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13 August 2025

In Silico Design of a Multiepitope Vaccine Against Intestinal Pathogenic Escherichia coli Based on the 2011 German O104:H4 Outbreak Strain Using Reverse Vaccinology and an Immunoinformatic Approach

,
and
1
The Lundquist Institute at Harbor UCLA Medical Center, Torrance, CA 90502, USA
2
Biotechnology Department, Faculty of Postgraduate Studies for Advanced Sciences, Beni-Suef University, Beni-Suef 62511, Egypt
3
Pathology Department, Faculty of Veterinary Medicine, Beni-Suef University, Beni-Suef 62511, Egypt
4
Department of Microbiology and Immunology, Faculty of Pharmacy, King Salman International University, Ras Sudr 46611, Egypt
Diseases2025, 13(8), 259;https://doi.org/10.3390/diseases13080259
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Abstract

Background: While most Escherichia coli strains are harmless members of the gastrointestinal microbiota, certain pathogenic variants can cause severe intestinal and extraintestinal diseases. A notable outbreak of E. coli O104:H4, involving both enteroaggregative (EAEC) and enterohemorrhagic (EHEC) strains, occurred in Europe, resulting in symptoms ranging from bloody diarrhea to life-threatening colitis and hemolytic uremic syndrome (HUS). Since treatment options remain limited and have changed little over the past 40 years, there is an urgent need for an effective vaccine. Such a vaccine would offer major public health and economic benefits by preventing severe infections and reducing outbreak-related costs. A multiepitope vaccine approach, enabled by advances in immunoinformatics, offers a promising strategy for targeting HUS-causing E. coli (O104:H4 and O157:H7 serotypes) with minimal disruption to normal microbiota. This study aimed to design an immunogenic multiepitope vaccine (MEV) construct using bioinformatics and immunoinformatic tools. Methods and Results: Comparative proteomic analysis identified 672 proteins unique to E. coli O104:H4, excluding proteins shared with the nonpathogenic E. coli K-12-MG1655 strain and those shorter than 100 amino acids. Subcellular localization (P-SORTb) identified 17 extracellular or outer membrane proteins. Four proteins were selected as vaccine candidates based on transmembrane domains (TMHMM), antigenicity (VaxiJen), and conservation among EHEC strains. Epitope prediction revealed ten B-cell, four cytotoxic T-cell, and three helper T-cell epitopes. Four MEVs with different adjuvants were designed and assessed for solubility, stability, and antigenicity. Structural refinement (GALAXY) and docking studies confirmed strong interaction with Toll-Like Receptor 4 (TLR4). In silico immune simulations (C-ImmSim) indicated robust humoral and cellular immune responses. In Conclusions, the proposed MEV construct demonstrated promising immunogenicity and warrants further validation in experimental models.

1. Introduction

Escherichia coli, a gram-negative bacterium, is a common constituent of the normal microbiota in humans and animals. It primarily colonizes the gastrointestinal tract, particularly the large intestine, soon after birth. While typically part of the normal gut flora, E. coli can also contribute to various illnesses, both intestinal and extraintestinal [1]. The Centers for Disease Control and Prevention (CDC) categorize intestinal E. coli causing gastroenteritis into various pathotypes, including enteroaggregative E. coli (EAEC), enteroinvasive E. coli (EIEC), enterotoxigenic E. coli (ETEC), enteropathogenic E. coli (EPEC), and enterohemorrhagic E. coli (EHEC). It is important to note that enterohemorrhagic E. coli (EHEC) is a subset of Shiga toxin–producing E. coli (STEC), although these terms are sometimes used interchangeably [2]. Each pathotype has a unique pathogenesis and is characterized by specific O:H serotypes, contributing to various epidemiological patterns and associated pathological conditions. Nevertheless, their interchangeable nature complicates the differentiation of traits within each subclass [3].
While some pathogenic strains of E. coli had been recognized earlier, E. coli O157:H7 was first identified as a major cause of foodborne illness in 1982, following outbreaks of bloody diarrhea in the U.S. This strain, a type of enterohemorrhagic E. coli (EHEC), has since become a significant public health concern [4,5,6,7]. Subsequently, sporadic cases emerged, showing severe colonic and/or renal diseases, such as hemolytic uremic syndrome (HUS) [6,8]. EHEC poses a significant global public health concern, primarily as a foodborne illness. It spreads through contaminated food during meat processing (slaughter process) or via EHEC-contaminated water reaching agricultural produce. The consumption of raw or undercooked beef, especially ground beef (hamburger), serves as a common transmission route for EHEC O157:H7. Outbreaks have also been linked to contaminated foods such as radish sprouts (e.g., the Sakai city incident in Japan in 1996), lettuce, spinach, strawberries, and tainted water [9,10,11,12,13,14,15]. Human fecal contamination of food and seeds may additionally contribute, particularly in developing countries [16].
While EHEC O157:H7 remains the most common and prevalent serotype associated with sporadic HUS cases [17], other non-O157:H7 serotypes are also noteworthy. In May 2011, a rare and novel O104:H4 serotype outbreak captured global attention for its significant impact, notably in Germany [18,19], France [20], and other European countries. Although there were only a few reported cases in Canada and the United States, these were primarily individuals who had recently visited Europe before becoming ill. The 2011 outbreak of E. coli O104:H4 in Europe displayed unusual virulence and lethality patterns [21]. Although a related E. coli O104:H4 strain was reported in a 2009 outbreak in the Republic of Georgia, that strain exhibited a different molecular profile, lacked some key virulence factors (such as the Shiga toxin gene), and showed lower levels of antibiotic resistance. This indicates that while both strains share the O104 serogroup, they represent distinct lineages with different pathogenic potentials.
Transmission during the 2011 outbreak was primarily through consumption of fenugreek sprouts contaminated with E. coli O104:H4 [22,23,24,25], and the outbreak showed limited zoonotic potential [26,27,28,29]. HUS cases began to cluster in northern Germany in May 2011, peaking and declining by July due to control measures. The Robert Koch Institute (RKI) reported 3842 cases, including 855 cases of HUS and 53 fatalities [19]. Approximately one month later, a smaller outbreak involving the same E. coli O104:H4 strain occurred in France [17,20].
This 2011 STEC outbreak, as reported by the WHO, affected 4075 people in 16 countries, resulting in 908 cases of HUS and 50 deaths, with a mortality rate of 1.23%. The mortality rate for HUS caused by E. coli O104:H4 was notably greater at 3.74% [30]. Approximately 90% of HUS cases occurred in adults, with about two-thirds of those in females. Around 10% of HUS cases were reported in children [19]. Transmission likely occurred through contact with infected individuals. This strain is more likely to cause severe disease in adults than in children [31].
The O104:H4 strain is a rare hybrid of enteroaggregative and Shiga toxin-producing E. coli resulting from genetic integration of virulence factors from both types, termed enteroaggregative hemorrhagic E. coli (EAHEC) [32]. This strain, identified in the 2011 outbreak, belonged to an EAEC lineage that acquired genes for Stx2 and antibiotic resistance [32,33,34,35,36]. It exhibits aggregative adherence fimbriae (AAFs) similar to those of EAEC, facilitating strong adhesion to food surfaces and the intestinal wall and enhancing its persistence and pathogenicity. This enhanced adhesion may also increase the absorption of Shiga toxin (Stx), the key factor contributing to the destruction of gut epithelial cells, leading to more severe symptoms, including abdominal cramps, bloody diarrhea, and HUS [2,37,38]. The strain produces extended-spectrum beta-lactamases (ESBL) [39,40], contributing to its ability to colonize and release toxins in the gut [34]. Infections typically last approximately two weeks, with an increased risk of developing HUS and mortality if treatment is inadequate [19,41,42].
Treatment for infections caused by EHEC, particularly strains such as O104:H4 and O157:H7, primarily involves supportive care due to the lack of specific antimicrobial therapies, which could worsen symptoms by triggering toxin release [43]. Supportive measures include hydration and electrolyte balance management, often requiring hospitalization for close monitoring. In severe cases, such as those leading to HUS, interventions such as blood transfusions and renal replacement therapy may be necessary [44,45,46,47]. The treatment of HUS caused by these infections is particularly challenging and has unpredictable outcomes. Traditional antibiotics do not effectively treat the disease and may even exacerbate symptoms by increasing Shiga toxin release. The complex pathogenesis of EHEC, involving adherence to intestinal cells and toxin production, complicates therapeutic strategies. Research into antibody-based therapies targeting Shiga toxins has shown promise in reducing toxin-mediated damage, although their clinical effectiveness remains uncertain and requires further study [43,48,49,50,51].
Given the lack of effective treatments and the serious clinical consequences associated with EHEC serotypes O104:H4 and O157:H7, the CDC categorizes them as high-risk pathogens capable of causing outbreaks with significant illness and complications [2,52]. Consequently, researchers are increasingly focusing on vaccine development as a preventive strategy, especially for travelers and high-risk populations [53]. Vaccines targeting specific EHEC serotypes, such as O157:H7 and O104:H4, aim to prevent infection and reduce symptom severity. The cost-effectiveness of vaccination in mitigating healthcare burdens from EHEC outbreaks and complications like HUS highlights their public health importance [7,54].
Despite ongoing efforts, no approved vaccines currently exist for EHEC infections. Novel research is crucial to identify effective vaccine candidates that specifically target pathogenic strains like O157:H7 and O104:H4 while preserving the normal gut flora. However, the genetic and antigenic diversity of EHEC strains remains a major challenge for vaccine development. Although infrequent, the severity and unique virulence profile of O104:H4 justify its inclusion in vaccine research efforts, particularly as a model for hybrid EHEC/EAEC strains. Developing multivalent or cross-protective vaccines that account for this diversity is a critical public health priority to prevent infections and reduce severe complications like HUS [43].
Reverse vaccinology, which leverages genomic data and bioinformatics to identify potential vaccine targets, has accelerated vaccine candidate discovery. In particular, multiepitope vaccines (MEVs) are gaining interest due to advances in computational methods for predicting epitopes and immune responses [55,56]. MEVs combine selected epitopes from multiple proteins into a single construct to enhance immunogenicity and efficacy. This approach has been widely studied across various pathogens and holds promise for next-generation vaccine development [57,58,59,60,61,62,63,64,65,66].

Aim of Work

To design a multiepitope vaccine based on proteins uniquely present in the E. coli O104:H4 proteome that are conserved in the predominant EHEC strain E. coli O157:H7, with the goal of targeting pathogenic strains specifically while preserving the commensal gut microbiota.

2. Materials and Methods

2.1. Data Retrieval and Comparative Proteomic Analysis Using a Reverse Vaccinology Approach

To identify potential vaccine targets specific to the pathogenic Escherichia coli O104:H4 strain, a reverse vaccinology approach was employed. Complete proteomes of E. coli O104:H4 strain 2011C-3493 and the commensal E. coli K-12 substr. MG1655 were retrieved from the NCBI FTP site using GenBank assembly accession numbers GCF_000299455.1 (CP003289.1) and GCA_000005845.2 (U00096.3), respectively.
A comparative proteomic analysis using standalone BLASTP v2.2.26 was conducted to identify proteins unique to the pathogenic O104:H4 strain. Proteins showing >80% coverage and >40% similarity to those in the K-12 strain were excluded, along with proteins shorter than 100 amino acids. This approach aimed at eliminating proteins shared with commensal E. coli, thereby focusing on strain-specific proteins that may contribute to pathogenicity while minimizing disruption to the normal gut flora. Proteins shorter than 100 amino acids were excluded because they are more likely to be non-functional, hypothetical, or result in spurious alignments, which could obscure the identification of meaningful virulence factors.

