In silico designing and immunoinformatics analysis of a novel peptide vaccine against metallo-beta-lactamase (VIM and IMP) variants

mRNA vaccine design using the proteome of Theileria annulata through immunoinformatics approaches

Theileriosis exerts a substantial impact on ruminants, resulting in significant economic losses within the animal husbandry sector. The current vaccine, a live attenuated parasite, has several limitations that hinder effective disease control. This study utilized immunoinformatics to prioritize potential vaccine candidates and pointed to the design of a novel mRNA vaccine against Theileria annulata using in silico methods. Nine antigenic proteins were selected using various software, and their epitopes were identified through immunoinformatics tools. These epitopes were assessed for their biological traits and homology. Their presentation by major histocompatibility complex (MHC) cells and other immune cells was analyzed using molecular docking techniques. A multi-epitope protein was then modeled and optimized, followed by structural and stability analyses of the mRNA vaccine construct. Finally, the immune response to the new vaccine was simulated. The identified epitopes were localized within the antigen-binding sites of their respective MHC alleles. The newly formulated vaccine demonstrated stability, exhibited no toxicity, and showed non-allergenic characteristics. It effectively elicited responses from both the humoral and cellular immune systems. The findings suggest that the desired engineered mRNA vaccine paves the way for the development of the deterrence of theileriosis. This potential merits additional exploration through rigorous laboratory experiments and subsequent clinical trials.IMPORTANCEThis study presents a cutting-edge approach in vaccine design against bovine theileriosis, a devastating disease affecting cattle globally. By leveraging immunoinformatics methodologies, a novel mRNA vaccine candidate was tailored using computational analyzes of Theileria annulata proteins. Antigenic protein identification, epitope evaluation, and structural optimization of the multi-epitope mRNA vaccine are pivotal advancements in vaccine development. Using computational modeling tools to predict immune responses enhances the efficiency and accuracy of vaccine design, potentially revolutionizing preventive strategies against bovine theileriosis. This research not only demonstrates the potential of immunoinformatics in vaccine innovation but also sheds light on a promising avenue for combating a significant livestock health concern, offering hope for more effective and targeted veterinary interventions.

A Reverse Vaccinology and Immunoinformatic Approach for the Designing of a Novel mRNA Vaccine Against Stomach Cancer Targeting the Potent Pathogenic Proteins of Helicobacter pylori

Helicobacter pylori infection of the stomach's epithelial cells is a significant risk factor for stomach cancer. Various H pylori proteins (CagA, GGT, NapA, PatA, urease, and VacA) were targeted to design 2 messenger RNA (mRNA) vaccines, V1 and V2, using bioinformatics tools. Physicochemical parameters, secondary and tertiary structure, molecular docking and dynamic simulation, codon optimization, and RNA structure prediction have also been estimated for these developed vaccines. Physicochemical analyses revealed that these developed vaccines are soluble (GRAVY < 0), basic (pI < 7), and stable (aliphatic index < 80). The secondary and tertiary structure of the vaccines demonstrated robustness. The docking with toll-like receptors (TLRs) revealed that the vaccines have a potential affinity for TLR-2 (V1: -1132.3 kJ/mol, V2: -1093.6 kJ/mol) and TLR-4 (V1: -1042.7 kJ/mol, V2: -1201.2 kJ/mol), and molecular dynamics simulations confirmed their dynamic stability. Structural analyses of V1 (-505.96 kcal/mol) and V2 (-634.92 kcal/mol) mRNA vaccines underscored their stability. In addition, the vaccine showed a considerable rise in the counts of B cells and extended activation of both T cells was also observed for the vaccines, suggesting the potential for long-lasting immunity, and offering enhanced protection against H pylori. These findings not only suggest potential long-lasting immunity against H pylori but also offer hope for the future of stomach cancer prevention. Notably, the study emphasizes the need for subsequent animal and human-based studies to confirm these promising results.

