphoribosylaminoimidazolecarboxamide formyltransferaseP31939QWS55813.1Q18A345WQLVKELKEA
SFKHVSPAGAAVG
REVSDGIIAPGY
KYTQSNSVCYAK
GAGQQSRIHCTRLAG		0.731.23
20Acyl-CoA dehydrogenaseP16219QWS55947.1Q18AQ12LIFEDCRIPK
ITEIYEGTSE		0.720.99
21Acetyl-CoA C-acetyltransferaseQ9BWD1QWS55952.1Q18AR02NASGINDGAA0.700.75
22Fe-S cluster assembly scaffold protein NifUQ9H1K1QWS56138.1Q18BE32GCGSAIASSS1.011.39
23Adenylosuccinate lyaseP30566QWS56192.1Q18BJ92RGVKGTTGTQASFL
YKRNPMRSER		0.810.94
Table 2. Autoimmune pathways of the selected homologs.
Table 2. Autoimmune pathways of the selected homologs.
Serial no. Protein HomologPHAROSPATHDIPLiterature
Autoimmunity PathwayInfection PathwayAutoimmune PathwayInfection PathwayAutoimmune PathwayInfection Pathway
1P38646Molecular chaperone DnaKAutoimmune disease, Parkinson’s diseasePerinatal necrotizing enterocolitis, HIV, TuberculosisParkinson’s disease, Huntington’s disease, Diabetes mellitus, Alzheimer’sTuberculosis, HIV, Papillomavirus, E. coli, Cytomegalovirus, Staphylococcus sp., Legionellosis, Chagas, Leishmaniasis, MeaslesGuillain–Barré syndrome [69], Multiple sclerosis [70], Vitiligo [71], Systemic lupus erythematosus [72], Ankylosing spondylitis [73], Type I Diabetes mellitus [74], Rheumatoid arthritis [75]Trypanosoma cruzi [76], Mycobacterium leprae [77]
2Q8N442Translation elongation factor 4-- Tuberculosis, Rheumatoid arthritis--
3P13051Uracil-DNA glycosylase---HIV, Viral carcinogenesis--
4Q9BWD1Acetyl-CoA C-acetyltransferase---HBV, Viral carcinogenesisSystemic lupus erythematosus [78]HCV [79]
5E9PDW23-oxoacid CoA-transferase subunit BCrohn’s disease---Rheumatoid arthritis [80]-
6Q14376UDP-glucose 4-epimerase GalEPsoriasis, Interstitial cystitisTinea corporis, Tinea pedis--Type I Diabetes mellitus [81]Hemophilus influenzae [82]
7P21281V-type proton ATPase subunit BIgA glomerulonephritis-Huntington’s disease, Rheumatoid arthritisHelicobacter pylori infection, HPV, Tuberculosis, Viral carcinogenesis, Vibrio chloerae, HIV-Tuberculosis [83], SARS-CoV-2 [84]
8P38606V-type ATP synthase catalytic unit APsoriasis, Myopathy-Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, Rheumatoid arthritis,HPV, H. pylori, Tuberculosis, Vibrio cholerae, Viral carcinogenesis, HIVThyroid eye disease [85]Influenza H1N1 [86], Salmonellosis [87], Rabies virus [88], SARS-CoV-2 [89], Tuberculosis [90]
9P09104Phosphopyruvate hydratasePsoriasis---Autoimmune encephalomyelitis [91], Rheumatoid arthritis [92]Cytomegalovirus [93]
10Q16740ATP-dependent Clp endopeptidase proteolytic subunit ClpPPsoriasis-Alzheimer’s disease, Huntington’s disease, Parkinson’s disease--Tuberculosis [94]
11P06576F0F1 ATP synthase subunit beta--Alzheimer’s, Huntington’s, Parkinson’s, Non-alcoholic fatty-acid liver diseasesEpstein–Barr virus infection, HBV, HCV, HPV, Measles, Legionellosis, E. coliAutoimmune myocarditis [95]MERS coronavirus [96], Echinococcus granulosus [97]
12P25705F0F1 ATP synthase subunit alphaAlzheimer’s disease---Sjogren’s syndrome [98], Crohn’s disease [98], Ankolysing spondolytis [73]-
13P49411Elongation factor Tu--Huntington’s, Parkinson’sHCV, HBV, Legionellosis, E. coli, V. choleraeSjogren’s syndrome [99], Crohn’s disease [98], Ankolysing spondylitis [73]Streptococcus pneumoniae [100], bacteria like Bacillus anthracis, Francisella talurensis, Staphylococcus sp., E. coli, H. pylori, etc. [101]
14P31153Methionine adenosyltransferaseType 2 diabetes mellitus, demyelinating diseases, MODY, Psoriasis, fatty liver, or non-alcoholic steatohepatitis---Rheumatoid arthritis [102], Uveitis
[103]		Herpes simplex type 1 [104], Poxvirus [105], West Nile virus [106]
15P10809Chaperonin GroELAllergic rhinitisTuberculosis, HIV--Type I Diabetes
[103], Juvenile chronic arthritis [107], Atherosclerosis [108], Crohn’s disease [109], Rheumatoid arthritis [110], Systemic lupus erythematosus [111], Sjogren syndrome [112], Hashimoto thyroiditis [113,114], and myasthenia gravis [115], Autism [116]		H. pylori [117], P. aeruginosa and S. aureus [118]