2.2. Bioinformatic Characterization of the Candidate Proteins

The unique proteins identified were subjected to a series of bioinformatic analyses to evaluate their suitability as vaccine targets. Since ideal vaccine candidates are typically extracellular or outer membrane proteins, subcellular localization was first assessed using the P-SORTb tool (https://www.psort.org/psortb/ (accessed on 1 June 2023)) [67]. Seventeen proteins were predicted to be located in the outer membrane and extracellular space. Subsequently, these proteins were further evaluated based on several key criteria, including antigenicity using VaxiJen (https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html (accessed on 1 June 2023)) [68], conservation among other E. coli O104:H4 strains and related intestinal pathogenic strains (e.g., O157:H7) to ensure broad coverage, similarity to human proteins to minimize cross-reactivity, and functional relevance related to virulence or pathogenicity. Additionally, transmembrane prediction was conducted using TMHMM 2 (https://services.healthtech.dtu.dk/services/TMHMM-2.0/ (accessed on 1 June 2023)) [69] to ensure accessibility of epitopes.
After completing the reverse vaccinology-driven selection process, the most promising vaccine candidates were selected for epitope mapping. Prior to epitope prediction, signal peptide regions were identified using SignalP (https://services.healthtech.dtu.dk/services/SignalP-5.0/ (accessed on 1 June 2023)) [70] and LipoP (https://services.healthtech.dtu.dk/services/LipoP-1.0/ (accessed on 1 June 2023)) [71] and subsequently excluded to ensure accurate epitope identification within the mature protein regions.

2.3. Epitope Mapping

Epitope mapping was performed to identify specific immunogenic regions within the selected proteins that can effectively elicit a targeted immune response without causing allergenic or toxic effects.

2.3.1. Linear B-Lymphocyte (LBL) Epitope Prediction

Linear B-cell epitopes for each target protein were predicted using multiple computational tools to enhance prediction reliability:
ABCpred (https://webs.iiitd.edu.in/raghava/abcpred/ABC_submission.html (accessed on 1 July 2023)) [72,73] with a cutoff score ≥ 0.8 at a length of 16 amino acids.
BepiPred-2.0 was obtained from the Immune Epitope Database (IEDB) (http://tools.iedb.org/bcell/ (accessed on 1 July 2023)) [74].
BCEPRED (https://webs.iiitd.edu.in/raghava/bcepred/bcepred_submission.html (accessed on 1 July 2023)) [75] at a threshold of −3 to 3 are based on both accessibility and surface exposure.
The predicted LBL epitopes were further screened:
The selection of the final LBL epitopes was based on the frequency of predication across different software tools, isotype predication (IgG or IgA), absence within signal peptides, and their non-allergenic, non-toxic, and virulent characteristics.

2.3.2. Cytotoxic T-Lymphocyte (CTL) Epitope Prediction

NetMHC-4.0 (https://services.healthtech.dtu.dk/services/NetMHC-4.0/ (accessed on 1 July 2023)) was used to predict cytotoxic T-cell epitopes [79,80]. Predictions of MHC-I binding were performed for 23 HLA-A, HLA-B, HLA-C, and HLA-E alleles with a length of 9 mer. Epitopes were selected based on a threshold for strong binding of 2% (adjusted rank ≤ 2); weak binders (adjusted rank ≤ 5%) were excluded.
The immunogenicity of the predicted CTL epitopes was assessed using Class I immunoreactivity from the IEDB analysis resource (http://tools.iedb.org/immunogenicity/ (accessed on 1 July 2023)) [81].
To evaluate TAP transport and proteasomal cleavage, the NetCTL1.2 server (http://www.cbs.dtu.dk/services/NetCTL (accessed on 1 July 2023)) was used [82]. Epitopes with an epitope identification score > 0.75 were considered, indicating high-quality proteasomal cleavage and efficient TAP transport, which are crucial for antigen presentation to CTLs.
The selection of the CTL epitopes was based on predicted binding to multiple alleles of MHC-I, immunogenicity, favorable TAP cleavage IC50 values, strong antigenicity scores, and predictions indicating non-allergenicity, non-toxicity, and virulence potential.

2.3.3. Helper T-Lymphocyte (HTL) Epitope Prediction

NETMHCII_pan 4.0 (https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.0/ (accessed on 1 July 2023)) was used to predict possible helper T-cell epitopes in each of the targeted proteins [83]. The prediction of MHC-II binding was performed for 20 HLA-DR alleles with a length of 15 mer. Epitopes were selected based on a threshold for strong binding of 2% (adjusted rank ≤ 2); weak binders (adjusted rank ≤ 5%) were excluded.
For each predicted HTL epitope, the ability to induce cytokine production was assessed:
The selection of HTL epitopes was based on predicted binding to multiple MHC-II alleles, their ability to induce key cytokines (IFN-γ, IL-4, and IL-10), high antigenicity scores, non-allergenic and non-toxic profiles, and demonstrated virulence potential.

2.4. Construction of a Multiepitope Vaccine (MEV)

A potential MEV was constructed by linking selected LBL, CTL, and HTL epitopes identified during epitope mapping. These epitopes were connected using KK, AAY, and GPGPG amino acid linkers, respectively, to ensure effective in vivo separation [58,87]. To maximize immunogenicity, the assembled epitopes were conjugated with various adjuvants known to enhance different aspects of the immune response (to be further discussed in Section 3), including a partial sequence of human β-defensin (GIINTLQKYYCRVRGGRCAVLSCLPKEEQIGKCSTRGRKCCRRKK), cholera toxin subunit B (CTXB) partial (AAISMAN), S. dublin flagellin, and RS09 (APPHALS).
Additionally, the Pan DR epitope (PADRE) sequence (AKFVAAWTLKAAA) was strategically incorporated into the construct to augment immune responses and as a stabilizer [88]. The adjuvants were linked with epitope assembly via an EAAAK linker, optimizing the functionality of the MEV construct [89,90].

2.5. Physicochemical Properties, Solubility Profile, Antigenicity, and Allergenicity of the Constructed MEV

The physicochemical properties were evaluated using the ExPASy ProtParam tool (https://web.expasy.org/protparam/ (accessed on 1 September 2023)) [91]. This tool calculates various properties, including molecular weight, theoretical isoelectric point (pI), estimated half-life, instability index, aliphatic index, and grand average hydropathicity (GRAVY). The solubility profile was predicted using two different tools: SOLpro (https://scratch.proteomics.ics.uci.edu/ (accessed on 1 September 2023)) [92] and Protein-Sol (https://protein-sol.manchester.ac.uk/ (accessed on 1 September 2023)) [93]. A construct was considered highly soluble if its solubility score was greater than 0.5. Additionally, the presence of signal peptides and transmembrane regions was assessed, along with the construct’s similarity to human proteins to minimize potential autoimmune responses.

2.6. Secondary Structure Prediction

The secondary structures of the MEV constructs were predicted using PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/ (accessed on 1 September 2023)) [94], a PSI-blast-based secondary structure prediction tool.

2.7. Tertiary Structure Prediction, Refining and Validation

The tertiary (3D) structure was modeled using the de novo modeling tool I-TASSER (https://zhanggroup.org/I-TASSER/ (accessed on 1 October 2023)) [95]. Visualization of the 3D structure was performed using PyMOL education (https://pymol.org/edu/ (accessed on 1 October 2023)) [96]. The modeled 3D structure was subjected to a refinement process using the GalaxyRefine tool on the GlaxyWEB server (https://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE (accessed on 1 October 2023)). The best module was selected based on parameters including GDT-HA, RMSD, MolProbity, Clash score, Poor rotamers, and Rama favored [97]. Stereochemical quality was assessed based on dihedral angles (ψ and φ) using a Ramachandran plot provided by the PROCHECK application (https://saves.mbi.ucla.edu/) [98]. The refined model was validated by calculating the z score using the ProSA server (https://prosa.services.came.sbg.ac.at/prosa.php (accessed on 1 October 2023)) to ensure that it fell within the typical range for native proteins of similar size [99]. To confirm the validated model, ERRAT (https://saves.mbi.ucla.edu/ (accessed on 1 October 2023)) and Verify3D (https://saves.mbi.ucla.edu/ (accessed on 1 October 2023)) were utilized [100,101].

2.8. Disulfide Engineering

Disulfide engineering was assessed using the Disulfide by Design 2 online server (http://cptweb.cpt.wayne.edu/DbD2/index.php (accessed on 1 October 2023)), employing default settings [102].

2.9. Prediction of Glycosylation

Glycosylation was predicted using the GlycoPP v1.0 online server (https://webs.iiitd.edu.in/raghava/glycopp/submit.html (accessed on 1 October 2023)). Both N-linked and O-linked glycosylation predictions were conducted using prediction based on Binary Profile of Patterns (BPP) with the default parameters [103].

2.10. Molecular Docking of MEV

The finalized MEV construct was molecularly docked with Toll-like Receptor 4 (TLR4) (PDB ID: 4G8A) using the ClustPro server (https://cluspro.bu.edu/home.php (accessed on 1 October 2023)). The PDB files were submitted to the server using the default settings [104,105]. The best TLR4 vaccine-docked complex with the lowest energy was selected, and the resulting structure was visualized using the PyMOL tool.

2.11. Immune Stimulation

To simulate the real-life immune response, computational immune stimulation was conducted using the C-ImmSim online tool (https://kraken.iac.rm.cnr.it/C-IMMSIM/index.php?page=1 (accessed on 1 October 2023)). The default parameters were used for simulation, except for the time steps, which were set at 1, 84, and 170 (time step 1 represents the initial injection at time = 0, with subsequent injections every 8 h). The simulation was conducted with three injections, each four weeks apart, corresponding to the recommended interval between doses for most commercial vaccines [106].

3. Results

The initial step in designing a vaccine against pathogenic E. coli was identifying target proteins using a reverse vaccinology approach, as illustrated in Figure 1.
Figure 1. Schematic diagram illustrating the workflow for selecting target virulent E. coli O104:H4 proteins used in the multi-epitope vaccine in this study.

3.1. The Identification of Potential Vaccine Candidates That Are Not Shared with Nonpathogenic E. coli, Have Outer Membrane, Are Antigenic and Are Important for Virulence

To avoid disrupting the normal gut microbiota—which are essential for maintaining gut health—we screened the entire proteome of E. coli O104:H4 using reciprocal BLAST analysis. Proteins shared with the nonpathogenic E. coli strain K-12 MG1655 were excluded, as detailed in Section 2. This exclusion process resulted in a refined dataset of 672 proteins (Table S1).
Next, subcellular localization was predicted using P-SORTb to identify proteins likely to be exposed on the bacterial surface. This step revealed 17 proteins localized to the outer membrane or extracellular space (Table 1), making them initial candidates for vaccine development.
Table 1. List of the extracellular and outer membrane proteins of E. coli O104:H4 strain that are not shared with commensal E. coli strains.
To further narrow down these 17 proteins to the most promising vaccine candidates, we applied a set of stringent selection criteria:
(1)
Antigenicity—Proteins with a VaxiJen score > 0.4 were considered antigenic.
(2)
Transmembrane Helix—Proteins without predicted transmembrane helices were prioritized, ensuring full surface accessibility.
(3)
Conservation—Candidates conserved across multiple E. coli O104:H4 strains and also present in other pathogenic strains, such as E. coli O157:H7, were selected to ensure broad-spectrum protection.
(4)
Specificity—Proteins not shared with other nonpathogenic E. coli strains, such as HS and W3110, were chosen to confirm their pathogenic-specific nature and minimize cross-reactivity with commensal strains.
(5)
Human Proteome Exclusion—Proteins were checked against the human proteome via the NCBI database to avoid potential autoimmunity.
Following this rigorous filtration process, four proteins emerged as strong vaccine candidates due to their virulence relevance and favorable immunogenic profiles: copper resistance protein B (CopB), long polar fimbrial protein (LpfD), putative outer membrane protein Lom (LomP), and hypothetical protein O3K_20405 (Hcp_VI), as highlighted in Table 1.