Revolutionizing Chikungunya Vaccines: mRNA Breakthroughs With Molecular and Immune Simulations

With the ability to cause massive epidemics that have consequences on millions of individuals globally, the Chikungunya virus (CHIKV) emerges as a severe menace. Developing an effective vaccine is urgent as no effective therapeutics are available for such viral infections. Therefore, we designed a novel mRNA vaccine against CHIKV with a combination of highly antigenic and potential MHC-I, MHC-II, and B-cell epitopes from the structural polyprotein. The vaccine demonstrated well-characterized physicochemical properties, indicating its solubility and potential functional stability within the body (GRAVY score of -0.639). Structural analyses of the vaccine revealed a well-stabilized secondary and tertiary structure (Ramachandran score of 82.8% and a Z-score of -4.17). Docking studies of the vaccine with TLR-2 (-1027.7 KJ/mol) and TLR-4 (-1212.4 KJ/mol) exhibited significant affinity with detailed hydrogen bond interactions. Molecular dynamics simulations highlighted distinct conformational dynamics among the vaccine, "vaccine-TLR-2" and "vaccine-TLR-4" complexes. The vaccine's ability to elicit both innate and adaptive immune responses, including the presence of memory B-cells and T-cells, persistent B-cell immunity for a year, and the activation of TH cells leading to the release of IFN-γ and IL-2, has significant implications for its potential effectiveness. The CHIKV vaccine developed in this study shows promise as a potential candidate for future vaccine production against CHIKV, suggesting its suitability for further clinical advancement, including in vitro and in vivo experiments.

Exploring the antibiotic potential of copper carbonate nanoparticles, wound healing, and glucose-lowering effects in diabetic albino mice

Bio-Nanoscience is an emerging field that integrates nanotechnology with biological systems to revolutionize medicine, agriculture, and environmental sustainability through innovative and targeted solutions. The aim of this study was to synthesize copper carbonate nanoparticles and to investigate their antibacterial, wound healing, and glucose-lowering properties. Nanoparticles (NPs) were Synthesized through chemical reduction method and confirmed by using SEM, XRD, and FTIR. Characterization revealed that the nanoparticles had an average size of 55 ± 16 nm, exhibited a crystalline structure, and were free of impurities. Antibacterial tests demonstrated enhanced inhibition zones for Pseudomonas spp., S. aureus, and other bacterial strains, with the largest zone of inhibition observed at 12 mg/ml, measuring 18.5 ± 1.05 mm for Pseudomonas spp. In wound healing activity in diabetic mice observations revealed a complete wound closure in NPs treated mice by day 14 as compared to the control group (96.10 % wound closure). Nanoparticle administration (oral) also significantly reduced glucose levels in diabetic mice after 15 days in the experimental period, whereas fasting glucose levels reduced from 398.00 ± 6.16 to 116.67 ± 12.47 mg/dl. The docking studies of copper carbonate nanoparticles (NPs) with proteins involved in wound healing, including Antileukoproteinase (-2.7 kcal/mol), Casein (-2.5 kcal/mol), Collagen (-2.9 kcal/mol), Lysozyme (-2.8 kcal/mol), and Phospholipase (-3.9 kcal/mol), revealed significant binding affinities, suggesting potential applications in enhancing wound healing processes. Therefore, the copper carbonate nanoparticles demonstrate strong antibacterial properties and show promising effects on wound healing, along with blood glucose-lowering activity. These findings suggest their potential in biomedical applications, particularly for treating diabetes and bacterial infections.

Immunoinformatic evaluation for the development of a potent multi-epitope vaccine against bacterial vaginosis caused by Gardnerella vaginalis

BACKGROUND

Bacterial vaginosis (BV) is the most common vaginal dysbiosis in fertile women, which is associated with side effects including the risk of premature birth. Gardnerella vaginalis (G. vaginalis) is a facultative anaerobic bacillus known as the main pathogen responsible for BV. In this study, using bioinformatics and immunoinformatics methods, a multi-epitope vaccine with optimal population coverage against BV caused by G. vaginalis was designed.

METHODS

Amino acid sequences of two important virulence factors (Vaginolysin and Sialidase) of G. vaginalis were retrieved from NCBI and UniProt databases. At first, three online servers ABCpred, BCPREDS and LBtope were used to predict linear B-cell epitopes (BCEs) and IEDB server was used for T cells. Then the antigenicity, toxicity, allergenicity were evaluated using bioinformatics tools. After modeling the three-dimensional (3D) structure of the vaccine by Robetta Server, molecular docking and molecular dynamics were performed. Finally, immune simulation and in silico cloning were considered effective for the design of vaccine production strategy.