16P50213Isocitrate dehydrogenase [NAD] subunit alphaPsoriasis-Huntington’s disease, Parkinson’s diseaseEpstein–Barr virus, HCV, LegionellosisAtherosclerosis [119], Type I diabetes [120]H. pylori [117]
17P31939Phosphoribosylaminoimidazolecarboxamide formyltransferaseRheumatoid arthritis, Psoriatic arthritis, Erythrodermic psoriasis, Pustular psoriasis, Plaque psoriasis, Diabetes mellitus, Juvenile idiopathic arthritis----C. neoformans [121]
18P16219Acyl-CoA dehydrogenaseAllergic rhinitis, Ulcerative colitis, Crohn’s disease-----
19Q9BWD1Acetyl-CoA C-acetyltransferase--Parkinson’s diseaseHBV, Viral carcinogenesis--
20Q9H1K1Fe-S cluster assembly scaffold protein NifU--Parkinson’s diseaseInfluenza, HIV-Human respiratory syncytial virus [122], SARS-CoV-1 [96]
21P30566Adenylosuccinate lyasePsoriasis----Schistosomiasis [123], Chlamydia sp. [124]
Table 3. Immunogenicity, allergenicity, and other properties of the antigenic mimics.
Table 3. Immunogenicity, allergenicity, and other properties of the antigenic mimics.
Serial no.Antigenic ScoreSequenceLengthImmunogenicity ScoreSVM Score for ToxicityHydrophobicityHydropathicityHydrophilicityChargeMol WtAllergenicity
1.1.09GIDLGTTNSCVAV150.650.070.85−0.42−11249.590.07No
2.0.58RTTPSVVAFT120.35−0.070.4−0.3911078.36−0.07No
3.0.89LLLDVTPLSLGIET120.310.151.18−0.49−21483.990.15yes
4.1.39PQIEVTFDIDANGIV110.290.040.42−0.16−31631.040.04yes
5.0.80DHGKSTLADRL120.26−0.33−1.010.660.51212.48−0.33No
6.1.22NASGINDGAA120.26−0.05−0.220.04−1889.02−0.05No
7.0.65GGAGYIGSHT240.210.08−0.13−0.520.5919.110.08No
8.1.47DGTGVRDYIHV100.18−0.15−0.420.09−0.51231.49−0.15No
9.1.20LDSRGNPTVEV100.18−0.23−0.570.39−11186.44−0.23yes
10.1.48VVEQTGRGER110.14−0.42−1.260.8801130.37−0.42No
11.0.54TGIKVVDLLAPY100.120.10.91−0.4701288.730.1No
12.1.25VGERTREGNDLY110.12−0.41−1.480.77−11408.66−0.41No
13.1.03PSAVGYQPTLAT100.110.030.08−0.5801204.510.03No
14.1.04TKKGSITSVQA140.11−0.19−0.380.221119.44−0.19no
15.0.54LGIYPAVDPL100.090.190.97−0.67−11057.40.19yes
16.0.65IKEGDIVKRTG100.06−0.29−0.690.8611215.58−0.29No
17.0.70CIYVAIGQKRST120.05−0.130.2−0.2321338.76−0.13No
18.0.53YTIVVSATAS100.020.141.22−0.8301011.270.14No
19.0.84IETQAGDVSAYIPTNVISITDGQI120.010.020.25−0.26−32506.130.02No
20.0.56DGPMPQTREH13−0.05−0.41−2.060.7−0.51167.4−0.41No
21.1.41EGHPDKICDQISD10−0.05−0.28−1.220.8−2.51456.73−0.28No
22.1.22HGGGAFSGKD12−0.06−0.1−0.840.280.5932.1−0.1No
23.0.57TKVDRSAAYAAR15−0.12−0.35−0.650.5121308.6−0.35No
24.1.75AGDGTTTATVLA12−0.130.060.52−0.28−11077.320.06No
25.1.07VTLIPGDGIGPE10−0.160.110.41−0.11−21167.510.11No
26.1.76AGDGTTTATVLA14−0.170.060.52−0.28−11077.320.06No
27.1.62GAGQQSRIHCTRLAG13−0.18−0.23−0.50.012.51554.97−0.23No
28.1.22NASGINDGAA10−0.19−0.05−0.220.04−1889.02−0.05No
29.1.42GCGSAIASSS10−0.200.060.66−0.260839.010.06No
30.1.65RGVKGTTGTQASFL11−0.22−0.14−0.24−0.0721422.81−0.14No
31.0.55YKRNPMRSER11−0.25−0.77−2.621.1931336.66−0.77No
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Alshamrani, S.; Mashraqi, M.M.; Alzamami, A.; Alturki, N.A.; Almasoudi, H.H.; Alshahrani, M.A.; Basharat, Z. Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in Clostridioides difficile and Prospects of Mimic-Based Vaccine Design: An In Silico Approach. Microorganisms 2023, 11, 2300. https://doi.org/10.3390/microorganisms11092300

AMA Style

Alshamrani S, Mashraqi MM, Alzamami A, Alturki NA, Almasoudi HH, Alshahrani MA, Basharat Z. Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in Clostridioides difficile and Prospects of Mimic-Based Vaccine Design: An In Silico Approach. Microorganisms. 2023; 11(9):2300. https://doi.org/10.3390/microorganisms11092300

Chicago/Turabian Style

Alshamrani, Saleh, Mutaib M. Mashraqi, Ahmad Alzamami, Norah A. Alturki, Hassan H. Almasoudi, Mohammed Abdulrahman Alshahrani, and Zarrin Basharat. 2023. "Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in Clostridioides difficile and Prospects of Mimic-Based Vaccine Design: An In Silico Approach" Microorganisms 11, no. 9: 2300. https://doi.org/10.3390/microorganisms11092300

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