3.2. Identification and Mapping of B-Cell and T-Cell Epitopes

Following the screening and selection of potential protein candidates from the E. coli O104:H4 proteome, the subsequent step in vaccine design involved scrutinizing each protein for potential linear B-cell, T-helper, and T-cytotoxic lymphocyte epitopes, separately.

3.2.1. Predication of Linear B-Cell Epitopes

The linear B-cell epitopes, which are crucial for inducing a humoral immune response, were predicted using three distinct online servers: ABCpred, BepiPred-2.0, and BCEPRED. These servers identified 39, 38, 36, and 23 epitopes for copB, LpfD, LomP, and Hcp_VI, respectively, as detailed in Table S2. Predicted epitopes were then evaluated for signal peptides, antigenicity, allergenicity, toxicity, virulence potential, and immunoglobulin isotype. Optimal epitopes were selected based on (1) consistency across multiple prediction tools to ensure accuracy, (2) high antigenicity score (VaxiJen score > 0.6), (3) nonallergenic and non-toxic characteristics, (4) virulence attributes, (5) absence of a signal peptide, and (6) ability to induce IgG or IgA isotypes. Ten LBL epitope sequences were finalized from all four proteins, and their sequences and properties are detailed in Table 2.
Table 2. List of the selected Linear B-lymphocyte (LBL) epitopes.

3.2.2. Predication of Cytotoxic T-Lymphocyte Epitope

The prediction of CTL epitopes was performed using NetMHC-4.0, where different alleles, including HLA-A, HLA-B, HLA-C, and HLA-E, were screened for MHC-I binding epitopes. Specifically, 108, 118, 83, and 53 epitopes were predicted for copB, LpfD, LomP, and Hcp_VI, respectively, as detailed in Table S3. The selection of CTL epitopes was based on consistent prediction across multiple alleles, immunogenicity, and favorable TAP cleavage IC50 values. One CTL epitope was selected from each protein, totaling four CTL epitopes, as listed in Table 3.
Table 3. List of the selected predicted Cytotoxic T-lymphocytes (CTL) epitopes.

3.2.3. Predication of Helper T-Lymphocyte Epitope

HTL epitopes were predicted using the NETMHCII_pan 4.0 server across various HLA-DR alleles, yielding 88 epitopes (21 for copB, 30 for LpfD, 24 for LomP, and 13 for Hcp_VI) as detailed in Table S4. From these, three HTL epitopes were selected (Table 4) based on their conservation across alleles, and ability to induce IL-4 and/or IL-10 cytokines, which are critical in bacterial infections.
Table 4. List of the selected predicted helper T-lymphocytes (HTL) epitopes.
Overall, the selected CTL and HTL epitopes demonstrate high antigenicity (VaxiJen score > 0.6), and are nonallergenic, nontoxic, and virulent.

3.3. Design of the MEV Construct

All 17 epitopes were interconnected using appropriate linkers. LBL epitopes were joined together using a bilysine (KK) linker. KK linkers are specifically recognized by cathepsin B, a lysosomal protease involved in processing antigenic peptides for their presentation on the cell surface via MHC-II-restricted antigen presentation [107,108]. For the CTL and HTL epitopes, alanine-tyrosine (AAY) and glycine-proline-glycine-proline (GPGPG) linkers were utilized, respectively. The AAY linker acts as a cleavage site for proteasomes in mammalian cells, facilitating the efficient separation of epitopes within cells [64,109,110]. GPGPG linkers are known to stimulate HTL responses. These linkers play a critical role in enhancing the immunogenicity of MEVs and are essential tools for overcoming junctional immunogenicity, thereby restoring the immunogenic potential of individual epitopes [60,62,111,112,113,114,115,116].
Four distinct MEV constructs were designed to elicit diverse immune responses, each incorporating different adjuvants. The selected adjuvants included partial human β-defensin (UniProt ID: P81534), which acts as an agonist for TLR1, 2, and4 [63,117]; cholera toxin B subunit (CTXB), a T-helper type 1 (Th1) agonist with anti-inflammatory properties [60]; Salmonella dublin flagellin, a TLR5 agonist known to stimulate IFN-γ and TNF-α production [118]; and RS09, a synthetic peptide serving as a TLR4 agonist [119]. Additionally, PADRE, a universal peptide, was included as a T-helper epitope separated by the EAAK sequence, which is known to enhance the functionality of MEV constructs when conjugated with other adjuvants [59,89,120]. Figure 2 illustrates the finalized MEV constructs, named Ecoepvc, following the integration of the various adjuvants.
Figure 2. Amino acid sequences of the vaccine constructs. (A) Ecoepvc1, 399 amino acids; (B) Ecoepvc2, 393 amino acids; (C) Ecoepvc3, 534 amino acids; (D) Ecoepvc4, 532 amino acids. Human βdefensin (yellow highlight), PADRE sequence (grey highlight), linkers (black, bold, underlined), CTXB (green highlight), S. dublin flagellin (dark yellow highlight), RS09 (pink highlight). LBL epitopes (green font), CTL epitopes (red font), HTL epitopes (blue font).

3.4. Features of the Construct

The physicochemical properties of each construct were evaluated and are summarized in Table 5. All MEV constructs exhibited high stability (instability index < 40), demonstrated excellent thermal stability, as assessed by the aliphatic index, and displayed hydrophilic properties, as indicated by the GRAVY. Predictions from the Protein-Sol and SOLpro servers indicated high solubility of all vaccine constructs. Furthermore, the constructs displayed high antigenicity scores, confirming their non-allergenic and non-toxic nature. The predicted secondary structures of all constructs are shown in Figure 3.
Table 5. Summary of the physicochemical properties of all the vaccine construct.
Figure 3. Secondary structure predictions for all vaccine constructs. PSIPRED was utilized to predict the secondary structures of MEVs: (A) Ecoepvc1, (B) Ecoepvc2, (C) Ecoepvc3, (D) Ecoepvc4. Structures are represented as strand (yellow highlight), helix (pink highlight), and coil (grey line).

3.5. Tertiary Structure Modeling and Structure Refining and Validation

3D modeling was conducted using the i-Tasser online server, and the model with the highest C-score was selected. Subsequently, the modeled structures were refined using GalaxyWEB software. This process involved removing steric clashes, reconstructing side chains, and generating several models. The refinement process considered parameters such as global distance test-high accuracy (GDT-HA), root-mean-square deviation (RMSD), MolProbity, and the Ramachandran favored score. Models with lower RMSD values (between 0 and 1.2 Å), MolProbity, Clash score, and Poor rotamer values and higher GDT-HA and Rama favored values were prioritized for their improved stability and of high quality for further validation (Figure 4A).
Figure 4. Refined tertiary structure of Ecoepvc3. (A) Predicted 3D structure of Ecoepvc3 refined using the Galaxy Web Server. (B) Ramachandran plot of Ecoepvc3 generated by PROCHECK, validating the stereochemical quality of the model.
Validation of the refined vaccine constructs was performed using a Ramachandran plot, which illustrates the percentage of amino acid residues within favored, generously allowed, additionally allowed, and disallowed regions. Post-refinement, all constructs showed a significant improvement in the percentage of residues within favored regions. Ecoepvc3 demonstrated the highest quality, with 88% of residues located in the most favored regions (Figure 4B), followed by Ecoepvc4 with 86.6%, Ecoepvc1 with 84.5%, and Ecoepvc2 with 82.1% (Figure S1). A high-quality protein model typically exhibits approximately 90% of residues in favored regions; therefore, Ecoepvc3, being closest to this benchmark, was selected for further analysis [121].
To further assess the structural integrity of Ecoepvc3, its 3D atomic model was evaluated for compatibility with its amino acid sequence using the Verify 3D and ERRAT tools. The Verify 3D tool assesses the compatibility between the 3D model and its linear sequence, while ERRAT identifies regions of potential error based on non-random atomic interactions [100,122,123]. Results from both validation tools confirmed the high quality of the Ecoepvc3 model; see Figure 5.
Figure 5. Quality analysis and structure validation of Ecoepvc3. (A) Ecoepvc3 exhibited a Z-score of −3.21 using the Pro-SA tool. (B) The overall quality of Ecoepvc3 was assessed as 80% using ERRAT software. (C) Verify3D analysis confirmed good compatibility between the atomic (3D) model and its corresponding amino acid sequence.

3.6. Disulfide Bond Engineering Results

Ecoepvc3 presents several potential disulfide bonds, with nine bonds identified within the favored energy range of <2 and chi3 angles ranging from −87 to + 97 ±30 degrees (Figure 6). These bonds involve pairs: SER11-ALA522, LEU18-GLY515, LEU25-ALA508, LEU109-ASN177, KYS136-SER139, ASN285-ASN290, KYS315-GLU320, SER324-ALA341, and ALA347-LEU353.
Figure 6. Disulfide engineering of Ecoepvc3 construct. Predicted disulfide bonds are indicated by yellow bars.
Predicting and understanding disulfide bonds in Ecoepvc3 is critical because these bonds stabilize protein structures, ensuring proper folding and resistance to degradation. This structural insight is crucial for optimizing vaccine stability and efficacy, which are essential for biotechnological and therapeutic applications [57].

3.7. Glycosylation Site Prediction

Predicting glycosylation sites is important in MEV design because it impacts antigen immunogenicity, stability, and immunomodulatory properties. Identifying potential glycosylation sites ensures optimal antigen presentation and immune recognition, thereby enhancing vaccine efficacy. Avoiding the glycosylation of critical epitopes prevents interference with antigenic presentation and reduces the risk of eliciting undesired immune responses [124]. GlycoPP v1.0 analysis revealed multiple N-linked glycosylation sites primarily located in the adjuvant flagellin head and tail regions, as indicated in Table 6, rather than concentrated in the epitope region. However, O-linked glycosylation sites were predominantly located, with 11 sites predicted within the epitope region, as shown in Table 7. This localization is advantageous because it minimizes potential glycosylation interference with epitope functionality, preserving their immunogenicity and enhancing vaccine specificity.
Table 6. Prediction of N-linked glycosylation sites using GlycoPP v1.0.
Table 7. Prediction of O-linked glycosylation sites using GlycoPP v1.0.

3.8. Molecular Docking of MEV with TLR4

The refined structure of Ecoepvc3 was docked against Toll-like Receptor 4 (TLR4), which is essential for understanding how vaccine components interact with this receptor at the molecular level. As shown in Figure 7, Ecopev3 strongly binds to the epitope regions of TLR4.
Figure 7. Docking results between Ecoepvc3 and TLR4. Cyan represents the vaccine construct Ecoepvc3, while magenta represents TLR4. Panel (A) shows the ribbon view, and panel (B) shows the surface view.