RESULTS

In total, six epitopes of BCEs, eight epitopes from CD4+ and seven epitopes from CD8+ were selected. The designed multi-epitope vaccine was non-allergenic and non-toxic and showed high levels of antigenicity and immunogenicity. After the 3D structure was predicted, it was refined and validated, which resulted in an optimized model with a Z-score of -7.4. Molecular docking and molecular dynamics simulation of the designed vaccine revealed stable and strong binding interactions. Finally, the results of vaccine immunity simulation showed a significant increase in immunoglobulins, higher levels of IFN-γ and IL-2.

CONCLUSION

According to the findings, the candidate multi-epitope vaccine has stable structural features. It also has the potential to stimulate long-term immunity in the host, but wet-lab validation is needed to justify it.

Computational design of multi-epitope vaccine against Hepatitis C Virus infection using immunoinformatics techniques

Hepatitis C Virus (HCV) is a blood borne pathogen that affects around 200 million individuals worldwide. Immunizations against the Hepatitis C Virus are intended to enhance T-cell responses and have been identified as a crucial component of successful antiviral therapy. Nevertheless, attempts to mediate clinically relevant anti-HCV activity in people have mainly failed, despite the vaccines present satisfactory progress. In this study, we used an array of immunoinformatics approaches to design a multiepitope peptide-based vaccine against HCV by emphasizing 6 conserved epitopes from viral protein NS5B. The potential epitopes were examined for their possible antigenic combination with each other along with GPGPG linkers using structural modeling and epitope-epitope interaction analysis. An adjuvant (β-defensin) was introduced to the N-terminus to increase the immunogenicity of the vaccine construct. Molecular dynamics simulation discloses the most stable structure of the proposed vaccine. The designed vaccine is potentially antigenic in nature and can form stable and significant interaction with both receptors TLR2 and TLR3. The vaccine construct was also subjected to In-Silico cloning which confirmed its expression efficiency in a vector. The findings indicate that the designed multi-epitope vaccine have a great potential for preclinical and clinical research, which is an important step in addressing the problems related to HCV infection.

Designing of an mRNA vaccine against high-risk human papillomavirus targeting the E6 and E7 oncoproteins exploiting immunoinformatics and dynamic simulation

Human papillomavirus 16 and human papillomavirus 18 have been associated with different life-threatening cancers, including cervical, lung, penal, vulval, vaginal, anal, and oropharyngeal cancers, while cervical cancer is the most prominent one. Several research studies have suggested that the oncoproteins E6 and E7 are the leading cause of cancers associated with the human papillomavirus infection. Therefore, we developed two mRNA vaccines (V1 and V2) targeting these oncoproteins. We used several bioinformatics tools to predict helper T lymphocyte, cytotoxic T lymphocyte, and B-cell epitopes derived from the proteins and assessed their antigenicity, allergenicity, and toxicity. Both vaccines were constructed using selected epitopes, linkers, and adjuvants. After that, the vaccines were applied for physicochemical properties, secondary and tertiary structure predictions, and subsequent docking and simulation analyses. Accordingly, vaccine 1 (V1) and vaccine 2 (V2) showed better hydrophilicity with the grand average hydropathicity score of -0.811 and -0.648, respectively. The secondary and tertiary structures of the vaccines were also deemed satisfactory, with high stability indicated by the Ramachandran plot (V1:94.5% and V2:87.1%) and Z scores (V1: -5.15 and V2: -4.1). Docking analysis revealed substantial affinity of the vaccines towards the toll-like receptor-2 (V1: -1159.3, V2: -1246.3) and toll-like receptor-4 (V1: -1109.3, V2: -1244.8) receptors. Molecular dynamic simulation validated structural integrity and indicated varying stability throughout the simulation. Codon optimization showed significant expression of the vaccines (V1:51.88% and V2:51.63%) in E. coli vectors. Furthermore, regarding immune stimulation, the vaccines elicited significant B-cell and T-cell responses, including sustained adaptive and innate immune responses. Finally, thermodynamic predictions indicated stable mRNA structures of the vaccines (V1: -502.60 kcal/mol and V2: -450.90 kcal/mol). The proposed vaccines designed effectively targeting human papillomavirus oncoproteins have demonstrated promising results via robust immune responses, suggesting their suitability for further clinical advancement, including in vitro and in vivo experiments.

