In silico reverse vaccinology designs mRNA vaccine candidate that blocks Shiga toxin-producing E. coli
Background
Escherichia coli is mostly harmless in the gut, but some strains cause severe food poisoning with deadly toxins. Shiga toxin-producing E. coli (STEC) causes hemorrhagic colitis and hemolytic uremic syndrome (HUS). Recently, the surge in multidrug-resistant and hypervirulent strains has raised serious concerns for public health. E. coli has a high rate of gene exchange, making it difficult to treat with existing antibiotics alone, and antibiotic administration can promote toxin release, worsening symptoms. Therefore, the development of a next-generation vaccine to prevent infection is urgently needed. Conventional methods require a great deal of time and resources to culture and attenuate pathogens, but reverse vaccinology, which uses computer simulations, has emerged as an alternative. This technology designs optimal protein fragments that induce an immune response based on genomic data.
Key Findings
The researchers used computer simulations to design a multiepitope mRNA vaccine candidate that targets Shiga toxin 1 (Stx1), a key factor in STEC. They used an immunogenicity prediction algorithm to precisely identify sites that stimulate immune cells.
The vaccine design focuses on simultaneously activating immune cells in the body. The researchers selected two cytotoxic T-lymphocyte (CTL) epitopes, three helper T-lymphocyte (HTL) epitopes, and two linear B-lymphocyte (LBL) epitopes. Strict filtering criteria were applied to exclude potential allergens and toxins and maximize antigenicity. The selected epitopes are linked with EAAK, AAY, GPGPG, and KK linkers. The adjuvant PefE protein was fused to amplify the immune response, creating a final multiepitope structure consisting of a total of 184 amino acid residues.
The predicted molecular weight of the vaccine candidate is 19969.45 Da, and it recorded a high score of 0.9278 in the VaxiJen v.2.0 antigenicity analysis tool. The isoelectric point is 10.31, indicating stability in a basic environment. The three-dimensional structure analysis revealed an alpha-helix content of 21.74%.
The binding force with human Toll-like receptor 2 (TLR2) and Toll-like receptor 4 (TLR4) was measured, showing weighted energy scores of -1096.2 kcal/mol and -1110.5 kcal/mol, respectively. This indicates that numerous hydrogen bonds and salt-bridge networks strongly interact with the receptor. Immune simulation results showed that after vaccine administration, there was a strong clonal expansion of B cells, T cell activation, and increased secretion of cytokines (IFN-γ, IL-10).
The codon adaptation index (CAI), which determines translation efficiency, is 0.87. The minimum free energy (MFE) of the mRNA secondary structure is -204.10 kcal/mol, which supports stable synthesis in cells.
Significance and Implications
This study demonstrates that vaccine candidates can be derived from genomic data and algorithms without directly culturing strains in the laboratory. Simulation-based design provides a basis for rapidly developing countermeasures in pandemic situations or when new variants emerge. It is also expected to be widely applicable to the development of vaccines for various multidrug-resistant bacteria, including E. coli.
The results of this study are based on a predictive model using computer simulations, which has limitations. It is uncertain whether the numerical values and binding forces in the algorithm will be the same in the actual in vivo environment. Due to the nature of mRNA vaccines, there is also a risk that stability may change or unexpected immune hypersensitivity reactions may occur during the lipid nanoparticle (LNP) formulation process. In vitro expression tests and in vivo efficacy and toxicity tests in animals are essential for commercialization. Further research is needed to prove actual protective efficacy before clinical trials can begin.
The adaptable nature of Escherichia coli, shifting from benign to virulent forms, poses a major healthcare challenge due to rising multidrug resistance and hypervirulence. In this study, using an in silico reverse vaccinology approach, we designed a multiepitope mRNA vaccine candidate targeting the Shiga toxin 1 (Stx1) of Shiga toxin-producing E. coli (STEC). Two cytotoxic T-Lymphocyte (CTL), three Helper T-Lymphocyte (HTL), and two Linear B-Lymphocyte (LBL) epitopes were identified and selected through stringent computational filtering based on high antigenicity, non-allergenicity, and non-toxicity. These epitopes were joined using optimized linkers (EAAK, AAY, GPGPG, KK) and the adjuvant PefE to form a stable 184-residue multi-epitope construct. Physicochemical profiling predicted a molecular weight of 19969.45 Da, an antigenicity score of 0.9278 (VaxiJen v.2.0), a basic isoelectric point (pI) of 10.31, and structural stability. Structural analysis revealed 21.74% alpha-helical content. Molecular docking exhibited strong binding with human Toll-like receptor 2 TLR2 and TLR4, with weighted energy scores of - 1096.2 and - 1110.5 kcal/mol, respectively, mediated by extensive hydrogen-bonding and salt-bridge networks. In silico immune simulation projected a strong clonal expansion of B-cells, active T-helper and cytotoxic T-cells, and higher cytokine production (IFN-γ, IL-10). Codon Adaptation Index (CAI) of 0.87 and a stable mRNA secondary structure were predicted with a minimum free energy (MFE) of - 204.10 kcal/mol. While these computational findings provide immunogenic potential, they represent a predictive hypothesis. Subsequent in vitro synthesis and in vivo testing in animal models are essential to validate its safety, immunogenicity, and protective efficacy.
This study presents a concrete scenario that can change the treatment paradigm for multidrug-resistant foodborne E. coli infections. In the event of a hypervirulent E. coli infection in a mass catering facility or livestock farm, it would be useful to proactively secure immunity with this vaccine in infected patients, rather than simply alleviating symptoms or prescribing antibiotics that may promote toxin release, thereby preventing progression to severe HUS. Industrially, in silico design can drastically reduce the development time from years to months, from actual vaccine synthesis to animal testing. Multiepitope design is expected to greatly contribute to simplifying vaccine production processes, increasing purification efficiency, and reducing manufacturing costs. This will establish it as a useful public health solution for foodborne disease prevention programs in developing countries with poor healthcare infrastructure.