3.9. Predicted Immune Responses Induced by the MEV

Ecopev3 demonstrated an enhanced immune response, particularly after boosting, characterized by elevated concentrations of immunoglobulins IgM and IgG1, with IgG2 levels remaining unaffected (Figure 8). The vaccine also notably induced a substantial increase in T-helper cells, predominantly T-helper 1 (Th1) rather than T-helper 2 (Th2) cells. Despite incorporating PADRE sequences, which are recognized for enhancing Th2 responses, into the vaccine construct at multiple sites, the C-IMMSUM analysis shown in Figure 8 revealed its ineffectiveness in promoting Th2 differentiation.
Figure 8. In silico stimulation of immune response using Ecoepvc3 construct as antigen using C-IMMSUM.

4. Discussion

The primary objective of this study was to identify strategic protein candidates for the development of an E. coli vaccine, focusing on proteins exclusive to pathogenic strains, conserved among HUS-causing variants, and essential for their pathogenicity. Given the challenging treatment landscape presented by the highly adhesive E. coli O104:H4 outbreak strain, our research emphasizes proteins critical for adhesion, attachment, and colonization promotion while also evaluating proteins that may disrupt cellular integrity and homeostasis. Additionally, we prioritized candidates conserved across various strains, including other EHEC strains, particularly the O157:H7 serotype. Careful selection criteria ensured that these candidates were less conserved or absent in three different commensal E. coli strains.
Our investigation identified four pivotal protein candidates—copper resistance protein B (CopB), long polar fimbrial protein (LpfD), putative outer membrane protein Lom (LomP), and hypothetical protein O3K_20405 (Hcp_VI)—all of which play significant roles in the infection process.
Copper resistance protein B (CopB) in E. coli is essential for bacterial adaptation and survival in environments with elevated copper levels, including host tissues during infection. CopB’s role is copper detoxification by facilitating the efflux or sequestration of copper ions, thereby maintaining cellular homeostasis and protecting against copper-induced toxicity. The function of CopB extends to contributing to virulence mechanisms, potentially aiding in the bacterium’s ability to evade host immune responses and persist within the host [125,126,127,128,129]. Targeting CopB in vaccine development could disrupt these adaptive strategies, potentially reducing E. coli virulence and enhancing therapeutic strategies against infections involving copper-rich environments.
Long polar fimbrial protein (LpfD) plays a central role in EHEC pathogenesis by mediating adhesion to intestinal epithelial cells. This protein facilitates the formation of microcolonies and biofilms on the mucosal surface, promoting persistent colonization. Its adhesive function is particularly relevant in O104:H4 and other EHEC strains, such as O157:H7, O26, O111, and O145, where prolonged intestinal attachment correlates with extended Shiga toxin (Stx) shedding, exacerbating disease severity and duration [130,131]. Therefore, targeting LpfD may help prevent early colonization and reduce the clinical impact of EHEC infections [132,133,134]. The high conservation of LpfD across O104:H4 strains, as shown in Table 1, supports its utility as a universal vaccine antigen.
The putative outer membrane protein Lom (LomP) is notably more prevalent in O157:H7 strains than in O104:H4 strains, as highlighted in Table 1. It plays a vital role in maintaining outer membrane integrity and contributes to immune evasion. In pathogenic EHEC, including O104:H4, LomP supports adhesion to intestinal epithelial cells, facilitating microcolony formation and enhancing virulence [135,136]. Given its higher prevalence in O157:H7 strains—commonly associated with severe HUS—LomP is a valuable vaccine target. Its inclusion in multivalent formulations could provide cross-protection against HUS-associated strains by impairing bacterial adherence and colonization.
Finally, the hypothetical protein O3K_20405 (Hcp_VI), despite its uncharacterized function, is highly conserved among multiple EHEC strains, including O104:H4, suggesting an important role in bacterial physiology or virulence. It may be involved in processes such as adhesion, immune evasion, or toxin production [137]. Its conservation and putative involvement in critical pathogenic mechanisms make it an attractive vaccine target. Immune responses directed against Hcp_VI could impair essential bacterial functions, limiting colonization, transmission, and the risk of severe outcomes such as HUS.
Several studies have identified numerous vaccine protein candidates, particularly those focused on EHEC strains, especially E. coli O157:H7 [25,138,139]. However, there has been limited exploration of candidates specifically for E. coli O104:H4. Other studies have introduced universal proteins in pathogenic E. coli but at the expense of being conserved in commensal E. coli, thus preventing their potential as future vaccine candidates [140]. Striking the right balance, where protein candidates are conserved among pathogenic E. coli, but not commensal strains, is challenging. In this study, we aimed to achieve this balance by focusing on HUS-causing E. coli strains, both O157:H7 (EHEC) and O104:H4 (EAHEC), and ensuring divergence from different commensal strains. These four proteins fulfill this dual aim effectively.
Although O104:H4 has not caused large-scale outbreaks since 2011, its hybrid virulence profile (combining features of EAEC and STEC), multidrug resistance, and severe clinical outcomes justify its inclusion as a model strain for vaccine research. The inclusion of both O157:H7 and O104:H4 allows us to examine conserved features across distinct pathogenic lineages, potentially informing broader vaccine strategies.
Notably, past EHEC vaccine efforts have faced significant limitations. A human vaccine developed in Canada targeting O157:H7 was discontinued due to severe local reactions, including injection site abscesses. It was later reformulated for cattle use but demonstrated limited effectiveness in preventing colonization or shedding [141]. Additionally, no human EHEC vaccine has received regulatory approval to date. These challenges underscore the importance of identifying safer, broadly effective targets that can be applied across different EHEC serotypes.
The diversity of EHEC serotypes remains a critical obstacle in vaccine development. Future strategies should consider multivalent or cross-protective designs to provide coverage against a broader range of clinically relevant serotypes. Our selection of targets contributes to this direction by prioritizing antigens with high pathogenic specificity and minimal cross-reactivity with commensal flora.
Despite Stx being the primary cause of HUS and neutralizing Shiga toxin-specific antibodies potentially serving as therapeutic agents [49,50], we did not include them in our vaccine proposal due to the existence of multiple Stx types [142]. Obtaining a universally conserved sequence for Stx is challenging given the diversity of Stx types and their structural variations. Instead, our focus is on identifying stable antigens that can serve as foundational elements for either a single vaccine or a multi-vaccine candidate. The severe effects observed are largely attributed to the super-adhesion properties that enable prolonged bacterial persistence and increased secretion of Stx toxins, exacerbating the condition. Targeting this adhesion mechanism from the onset represents a strategic intervention to mitigate the downstream effects of Stx. Thus, the core aim of our vaccine strategy is to interfere with the colonization process, thereby indirectly attenuating Stx-mediated pathology.
We identified and analyzed key epitopes from selected vaccine candidates to construct a multi-epitope vaccine (MEV) designed to enhance immune activation. Rather than using whole proteins, we focused on assembling the most significant epitopes into a single construct, incorporating an internal adjuvant to further stimulate immune responses. We screened these epitopes based on their strong predicted immunogenicity to ensure effective incorporation in the MEV design. Special emphasis was placed on B-cell epitopes, reflecting the critical role of humoral immunity—particularly IgA—in combating E. coli infections. Of particular importance was the identification of subtype A epitopes within linear B-cell epitopes, which play a vital role in preventing bacterial adhesion to mucosal surfaces and thus blocking the initial steps of infection.
In selecting the HTL, the design incorporated predictions for IL-4 and IL-10 inducers, recognizing their fundamental roles in immune modulation. IL-4 is essential for promoting Th2 responses, which are crucial for defense against extracellular pathogens and enhancing antibody production, thereby reinforcing humoral immunity. Conversely, IL-10 serves as a potent anti-inflammatory cytokine critical for regulating immune responses and preventing excessive inflammation and tissue damage. By enhancing IL-4 and IL-10 production, vaccine constructs aim to achieve a balanced Th1/Th2 immune profile, fostering robust cellular and humoral immune responses while minimizing immunopathology. This integrated approach is supported by research indicating improved vaccine efficacy and safety outcomes through targeted cytokine modulation [143,144]. However, despite the inclusion of predictions and incorporation of PADRE sequences, which are Th inducers, in the Ecoepv3 vaccine construct, Th1 responses predominated over Th2 responses in immune response simulations. The observed Th1 bias may be attributed to all the HTL epitopes being predicted to induce IFN-γ production, which is necessary for promoting cellular immunity, supporting Th1 polarization, and enhancing vaccine efficacy and immunogenicity [145].
We utilized four multi-epitope-based vaccine constructs, each incorporating diverse adjuvants to optimize immune response variability for each candidate. While all candidates exhibited favorable physicochemical characteristics, indicating potential for vaccine development, Ecopev3 was selected for further advancement due to its better structure, as confirmed by Ramachandran plot analysis after refinement with GalaxyWEB. Importantly, docking studies confirmed the robust ability of Ecopev3 to bind and activate TLR4 effectively, highlighting its ability to initiate a potent immune response.
Through analysis using various immunoinformatic tools, Ecoepv3 has shown considerable promise as a vaccine candidate. Its robust potential for future development is underscored by its favorable characteristics identified through these assessments. However, it is important to note that this study currently lacks in vivo data in animal models, which is highly recommended for confirming the predicted efficacy observed in silico. Rigorous in vivo studies will be essential to validate the ability of Ecoepv3 to induce immune responses effectively. Moreover, these studies will provide critical insights into further structural optimization, ensuring its suitability for advancing into preclinical and clinical trials.

5. Conclusions and Limitations

In this study, we introduced novel protein candidates with potential for vaccine development, sourced from the E. coli O104:H4 proteome and conserved within various strains of the O157:H7 serotype, ensuring coverage of some HUS-causing strains. Importantly, these proteins were selected to avoid conservation in commensal strains, thereby preserving the normal flora. They were utilized to design a multiepitope vaccine construct incorporating LBL, CTL, and HTL. This approach suggests that these candidates hold promise for enhancing immune responses.
However, this work is primarily computational and predictive in nature. The absence of experimental validation, particularly in vivo studies, limits the immediate applicability of the findings. Further functional assays and animal model testing are essential to evaluate the safety, immunogenicity, and efficacy of the proposed vaccine candidates.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diseases13080259/s1, Figure S1: Ramachandran plots of the refined tertiary structures of (A) Ecoepvc1, (B) Ecoepvc2, and (C) Ecoepvc4; Table S1: List of all proteins specific to E. coli O104 that do not meet the criteria for being shared with commensal E. coli; Table S2: List of all LBL epitopes predicated from different online tools; Table S3: List of all predicted CTL epitopes; Table S4: List of all predicted HTL epitopes.

Author Contributions

Conceptualization, E.G.Y.; methodology, E.G.Y.; software, E.G.Y.; validation, E.G.Y.; formal analysis, E.G.Y.; investigation, E.G.Y.; resources, E.G.Y.; data curation, E.G.Y.; writing—original draft preparation, E.G.Y.; writing—review and editing, E.G.Y., K.E. and A.H.; visualization, E.G.Y., K.E. and A.H.; supervision, A.H.; project administration, E.G.Y. and K.E.; funding acquisition, E.G.Y. and K.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Beni-Suef University, Egypt, through the University Performance Development Center and the Research Project Fund and Support Office under the 5th Call—BSU-CP5.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All data are presented in this manuscript and provided as Supplementary Information.