Biocompatibility, antimicrobial efficacy, and therapeutic potential of cobalt carbonate nanoparticles in wound healing, sex hormones, and metabolic regulation in diabetic albino mice

Nanotechnology enables the manipulation of materials at the nanoscale, offering innovative solutions in various fields. Nanoparticles, with their small size and unique properties, have significant applications in the biomedical filed. The current study was designed to assess the biological applications of self-synthesized cobalt carbonate (CoCO3) nanoparticles. The crystalline structure and chemical composition of the CoCO3-NPs were confirmed by SEM, XRD, and FTIR techniques. We observed the 16.58 nm size of novelly synthesized CoCO3 NPS. The scanning electron microscope study confirmed a uniform cubic spinel structure. The biocompatibility and antimicrobial activity were checked in an invitro setup. We exposed albino mice to these synthesized NPs to study wound healing and metabolic effects. The results of biocompatibility analysis indicated hemolytic activity in a dose-dependent way, which showed no cytotoxic effect except at a higher concentration. Furthermore, the results showed enhanced wound healing processes in CoCO3-NP-treated albino mice as compared to the control group. CoCO3-NPs have considerable effect on the thyroid hormone and insulin levels in albino mice. The levels of T3, T4, and insulin were increased in a dose-dependent manner. Interactions between CoCO3-NPs and thyroxine and insulin were confirmed through molecular docking. We confirmed the antimicrobial efficiency of the nanoparticles using MIC values and zones of inhibition against Staphylococcus haemolyticus and Staphylococcus aureus. Despite their concentration-dependent biocompatibility concerns, the results are promising, as CoCO3-NPs hold potential for use in medical practice, particularly in advanced wound management and microbe inhibition.

Designing novel multiepitope mRNA vaccine targeting Hendra virus (HeV): An integrative approach utilizing immunoinformatics, reverse vaccinology, and molecular dynamics simulation

Human and animal health is threatened by Hendra virus (HeV), which has few treatments. This in-silico vaccine design study focuses on HeV G (glycoprotein), F (fusion protein), and M (matrix protein). These proteins were computationally assessed for B and T-cell epitopes after considering HeV strain conservation, immunogenicity, and antigenicity. To improve vaccination immunogenicity, these epitopes were selectively ligated into a multiepitope construct. To improve vaccination longevity and immunological response, adjuvants and linkers were ligated. G, F, and M epitopes were used to create an mRNA HeV vaccine. Cytotoxic, helper, and linear B-lymphocytes' epitopes are targeted by this vaccine. The population coverage analysis demonstrates that multi-epitope vaccination covers 91.81 percent of CTL and 98.55 percent of HTL epitopes worldwide. GRAVY evaluated the vaccine's well-characterized physicochemical properties -0.503, indicating solubility and functional stability. Structure analysis showed well-stabilized 2° and 3° structures in the vaccine, with alpha helix, beta sheet, and coil structures (Ramachandran score of 88.5% and Z score of -3.44). There was a strong affinity as shown by docking tests with TLR-4 (central score of -1139.4 KJ/mol) and TLR-2 (center score of -1277.9 KJ/mol). The coupled V-apo, V-TLR2, and V-TLR4 complexes were tested for binding using molecular dynamics simulation where extremely stable complexes were found. The predicted mRNA structures provided significant stability. Codon optimization for Escherichia. coli synthesis allowed the vaccine to attain a GC content of 46.83% and a CAI score of 1.0, which supports its significant expression. Immunological simulations indicated vaccine-induced innate and adaptive immune reactions. Finally, this potential HeV vaccine needs more studies to prove its efficacy and safety.

In silico designing of multi-epitope vaccine against canine parvovirus using reverse vaccinology