Acknowledgments

During the preparation of this manuscript, ChatGPT-4 (online version) was utilized for grammar correction and text refinement.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Mueller, M.; Tainter, C.R. Escherichia coli Infection; StatPearls: Treasure Island, FL, USA, 2024. [Google Scholar]
  2. Centers for Disease Control and Prevention. ivCopyright Page. In CDC Yellow Book 2024: Health Information for International Travel; Nemhauser, J.B., Ed.; Oxford University Press: Oxford, UK, 2023. [Google Scholar]
  3. Robins-Browne, R.M.; Holt, K.E.; Ingle, D.J.; Hocking, D.M.; Yang, J.; Tauschek, M. Are Escherichia coli Pathotypes Still Relevant in the Era of Whole-Genome Sequencing? Front. Cell. Infect. Microbiol. 2016, 6, 141. [Google Scholar] [CrossRef] [PubMed]
  4. Riley, L.W.; Remis, R.S.; Helgerson, S.D.; McGee, H.B.; Wells, J.G.; Davis, B.R.; Hebert, R.J.; Olcott, E.S.; Johnson, L.M.; Hargrett, N.T.; et al. Hemorrhagic colitis associated with a rare Escherichia coli serotype. N. Engl. J. Med. 1983, 308, 681–685. [Google Scholar] [CrossRef] [PubMed]
  5. Wells, J.G.; Davis, B.R.; Wachsmuth, I.K.; Riley, L.W.; Remis, R.S.; Sokolow, R.; Morris, G.K. Laboratory investigation of hemorrhagic colitis outbreaks associated with a rare Escherichia coli serotype. J. Clin. Microbiol. 1983, 18, 512–520. [Google Scholar] [CrossRef] [PubMed]
  6. Ameer, M.A.; Wasey, A.; Salen, P. Escherichia coli (E. coli 0157 H7). In StatPearls; StatPearls Publishing LLC: Treasure Island, FL, USA, 2025. [Google Scholar]
  7. Gambushe, S.M.; Zishiri, O.T.; El Zowalaty, M.E. Review of Escherichia coli O157:H7 Prevalence, Pathogenicity, Heavy Metal and Antimicrobial Resistance, African Perspective. Infect. Drug Resist. 2022, 15, 4645–4673. [Google Scholar] [CrossRef]
  8. Karmali, M.A.; Steele, B.T.; Petric, M.; Lim, C. Sporadic cases of haemolytic-uraemic syndrome associated with faecal cytotoxin and cytotoxin-producing Escherichia coli in stools. Lancet 1983, 1, 619–620. [Google Scholar] [CrossRef]
  9. Michino, H.; Araki, K.; Minami, S.; Takaya, S.; Sakai, N.; Miyazaki, M.; Ono, A.; Yanagawa, H. Massive outbreak of Escherichia coli O157:H7 infection in schoolchildren in Sakai City, Japan, associated with consumption of white radish sprouts. Am. J. Epidemiol. 1999, 150, 787–796. [Google Scholar] [CrossRef]
  10. Grant, J.; Wendelboe, A.M.; Wendel, A.; Jepson, B.; Torres, P.; Smelser, C.; Rolfs, R.T. Spinach-associated Escherichia coli O157:H7 outbreak, Utah and New Mexico, 2006. Emerg. Infect. Dis. 2008, 14, 1633–1636. [Google Scholar] [CrossRef]
  11. Laidler, M.R.; Tourdjman, M.; Buser, G.L.; Hostetler, T.; Repp, K.K.; Leman, R.; Samadpour, M.; Keene, W.E. Escherichia coli O157:H7 infections associated with consumption of locally grown strawberries contaminated by deer. Clin. Infect. Dis. 2013, 57, 1129–1134. [Google Scholar] [CrossRef]
  12. Soderstrom, A.; Osterberg, P.; Lindqvist, A.; Jonsson, B.; Lindberg, A.; Blide Ulander, S.; Welinder-Olsson, C.; Lofdahl, S.; Kaijser, B.; De Jong, B.; et al. A large Escherichia coli O157 outbreak in Sweden associated with locally produced lettuce. Foodborne Pathog. Dis. 2008, 5, 339–349. [Google Scholar] [CrossRef]
  13. Hrudey, S.E.; Payment, P.; Huck, P.M.; Gillham, R.W.; Hrudey, E.J. A fatal waterborne disease epidemic in Walkerton, Ontario: Comparison with other waterborne outbreaks in the developed world. Water Sci. Technol. 2003, 47, 7–14. [Google Scholar] [CrossRef]
  14. Correa-Martinez, C.L.; Leopold, S.R.; Köck, R.; Kossow, A.; Bauwens, A.; Mellmann, A. Enterohemorrhagic E. coli (EHEC): Environmental-Vehicle-Human Interface. In Zoonoses: Infections Affecting Humans and Animals; Sing, A., Ed.; Springer International Publishing: Cham, Switzerland, 2023; pp. 355–372. [Google Scholar] [CrossRef]
  15. Warmate, D.; Onarinde, B.A. Food safety incidents in the red meat industry: A review of foodborne disease outbreaks linked to the consumption of red meat and its products, 1991 to 2021. Int. J. Food Microbiol. 2023, 398, 110240. [Google Scholar] [CrossRef]
  16. Karch, H.; Mellmann, A.; Bielaszewska, M. Epidemiology and pathogenesis of enterohaemorrhagic Escherichia coli. Berl. Munch. Tierarztl. Wochenschr. 2009, 122, 417–424. [Google Scholar]
  17. Joseph, A.; Cointe, A.; Mariani Kurkdjian, P.; Rafat, C.; Hertig, A. Shiga Toxin-Associated Hemolytic Uremic Syndrome: A Narrative Review. Toxins 2020, 12, 67. [Google Scholar] [CrossRef]
  18. Jansen, A.; Kielstein, J.T. The new face of enterohaemorrhagic Escherichia coli infections. Euro Surveill. 2011, 16, 19898. [Google Scholar] [CrossRef]
  19. Frank, C.; Werber, D.; Cramer, J.P.; Askar, M.; Faber, M.; an der Heiden, M.; Bernard, H.; Fruth, A.; Prager, R.; Spode, A.; et al. Epidemic profile of Shiga-toxin-producing Escherichia coli O104:H4 outbreak in Germany. N. Engl. J. Med. 2011, 365, 1771–1780. [Google Scholar] [CrossRef]
  20. Gault, G.; Weill, F.X.; Mariani-Kurkdjian, P.; Jourdan-da Silva, N.; King, L.; Aldabe, B.; Charron, M.; Ong, N.; Castor, C.; Mace, M.; et al. Outbreak of haemolytic uraemic syndrome and bloody diarrhoea due to Escherichia coli O104:H4, south-west France, June 2011. Euro Surveill. 2011, 16, 19905. [Google Scholar] [CrossRef]
  21. Borgatta, B.; Kmet-Lunaček, N.; Rello, J. E. coli O104:H4 outbreak and haemolytic-uraemic syndrome. Med. Intensiv. 2012, 36, 576–583. [Google Scholar] [CrossRef][Green Version]
  22. Buchholz, U.; Bernard, H.; Werber, D.; Böhmer, M.M.; Remschmidt, C.; Wilking, H.; Deleré, Y.; an der Heiden, M.; Adlhoch, C.; Dreesman, J.; et al. German outbreak of Escherichia coli O104:H4 associated with sprouts. N. Engl. J. Med. 2011, 365, 1763–1770. [Google Scholar] [CrossRef]
  23. Manitz, J.; Kneib, T.; Schlather, M.; Helbing, D.; Brockmann, D. Origin Detection During Food-borne Disease Outbreaks—A Case Study of the 2011 EHEC/HUS Outbreak in Germany. PLoS Curr. 2014, 6. [Google Scholar] [CrossRef]
  24. Thomas, G.A.; Paradell Gil, T.; Müller, C.T.; Rogers, H.J.; Berger, C.N. From field to plate: How do bacterial enteric pathogens interact with ready-to-eat fruit and vegetables, causing disease outbreaks? Food Microbiol. 2024, 117, 104389. [Google Scholar] [CrossRef]
  25. Pokharel, P.; Dhakal, S.; Dozois, C.M. The Diversity of Escherichia coli Pathotypes and Vaccination Strategies against This Versatile Bacterial Pathogen. Microorganisms 2023, 11, 344. [Google Scholar] [CrossRef]
  26. Bae, W.K.; Lee, Y.K.; Cho, M.S.; Ma, S.K.; Kim, S.W.; Kim, N.H.; Choi, K.C. A case of hemolytic uremic syndrome caused by Escherichia coli O104:H4. Yonsei Med. J. 2006, 47, 437–439. [Google Scholar] [CrossRef]
  27. Mellmann, A.; Bielaszewska, M.; Köck, R.; Friedrich, A.W.; Fruth, A.; Middendorf, B.; Harmsen, D.; Schmidt, M.A.; Karch, H. Analysis of collection of hemolytic uremic syndrome-associated enterohemorrhagic Escherichia coli. Emerg. Infect. Dis. 2008, 14, 1287–1290. [Google Scholar] [CrossRef]
  28. Jones, G.; Mariani-Kurkdjian, P.; Cointe, A.; Bonacorsi, S.; Lefèvre, S.; Weill, F.-X.; Le Strat, Y. Sporadic Shiga Toxin–Producing Escherichia coli Associated Pediatric Hemolytic Uremic Syndrome, France, 2012–2021. Emerg. Infect. Dis. J. 2023, 29, 2054. [Google Scholar] [CrossRef]
  29. Bahgat, O.T.; Rizk, D.E.; Kenawy, H.I.; Barwa, R. Characterization of non-O157 enterohemorrhagic Escherichia coli isolated from different sources in Egypt. BMC Microbiol. 2024, 24, 488. [Google Scholar] [CrossRef]
  30. Foley, C.; Harvey, E.; Bidol, S.A.; Henderson, T.; Njord, R.; DeSalvo, T.; Haupt, T.; Mba-Jonas, A.; Bailey, C.; Bopp, C.; et al. Outbreak of Escherichia coli O104:H4 infections associated with sprout consumption—Europe and North America, May–July 2011. MMWR Morb. Mortal. Wkly. Rep. 2013, 62, 1029–1031. [Google Scholar]
  31. Aldharman, S.S.; Almutairi, S.M.; Alharbi, A.A.; Alyousef, M.A.; Alzankrany, K.H.; Althagafi, M.K.; Alshalahi, E.E.; Al-Jabr, K.H.; Alghamdi, A.; Jamil, S.F. The Prevalence and Incidence of Hemolytic Uremic Syndrome: A Systematic Review. Cureus 2023, 15, e39347. [Google Scholar] [CrossRef]
  32. Brzuszkiewicz, E.; Thürmer, A.; Schuldes, J.; Leimbach, A.; Liesegang, H.; Meyer, F.D.; Boelter, J.; Petersen, H.; Gottschalk, G.; Daniel, R. Genome sequence analyses of two isolates from the recent Escherichia coli outbreak in Germany reveal the emergence of a new pathotype: Entero-Aggregative-Haemorrhagic Escherichia coli (EAHEC). Arch. Microbiol. 2011, 193, 883–891. [Google Scholar] [CrossRef]
  33. Rohde, H.; Qin, J.; Cui, Y.; Li, D.; Loman, N.J.; Hentschke, M.; Chen, W.; Pu, F.; Peng, Y.; Li, J.; et al. Open-source genomic analysis of Shiga-toxin-producing E. coli O104:H4. N. Engl. J. Med. 2011, 365, 718–724. [Google Scholar] [CrossRef]
  34. Ruggenenti, P.; Remuzzi, G. A German outbreak of haemolytic uraemic syndrome. Lancet 2011, 378, 1057–1058. [Google Scholar] [CrossRef]
  35. Finton, M.D.; Meisal, R.; Porcellato, D.; Brandal, L.T.; Lindstedt, B.-A. Comparative genomics of clinical hybrid Escherichia coli strains in Norway. Int. J. Med. Microbiol. 2025, 318, 151651. [Google Scholar] [CrossRef]
  36. Detzner, J.; Pohlentz, G.; Müthing, J. Enterohemorrhagic Escherichia coli and a Fresh View on Shiga Toxin-Binding Glycosphingolipids of Primary Human Kidney and Colon Epithelial Cells and Their Toxin Susceptibility. Int. J. Mol. Sci. 2022, 23, 6884. [Google Scholar] [CrossRef] [PubMed]
  37. Moriel, D.G.; Rosini, R.; Seib, K.L.; Serino, L.; Pizza, M.; Rappuoli, R. Escherichia coli: Great diversity around a common core. mBio 2012, 3, e00118-12. [Google Scholar] [CrossRef] [PubMed]
  38. Gonyar, L.A.; Smith, R.M.; Giron, J.A.; Zachos, N.C.; Ruiz-Perez, F.; Nataro, J.P. Aggregative Adherence Fimbriae II of Enteroaggregative Escherichia coli Are Required for Adherence and Barrier Disruption during Infection of Human Colonoids. Infect. Immun. 2020, 88, e00176-20. [Google Scholar] [CrossRef] [PubMed]
  39. Eurosurveillance Editorial Team. EFSA publishes scientific report on the public health risk of Shiga-toxin producing Escherichia coli (STEC) in fresh vegetables. Euro Surveill. 2011, 16, 19888. [Google Scholar]
  40. Denamur, E. The 2011 Shiga toxin-producing Escherichia coli O104:H4 German outbreak: A lesson in genomic plasticity. Clin. Microbiol. Infect. 2011, 17, 1124–1125. [Google Scholar] [CrossRef]
  41. Turner, M. German E. coli outbreak caused by previously unknown strain. Nature 2011, 474, 137–138. [Google Scholar] [CrossRef]
  42. Nawrocki Erin, M.; Mosso Hillary, M.; Dudley Edward, G. A Toxic Environment: A Growing Understanding of How Microbial Communities Affect Escherichia coli O157:H7 Shiga Toxin Expression. Appl. Environ. Microbiol. 2020, 86, e00509–e00520. [Google Scholar] [CrossRef]
  43. Liu, Y.; Thaker, H.; Wang, C.; Xu, Z.; Dong, M. Diagnosis and Treatment for Shiga Toxin-Producing Escherichia coli Associated Hemolytic Uremic Syndrome. Toxins 2022, 15, 10. [Google Scholar] [CrossRef]
  44. Kielstein, J.T.; Beutel, G.; Fleig, S.; Steinhoff, J.; Meyer, T.N.; Hafer, C.; Kuhlmann, U.; Bramstedt, J.; Panzer, U.; Vischedyk, M.; et al. Best supportive care and therapeutic plasma exchange with or without eculizumab in Shiga-toxin-producing E. coli O104:H4 induced haemolytic-uraemic syndrome: An analysis of the German STEC-HUS registry. Nephrol. Dial. Transplant. 2012, 27, 3807–3815. [Google Scholar] [CrossRef]
  45. Karpman, D. Management of Shiga toxin-associated Escherichia coli-induced haemolytic uraemic syndrome: Randomized clinical trials are needed. Nephrol. Dial. Transplant. 2012, 27, 3669–3674. [Google Scholar] [CrossRef]
  46. Mengistu, D.Y.; Mengesha, Y. New approaches for severity intervention and rapid diagnosis of enterohemorrhagic Escherichia coli: A review. All Life 2023, 16, 2218582. [Google Scholar] [CrossRef]
  47. Oluwarinde, B.O.; Ajose, D.J.; Abolarinwa, T.O.; Montso, P.K.; Du Preez, I.; Njom, H.A.; Ateba, C.N. Safety Properties of Escherichia coli O157:H7 Specific Bacteriophages: Recent Advances for Food Safety. Foods 2023, 12, 3989. [Google Scholar] [CrossRef] [PubMed]
  48. Islam, M.S.; Stimson, W.H. Production and characterization of monoclonal antibodies with therapeutic potential against Shiga toxin. J. Clin. Lab. Immunol. 1990, 33, 11–16. [Google Scholar] [PubMed]
  49. Skinner, C.; Patfield, S.; Stanker, L.H.; Fratamico, P.; He, X. New high-affinity monoclonal antibodies against Shiga toxin 1 facilitate the detection of hybrid Stx1/Stx2 in vivo. PLoS ONE 2014, 9, e99854. [Google Scholar] [CrossRef]
  50. Henrique, I.M.; Sacerdoti, F.; Ferreira, R.L.; Henrique, C.; Amaral, M.M.; Piazza, R.M.F.; Luz, D. Therapeutic Antibodies Against Shiga Toxins: Trends and Perspectives. Front. Cell. Infect. Microbiol. 2022, 12, 825856. [Google Scholar] [CrossRef]
  51. Kim, J.-S.; Lee, M.-S.; Kim, J.H. Recent Updates on Outbreaks of Shiga Toxin-Producing Escherichia coli and Its Potential Reservoirs. Front. Cell. Infect. Microbiol. 2020, 10, 2020. [Google Scholar] [CrossRef] [PubMed]
  52. Mody, R.K.; Hoekstra, R.M.; Scott, M.K.; Dunn, J.; Smith, K.; Tobin-D’Angelo, M.; Shiferaw, B.; Wymore, K.; Clogher, P.; Palmer, A.; et al. Risk of Hemolytic Uremic Syndrome Related to Treatment of Escherichia coli O157 Infection with Different Antimicrobial Classes. Microorganisms 2021, 9, 1997. [Google Scholar] [CrossRef]
  53. Bowser, S.; Melton-Celsa, A.; Chapartegui-González, I.; Torres, A.G. Further Evaluation of Enterohemorrhagic Escherichia coli Gold Nanoparticle Vaccines Utilizing Citrobacter rodentium as the Model Organism. Vaccines 2024, 12, 508. [Google Scholar] [CrossRef]
  54. Anderson, J.D.t.; Bagamian, K.H.; Muhib, F.; Baral, R.; Laytner, L.A.; Amaya, M.; Wierzba, T.; Rheingans, R. Potential impact and cost-effectiveness of future ETEC and Shigella vaccines in 79 low- and lower middle-income countries. Vaccine X 2019, 2, 100024. [Google Scholar] [CrossRef]
  55. Khalid, K.; Poh, C.L. The Promising Potential of Reverse Vaccinology-Based Next-Generation Vaccine Development over Conventional Vaccines against Antibiotic-Resistant Bacteria. Vaccines 2023, 11, 1264. [Google Scholar] [CrossRef]
  56. Li, Y.; Farhan, M.H.R.; Yang, X.; Guo, Y.; Sui, Y.; Chu, J.; Huang, L.; Cheng, G. A review on the development of bacterial multi-epitope recombinant protein vaccines via reverse vaccinology. Int. J. Biol. Macromol. 2024, 282, 136827. [Google Scholar] [CrossRef]
  57. Alzarea, S.I. Identification and construction of a multi-epitopes vaccine design against Klebsiella aerogenes: Molecular modeling study. Sci. Rep. 2022, 12, 14402. [Google Scholar] [CrossRef]
  58. Dorosti, H.; Eslami, M.; Negahdaripour, M.; Ghoshoon, M.B.; Gholami, A.; Heidari, R.; Dehshahri, A.; Erfani, N.; Nezafat, N.; Ghasemi, Y. Vaccinomics approach for developing multi-epitope peptide pneumococcal vaccine. J. Biomol. Struct. Dyn. 2019, 37, 3524–3535. [Google Scholar] [CrossRef]
  59. Hasan, M.; Ghosh, P.P.; Azim, K.F.; Mukta, S.; Abir, R.A.; Nahar, J.; Hasan Khan, M.M. Reverse vaccinology approach to design a novel multi-epitope subunit vaccine against avian influenza A (H7N9) virus. Microb. Pathog. 2019, 130, 19–37. [Google Scholar] [CrossRef]
  60. Meza, B.; Ascencio, F.; Sierra-Beltran, A.P.; Torres, J.; Angulo, C. A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: An in silico approach. Infect. Genet. Evol. 2017, 49, 309–317. [Google Scholar] [CrossRef] [PubMed]
  61. Yousaf, M.; Ullah, A.; Sarosh, N.; Abbasi, S.W.; Ismail, S.; Bibi, S.; Hasan, M.M.; Albadrani, G.M.; Talaat Nouh, N.A.; Abdulhakim, J.A.; et al. Design of Multi-Epitope Vaccine for Staphylococcus saprophyticus: Pan-Genome and Reverse Vaccinology Approach. Vaccines 2022, 10, 1192. [Google Scholar] [CrossRef] [PubMed]
  62. Ayyagari, V.S.; Venkateswarulu, T.C.; Abraham Peele, K.; Srirama, K. Design of a multi-epitope-based vaccine targeting M-protein of SARS-CoV2: An immunoinformatics approach. J. Biomol. Struct. Dyn. 2022, 40, 2963–2977. [Google Scholar] [CrossRef] [PubMed]
  63. Idrees, M.; Noorani, M.Y.; Altaf, K.U.; Alatawi, E.A.; Aba Alkhayl, F.F.; Allemailem, K.S.; Almatroudi, A.; Ali Khan, M.; Hamayun, M.; Khan, T.; et al. Core-Proteomics-Based Annotation of Antigenic Targets and Reverse-Vaccinology-Assisted Design of Ensemble Immunogen against the Emerging Nosocomial Infection-Causing Bacterium Elizabethkingia meningoseptica. Int. J. Environ. Res. Public Health 2021, 19, 194. [Google Scholar] [CrossRef]
  64. Yang, Y.; Sun, W.; Guo, J.; Zhao, G.; Sun, S.; Yu, H.; Guo, Y.; Li, J.; Jin, X.; Du, L.; et al. In silico design of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations. Hum. Vaccines Immunother. 2015, 11, 795–805. [Google Scholar] [CrossRef]
  65. Zhuang, L.; Ali, A.; Yang, L.; Ye, Z.; Li, L.; Ni, R.; An, Y.; Ali, S.L.; Gong, W. Leveraging computer-aided design and artificial intelligence to develop a next-generation multi-epitope tuberculosis vaccine candidate. Infect. Med. 2024, 3, 100148. [Google Scholar] [CrossRef] [PubMed]
  66. Ghaffar, S.A.; Tahir, H.; Muhammad, S.; Shahid, M.; Naqqash, T.; Faisal, M.; Albekairi, T.H.; Alshammari, A.; Albekairi, N.A.; Manzoor, I. Designing of a multi-epitopes based vaccine against Haemophilius parainfluenzae and its validation through integrated computational approaches. Front. Immunol. 2024, 15, 1380732. [Google Scholar] [CrossRef] [PubMed]
  67. Yu, N.Y.; Wagner, J.R.; Laird, M.R.; Melli, G.; Rey, S.; Lo, R.; Dao, P.; Sahinalp, S.C.; Ester, M.; Foster, L.J.; et al. PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010, 26, 1608–1615. [Google Scholar] [CrossRef] [PubMed]
  68. Doytchinova, I.A.; Flower, D.R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 2007, 8, 4. [Google Scholar] [CrossRef]