Canine parvovirus (CPV-2) is a highly contagious virus affecting dogs worldwide, posing a significant threat. The VP2 protein stands out as the predominant and highly immunogenic structural component of CPV-2. Soon after its emergence, CPV-2 was replaced by variants known as CPV-2a, 2b and 2c, marked by changes in amino acid residue 426 of VP2. Additional amino acid alterations have been identified within VP2, with certain modifications serving as signatures of emerging variants. In Brazil, CPV-2 outbreaks persist with diverse VP2 profiles. Vaccination is the main preventive measure against the virus. However, the emergence of substitutions presents challenges to conventional vaccine methods. Commercial vaccines are formulated with strains that usually do not match those currently circulating in the field. To address this, the study aimed to investigate CPV-2 variants in Brazil, predict epitopes, and design an in silico vaccine tailored to local variants employing reverse vaccinology. The methodology involved data collection, genetic sequence analysis, and amino acid comparison between field strains and vaccines, followed by the prediction of B and T cell epitope regions. The predicted epitopes were evaluated for antigenicity, allergenicity and toxicity. The final vaccine construct consisted of selected epitopes linked to an adjuvant and optimized for expression in Escherichia coli. Structural predictions confirmed the stability and antigenicity of the vaccine, while molecular docking demonstrated interaction with the canine toll-like receptor 4. Molecular dynamics simulations indicated a stable complex formation. In silico immune simulations demonstrated a progressive immune response post-vaccination, including increased antibody production and T-helper cell activity. The multi-epitope vaccine design targeted prevalent CPV-2 variants in Brazil and potentially other regions globally. However, experimental validation is essential to confirm our in silico findings.

Artificial intelligence-driven reverse vaccinology for Neisseria gonorrhoeae vaccine: Prioritizing epitope-based candidates

Neisseria gonorrhoeae is the causative agent of the sexually transmitted disease gonorrhea. The increasing prevalence of this disease worldwide, the rise of antibiotic-resistant strains, and the difficulties in treatment necessitate the development of a vaccine, highlighting the significance of preventative measures to control and eradicate the infection. Currently, there is no widely available vaccine, partly due to the bacterium's ability to evade natural immunity and the limited research investment in gonorrhea compared to other diseases. To identify distinct vaccine candidates, we chose to focus on the uncharacterized, hypothetical proteins (HPs) as our initial approach. Using the in silico method, we first carried out a comprehensive assessment of hypothetical proteins of Neisseria gonorrhoeae, encompassing assessments of physicochemical properties, cellular localization, secretary pathways, transmembrane regions, antigenicity, toxicity, and prediction of B-cell and T-cell epitopes, among other analyses. Detailed analysis of all HPs resulted in the functional annotation of twenty proteins with a great degree of confidence. Further, using the immuno-informatics approach, the prediction pipeline identified one CD8+ restricted T-cell epitope, seven linear B-cell epitopes, and seven conformational B-cell epitopes as putative epitope-based peptide vaccine candidates which certainly require further validation in laboratory settings. The study accentuates the promise of functional annotation and immuno-informatics in the systematic design of epitope-based peptide vaccines targeting Neisseria gonorrhoeae.

A computational approach to developing a multi-epitope vaccine for combating Pseudomonas aeruginosa-induced pneumonia and sepsis

Pseudomonas aeruginosa is a complex nosocomial infectious agent responsible for numerous illnesses, with its growing resistance variations complicating treatment development. Studies have emphasized the importance of virulence factors OprE and OprF in pathogenesis, highlighting their potential as vaccine candidates. In this study, B-cell, MHC-I, and MHC-II epitopes were identified, and molecular linkers were active to join these epitopes with an appropriate adjuvant to construct a vaccine. Computational tools were employed to forecast the tertiary framework, characteristics, and also to confirm the vaccine's composition. The potency was weighed through population coverage analysis and immune simulation. This project aims to create a multi-epitope vaccine to reduce P. aeruginosa-related illness and mortality using immunoinformatics resources. The ultimate complex has been determined to be stable, soluble, antigenic, and non-allergenic upon inspection of its physicochemical and immunological properties. Additionally, the protein exhibited acidic and hydrophilic characteristics. The Ramachandran plot, ProSA-web, ERRAT, and Verify3D were employed to ensure the final model's authenticity once the protein's three-dimensional structure had been established and refined. The vaccine model showed a significant binding score and stability when interacting with MHC receptors. Population coverage analysis indicated a global coverage rate of 83.40%, with the USA having the highest coverage rate, exceeding 90%. Moreover, the vaccine sequence underwent codon optimization before being cloned into the Escherichia coli plasmid vector pET-28a (+) at the EcoRI and EcoRV restriction sites. Our research has developed a vaccine against P. aeruginosa that has strong binding affinity and worldwide coverage, offering an acceptable way to mitigate nosocomial infections.