  69. Sonnhammer, E.L.; von Heijne, G.; Krogh, A. A hidden Markov model for predicting transmembrane helices in protein sequences. In Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology (ISMB-98), Montréal, QC, Canada, 28 June–1 July 1998; Volume 6, pp. 175–182. [Google Scholar]
  70. Nielsen, H. Predicting Secretory Proteins with SignalP. Methods Mol. Biol. 2017, 1611, 59–73. [Google Scholar] [CrossRef]
  71. Juncker, A.S.; Willenbrock, H.; Von Heijne, G.; Brunak, S.; Nielsen, H.; Krogh, A. Prediction of lipoprotein signal peptides in Gram-negative bacteria. Protein Sci. 2003, 12, 1652–1662. [Google Scholar] [CrossRef]
  72. Saha, S.; Raghava, G.P. Prediction methods for B-cell epitopes. Methods Mol. Biol. 2007, 409, 387–394. [Google Scholar] [CrossRef]
  73. Saha, S.; Raghava, G.P. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 2006, 65, 40–48. [Google Scholar] [CrossRef]
  74. Jespersen, M.C.; Peters, B.; Nielsen, M.; Marcatili, P. BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017, 45, W24–W29. [Google Scholar] [CrossRef]
  75. Saha, S.; Raghava, G.P.S. BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-Chemical Properties. In Artificial Immune Systems; Springer: Berlin/Heidelberg, Germany, 2004. [Google Scholar]
  76. Gupta, S.; Ansari, H.R.; Gautam, A.; Raghava, G.P.S.; Open Source Drug Discovery Consortium. Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biol. Direct 2013, 8, 27. [Google Scholar] [CrossRef]
  77. Dimitrov, I.; Bangov, I.; Flower, D.R.; Doytchinova, I. AllerTOP v.2—A server for in silico prediction of allergens. J. Mol. Model. 2014, 20, 2278. [Google Scholar] [CrossRef] [PubMed]
  78. Gupta, S.; Kapoor, P.; Chaudhary, K.; Gautam, A.; Kumar, R.; Open Source Drug Discovery, C.; Raghava, G.P. In silico approach for predicting toxicity of peptides and proteins. PLoS ONE 2013, 8, e73957. [Google Scholar] [CrossRef] [PubMed]
  79. Nielsen, M.; Lundegaard, C.; Worning, P.; Lauemoller, S.L.; Lamberth, K.; Buus, S.; Brunak, S.; Lund, O. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003, 12, 1007–1017. [Google Scholar] [CrossRef] [PubMed]
  80. Andreatta, M.; Nielsen, M. Gapped sequence alignment using artificial neural networks: Application to the MHC class I system. Bioinformatics 2016, 32, 511–517. [Google Scholar] [CrossRef] [PubMed]
  81. Calis, J.J.; Maybeno, M.; Greenbaum, J.A.; Weiskopf, D.; De Silva, A.D.; Sette, A.; Kesmir, C.; Peters, B. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 2013, 9, e1003266. [Google Scholar] [CrossRef]
  82. Larsen, M.V.; Lundegaard, C.; Lamberth, K.; Buus, S.; Lund, O.; Nielsen, M. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinform. 2007, 8, 424. [Google Scholar] [CrossRef]
  83. Reynisson, B.; Barra, C.; Kaabinejadian, S.; Hildebrand, W.H.; Peters, B.; Nielsen, M. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data. J. Proteome Res. 2020, 19, 2304–2315. [Google Scholar] [CrossRef]
  84. Dhanda, S.K.; Vir, P.; Raghava, G.P. Designing of interferon-gamma inducing MHC class-II binders. Biol. Direct 2013, 8, 30. [Google Scholar] [CrossRef]
  85. Dhanda, S.K.; Gupta, S.; Vir, P.; Raghava, G.P. Prediction of IL4 inducing peptides. Clin. Dev. Immunol. 2013, 2013, 263952. [Google Scholar] [CrossRef]
  86. Nagpal, G.; Usmani, S.S.; Dhanda, S.K.; Kaur, H.; Singh, S.; Sharma, M.; Raghava, G.P. Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci. Rep. 2017, 7, 42851. [Google Scholar] [CrossRef]
  87. Shawan, M.; Sharma, A.R.; Halder, S.K.; Arian, T.A.; Shuvo, M.N.; Sarker, S.R.; Hasan, M.A. Advances in Computational and Bioinformatics Tools and Databases for Designing and Developing a Multi-Epitope-Based Peptide Vaccine. Int. J. Pept. Res. Ther. 2023, 29, 60. [Google Scholar] [CrossRef]
  88. Hasan, M.; Islam, S.; Chakraborty, S.; Mustafa, A.H.; Azim, K.F.; Joy, Z.F.; Hossain, M.N.; Foysal, S.H.; Hasan, M.N. Contriving a chimeric polyvalent vaccine to prevent infections caused by herpes simplex virus (type-1 and type-2): An exploratory immunoinformatic approach. J. Biomol. Struct. Dyn. 2020, 38, 2898–2915. [Google Scholar] [CrossRef]
  89. Chen, H.; Wu, B.; Zhang, T.; Jia, J.; Lu, J.; Chen, Z.; Ni, Z.; Tan, T. Effect of Linker Length and Flexibility on the Clostridium thermocellum Esterase Displayed on Bacillus subtilis Spores. Appl. Biochem. Biotechnol. 2017, 182, 168–180. [Google Scholar] [CrossRef]
  90. Olejnik, J.; Hume, A.J.; Mühlberger, E. Toll-like receptor 4 in acute viral infection: Too much of a good thing. PLoS Pathog. 2018, 14, e1007390. [Google Scholar] [CrossRef]
  91. Gasteiger, E.; Hoogland, C.; Gattiker, A.; Duvaud, S.e.; Wilkins, M.R.; Appel, R.D.; Bairoch, A. Protein Identification and Analysis Tools on the ExPASy Server. In The Proteomics Protocols Handbook; Walker, J.M., Ed.; Humana Press: Totowa, NJ, USA, 2005; pp. 571–607. [Google Scholar] [CrossRef]
  92. Magnan, C.N.; Randall, A.; Baldi, P. SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics 2009, 25, 2200–2207. [Google Scholar] [CrossRef] [PubMed]
  93. Hebditch, M.; Carballo-Amador, M.A.; Charonis, S.; Curtis, R.; Warwicker, J. Protein-Sol: A web tool for predicting protein solubility from sequence. Bioinformatics 2017, 33, 3098–3100. [Google Scholar] [CrossRef] [PubMed]
  94. Buchan, D.W.; Minneci, F.; Nugent, T.C.; Bryson, K.; Jones, D.T. Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res. 2013, 41, W349–W357. [Google Scholar] [CrossRef] [PubMed]
  95. Zhou, X.; Zheng, W.; Li, Y.; Pearce, R.; Zhang, C.; Bell, E.W.; Zhang, G.; Zhang, Y. I-TASSER-MTD: A deep-learning-based platform for multi-domain protein structure and function prediction. Nat. Protoc. 2022, 17, 2326–2353. [Google Scholar] [CrossRef]
  96. Yuan, S.; Chan, H.C.S.; Hu, Z. Using PyMOL as a platform for computational drug design. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2017, 7, e1298. [Google Scholar] [CrossRef]
  97. Ko, J.; Park, H.; Heo, L.; Seok, C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. 2012, 40, W294–W297. [Google Scholar] [CrossRef]
  98. Laskowski, R.A.; Rullmannn, J.A.; MacArthur, M.W.; Kaptein, R.; Thornton, J.M. AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR 1996, 8, 477–486. [Google Scholar] [CrossRef]
  99. Wiederstein, M.; Sippl, M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007, 35, W407–W410. [Google Scholar] [CrossRef] [PubMed]
  100. Colovos, C.; Yeates, T.O. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci. 1993, 2, 1511–1519. [Google Scholar] [CrossRef] [PubMed]
  101. Bowie, J.U.; Luthy, R.; Eisenberg, D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991, 253, 164–170. [Google Scholar] [CrossRef]
  102. Craig, D.B.; Dombkowski, A.A. Disulfide by Design 2.0: A web-based tool for disulfide engineering in proteins. BMC Bioinform. 2013, 14, 346. [Google Scholar] [CrossRef] [PubMed]
  103. Chauhan, J.S.; Bhat, A.H.; Raghava, G.P.S.; Rao, A. GlycoPP: A Webserver for Prediction of N- and O-Glycosites in Prokaryotic Protein Sequences. PLoS ONE 2012, 7, e40155. [Google Scholar] [CrossRef]
  104. Desta, I.T.; Porter, K.A.; Xia, B.; Kozakov, D.; Vajda, S. Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure 2020, 28, 1071–1081.e3. [Google Scholar] [CrossRef]
  105. Vajda, S.; Yueh, C.; Beglov, D.; Bohnuud, T.; Mottarella, S.E.; Xia, B.; Hall, D.R.; Kozakov, D. New additions to the ClusPro server motivated by CAPRI. Proteins 2017, 85, 435–444. [Google Scholar] [CrossRef]
  106. Rapin, N.; Lund, O.; Bernaschi, M.; Castiglione, F. Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System. PLoS ONE 2010, 5, e9862. [Google Scholar] [CrossRef]
  107. Yano, A.; Onozuka, A.; Asahi-Ozaki, Y.; Imai, S.; Hanada, N.; Miwa, Y.; Nisizawa, T. An ingenious design for peptide vaccines. Vaccine 2005, 23, 2322–2326. [Google Scholar] [CrossRef]
  108. Li, X.; Guo, L.; Kong, M.; Su, X.; Yang, D.; Zou, M.; Liu, Y.; Lu, L. Design and Evaluation of a Multi-Epitope Peptide of Human Metapneumovirus. Intervirology 2015, 58, 403–412. [Google Scholar] [CrossRef]
  109. Velders, M.P.; Weijzen, S.; Eiben, G.L.; Elmishad, A.G.; Kloetzel, P.M.; Higgins, T.; Ciccarelli, R.B.; Evans, M.; Man, S.; Smith, L.; et al. Defined flanking spacers and enhanced proteolysis is essential for eradication of established tumors by an epitope string DNA vaccine. J. Immunol. 2001, 166, 5366–5373. [Google Scholar] [CrossRef] [PubMed]
  110. Wang, Q.M.; Sun, S.H.; Hu, Z.L.; Zhou, F.J.; Yin, M.; Xiao, C.J.; Zhang, J.C. Epitope DNA vaccines against tuberculosis: Spacers and ubiquitin modulates cellular immune responses elicited by epitope DNA vaccine. Scand. J. Immunol. 2004, 60, 219–225. [Google Scholar] [CrossRef] [PubMed]
  111. Livingston, B.; Crimi, C.; Newman, M.; Higashimoto, Y.; Appella, E.; Sidney, J.; Sette, A. A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes. J. Immunol. 2002, 168, 5499–5506. [Google Scholar] [CrossRef]
  112. Shey, R.A.; Ghogomu, S.M.; Esoh, K.K.; Nebangwa, N.D.; Shintouo, C.M.; Nongley, N.F.; Asa, B.F.; Ngale, F.N.; Vanhamme, L.; Souopgui, J. In-silico design of a multi-epitope vaccine candidate against onchocerciasis and related filarial diseases. Sci. Rep. 2019, 9, 4409. [Google Scholar] [CrossRef] [PubMed]
  113. Bhatnager, R.; Bhasin, M.; Arora, J.; Dang, A.S. Epitope based peptide vaccine against SARS-COV2: An immune-informatics approach. J. Biomol. Struct. Dyn. 2021, 39, 5690–5705. [Google Scholar] [CrossRef]
  114. Moradkasani, S.; Esmaeili, S.; Asadi Karam, M.R.; Mostafavi, E.; Shahbazi, B.; Salek Farrokhi, A.; Chiani, M.; Badmasti, F. Development of a multi-epitope vaccine from outer membrane proteins and identification of novel drug targets against Francisella tularensis: An In Silico approach. Front. Immunol. 2025, 16, 1479862. [Google Scholar] [CrossRef]
  115. Rastogi, A.; Gautam, S.; Kumar, M. Bioinformatic elucidation of conserved epitopes to design a potential vaccine candidate against existing and emerging SARS-CoV-2 variants of concern. Heliyon 2024, 10, e35129. [Google Scholar] [CrossRef]
  116. Sami, S.A.; Marma, K.K.S.; Mahmud, S.; Khan, M.A.N.; Albogami, S.; El-Shehawi, A.M.; Rakib, A.; Chakraborty, A.; Mohiuddin, M.; Dhama, K.; et al. Designing of a Multi-epitope Vaccine against the Structural Proteins of Marburg Virus Exploiting the Immunoinformatics Approach. ACS Omega 2021, 6, 32043–32071. [Google Scholar] [CrossRef]
  117. Dey, J.; Mahapatra, S.R.; Singh, P.K.; Prabhuswamimath, S.C.; Misra, N.; Suar, M. Designing of multi-epitope peptide vaccine against Acinetobacter baumannii through combined immunoinformatics and protein interaction-based approaches. Immunol. Res. 2023, 71, 639–662. [Google Scholar] [CrossRef]
  118. Akhtar, N.; Joshi, A.; Kaushik, V.; Kumar, M.; Mannan, M.A. In-silico design of a multivalent epitope-based vaccine against Candida auris. Microb. Pathog. 2021, 155, 104879. [Google Scholar] [CrossRef]
  119. Pandey, R.K.; Bhatt, T.K.; Prajapati, V.K. Novel Immunoinformatics Approaches to Design Multi-epitope Subunit Vaccine for Malaria by Investigating Anopheles Salivary Protein. Sci. Rep. 2018, 8, 1125. [Google Scholar] [CrossRef] [PubMed]
  120. Motamedi, M.J.; Amani, J.; Shahsavandi, S.; Salmanian, A.H. In Silico Design of Multimeric HN-F Antigen as a Highly Immunogenic Peptide Vaccine Against Newcastle Disease Virus. Int. J. Pept. Res. Ther. 2014, 20, 179–194. [Google Scholar] [CrossRef]
  121. Morris, A.L.; MacArthur, M.W.; Hutchinson, E.G.; Thornton, J.M. Stereochemical quality of protein structure coordinates. Proteins 1992, 12, 345–364. [Google Scholar] [CrossRef]
  122. Lüthy, R.; Bowie, J.U.; Eisenberg, D. Assessment of protein models with three-dimensional profiles. Nature 1992, 356, 83–85. [Google Scholar] [CrossRef]
  123. Al-Khayyat, M.Z.; Al-Dabbagh, A.G. In silico Prediction and Docking of Tertiary Structure of LuxI, an Inducer Synthase of Vibrio fischeri. Rep. Biochem. Mol. Biol. 2016, 4, 66–75. [Google Scholar]
  124. Taherzadeh, G.; Campbell, M.; Zhou, Y. Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins. Methods Mol. Biol. 2022, 2499, 177–186. [Google Scholar] [CrossRef]
  125. Tottey, S.; Waldron, K.J.; Firbank, S.J.; Reale, B.; Bessant, C.; Sato, K.; Cheek, T.R.; Gray, J.; Banfield, M.J.; Dennison, C.; et al. Protein-folding location can regulate manganese-binding versus copper- or zinc-binding. Nature 2008, 455, 1138–1142. [Google Scholar] [CrossRef]
  126. Franke, S.; Grass, G.; Rensing, C.; Nies, D.H. Molecular analysis of the copper-transporting efflux system CusCFBA of Escherichia coli. J. Bacteriol. 2003, 185, 3804–3812. [Google Scholar] [CrossRef]
  127. Outten, C.E.; O’Halloran, T.V. Femtomolar sensitivity of metalloregulatory proteins controlling zinc homeostasis. Science 2001, 292, 2488–2492. [Google Scholar] [CrossRef]
  128. Hyre, A.; Casanova-Hampton, K.; Subashchandrabose, S. Copper Homeostatic Mechanisms and Their Role in the Virulence of Escherichia coli and Salmonella enterica. EcoSal Plus 2021, 9, eESP00142020. [Google Scholar] [CrossRef]
  129. Virieux-Petit, M.; Hammer-Dedet, F.; Aujoulat, F.; Jumas-Bilak, E.; Romano-Bertrand, S. From Copper Tolerance to Resistance in Pseudomonas aeruginosa towards Patho-Adaptation and Hospital Success. Genes 2022, 13, 301. [Google Scholar] [CrossRef]
  130. Torres, A.G.; Blanco, M.; Valenzuela, P.; Slater, T.M.; Patel, S.D.; Dahbi, G.; López, C.; Barriga, X.F.; Blanco, J.E.; Gomes, T.A.; et al. Genes related to long polar fimbriae of pathogenic Escherichia coli strains as reliable markers to identify virulent isolates. J. Clin. Microbiol. 2009, 47, 2442–2451. [Google Scholar] [CrossRef]
  131. Aas, C.G.; Drabløs, F.; Haugum, K.; Afset, J.E. Comparative Transcriptome Profiling Reveals a Potential Role of Type VI Secretion System and Fimbriae in Virulence of Non-O157 Shiga Toxin-Producing Escherichia coli. Front. Microbiol. 2018, 9, 01416. [Google Scholar] [CrossRef]
  132. Tarr, P.I.; Gordon, C.A.; Chandler, W.L. Shiga-toxin-producing Escherichia coli and haemolytic uraemic syndrome. Lancet 2005, 365, 1073–1086. [Google Scholar] [CrossRef]
  133. Torres, A.G.; Payne, S.M. Haem iron-transport system in enterohaemorrhagic Escherichia coli O157:H7. Mol. Microbiol. 1997, 23, 825–833. [Google Scholar] [CrossRef]
  134. Aleksandrowicz, A.; Kjærup, R.B.; Grzymajło, K.; Martinez, F.G.; Muñoz, J.; Borowska, D.; Sives, S.; Vervelde, L.; Dalgaard, T.S.; Kingsley, R.A.; et al. FdeC expression regulates motility and adhesion of the avian pathogenic Escherichia coli strain IMT5155. Vet. Res. 2024, 55, 70. [Google Scholar] [CrossRef]
  135. Wickham, M.E.; Brown, N.F.; Boyle, E.C.; Coombes, B.K.; Finlay, B.B. Virulence is positively selected by transmission success between mammalian hosts. Curr. Biol. 2007, 17, 783–788. [Google Scholar] [CrossRef]
  136. Pen, G.; Yang, N.; Teng, D.; Hao, Y.; Mao, R.; Wang, J. The Outer Membrane Proteins and Their Synergy Triggered the Protective Effects against Pathogenic Escherichia coli. Microorganisms 2022, 10, 982. [Google Scholar] [CrossRef]
  137. Bidossi, A.; Mulas, L.; Decorosi, F.; Colomba, L.; Ricci, S.; Pozzi, G.; Deutscher, J.; Viti, C.; Oggioni, M.R. A Functional Genomics Approach to Establish the Complement of Carbohydrate Transporters in Streptococcus pneumoniae. PLoS ONE 2012, 7, e33320. [Google Scholar] [CrossRef]
  138. Garcia-Angulo, V.A.; Kalita, A.; Kalita, M.; Lozano, L.; Torres, A.G. Comparative genomics and immunoinformatics approach for the identification of vaccine candidates for enterohemorrhagic Escherichia coli O157:H7. Infect. Immun. 2014, 82, 2016–2026. [Google Scholar] [CrossRef]
  139. Arshadi, N.; Mousavi Gargari, S.L.; Amani, J.; Nazarian, S. Immunogenicity of inactivated Escherichia coli O157:H7 with Stx2B microparticle in mice. Iran. J. Basic Med. Sci. 2022, 25, 1069–1076. [Google Scholar] [CrossRef]
  140. Soltan, M.A.; Behairy, M.Y.; Abdelkader, M.S.; Albogami, S.; Fayad, E.; Eid, R.A.; Darwish, K.M.; Elhady, S.S.; Lotfy, A.M.; Alaa Eldeen, M. In silico Designing of an Epitope-Based Vaccine Against Common E. coli Pathotypes. Front. Med. 2022, 9, 829467. [Google Scholar] [CrossRef] [PubMed]
  141. Van Donkersgoed, J.; Hancock, D.; Rogan, D.; Potter, A.A. Escherichia coli O157:H7 vaccine field trial in 9 feedlots in Alberta and Saskatchewan. Can. Vet. J. 2005, 46, 724–728. [Google Scholar] [PubMed]
  142. Scheutz, F.; Teel, L.D.; Beutin, L.; Piérard, D.; Buvens, G.; Karch, H.; Mellmann, A.; Caprioli, A.; Tozzoli, R.; Morabito, S.; et al. Multicenter evaluation of a sequence-based protocol for subtyping Shiga toxins and standardizing Stx nomenclature. J. Clin. Microbiol. 2012, 50, 2951–2963. [Google Scholar] [CrossRef] [PubMed]
  143. Carlini, V.; Noonan, D.M.; Abdalalem, E.; Goletti, D.; Sansone, C.; Calabrone, L.; Albini, A. The multifaceted nature of IL-10: Regulation, role in immunological homeostasis and its relevance to cancer, COVID-19 and post-COVID conditions. Front. Immunol. 2023, 14, 1161067. [Google Scholar] [CrossRef]
  144. Spellberg, B.; Edwards, J.E., Jr. Type 1/Type 2 Immunity in Infectious Diseases. Clin. Infect. Dis. 2001, 32, 76–102. [Google Scholar] [CrossRef]
  145. Benson, L.N.; Liu, Y.; Deck, K.; Mora, C.; Mu, S. IFN-γ Contributes to the Immune Mechanisms of Hypertension. Kidney360 2022, 3, 2164–2173. [Google Scholar] [CrossRef]
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Association Between Dietary Patterns and Lifestyle Habits with Vascular Inflammatory Responses in Individuals with Hypertension Living in PM2.5-Polluted Areas: A Cross-Sectional Pilot Study in Chiang Mai Province, Thailand
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Establishment and Characterization of Behavioral Changes in the Nuclear Localization Human α-Synuclein Transgenic Mice
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