An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches

Pancreatic ductal adenocarcinoma is the most prevalent pancreatic cancer, which is considered a significant global health concern. Chemotherapy and surgery are the mainstays of current pancreatic cancer treatments; however, a few cases are suitable for surgery, and most of the cases will experience recurrent episodes. Compared to DNA or peptide vaccines, mRNA vaccines for pancreatic cancer have more promise because of their delivery, enhanced immune responses, and lower proneness to mutation. We constructed an mRNA vaccine by analyzing S100 family proteins, which are all major activators of receptors for advanced glycation end products. We applied immunoinformatic approaches, including physicochemical properties analysis, structural prediction and validation, molecular docking study, in silico cloning, and immune simulations. The designed mRNA vaccine was estimated to have a molecular weight of 165023.50 Da and was highly soluble (grand average of hydropathicity of -0.440). In the structural assessment, the vaccine seemed to be a well-stable and functioning protein (Z score of -8.94). Also, the docking analysis suggested that the vaccine had a high affinity for TLR-2 and TLR-4 receptors. Additionally, the molecular mechanics with generalized Born and surface area solvation analysis of the "Vaccine-TLR-2" (-141.07 kcal/mol) and "Vaccine-TLR-4" (-271.72 kcal/mol) complexes also suggests a strong binding affinity for the receptors. Codon optimization also provided a high expression level with a GC content of 47.04% and a codon adaptation index score 1.0. The appearance of memory B-cells and T-cells was also observed over a while, with an increased level of helper T-cells and immunoglobulins (IgM and IgG). Moreover, the minimum free energy of the mRNA vaccine was predicted at -1760.00 kcal/mol, indicating the stability of the vaccine following its entry, transcription, and expression. This hypothetical vaccine offers a groundbreaking tool for future research and therapeutic development of pancreatic cancer.

An immunoinformatics and structural vaccinology approach to design a novel and potent multi-epitope base vaccine targeting Zika virus

Zika virus is an infectious virus, that belongs to Flaviviridae family, which is transferred to humans through mosquito vectors and severely threatens human health; but, apart from available resources, no effective and secure vaccine is present against Zika virus, to prevent such infections. In current study, we employed structural vaccinology approach to design an epitope-based vaccine against Zika virus, which is biocompatible, and secure and might trigger an adaptive and innate immune response by using computational approaches. We first retrieved the protein sequence from National Center for Biotechnology Information (NCBI) database and carried out for BLAST P. After BLAST P, predicted protein sequences were shortlisted and checked for allergic features and antigenic properties. Final sequence of Zika virus, with accession number (APO40588.1) was selected based on high antigenic score and non-allergenicity. Final protein sequence used various computational approaches including antigenicity testing, toxicity evaluation, allergenicity, and conservancy assessment to identify superior B-cell and T-cell epitopes. Two B-cell epitopes, five MHC-six MHC-II epitopes and I were used to construct an immunogenic multi-epitope-based vaccine by using suitable linkers. A 50S ribosomal protein was added at N terminal to improve the immunogenicity of vaccine. In molecular docking, strong interactions were presented between constructed vaccine and Toll-like receptor 9 (- 1100.6 kcal/mol), suggesting their possible relevance in the immunological response to vaccine. The molecular dynamics simulations ensure the dynamic and structural stability of constructed vaccine. The results of C-immune simulation revealed that constructed vaccine activate B and T lymphocytes which induce high level of antibodies and cytokines to combat Zika infection. The constructed vaccine is an effective biomarker with non-sensitization, nontoxicity; nonallergic, good immunogenicity, and antigenicity, however, experimental assays are required to verify the results of present study.

Designing a novel multi-epitopes pan-vaccine against SARS-CoV-2 and seasonal influenza: in silico and immunoinformatics approach

The merger of COVID-19 and seasonal influenza infections is considered a potentially serious threat to public health. These two viral agents can cause extensive and severe lower and upper respiratory tract infections with lung damage with host factors. Today, the development of vaccination has been shown to reduce the risk of hospitalization and mortality from the COVID-19 virus and influenza epidemics. Therefore, this study contributes to an immunoinformatics approach to producing a vaccine that can elicit strong and specific immune responses against COVID-19 and influenza A and B viruses. The NCBI, GISAID, and Uniprot databases were used to retrieve sequences. Linear B cell, Cytotoxic T lymphocyte, and Helper T lymphocyte epitopes were predicted using the online servers. Population coverage of MHC I epitopes worldwide for SARS-CoV-2, Influenza virus H3N2, H3N2, and Yamagata/Victoria were 99.93%, 68.67%, 68.38%, and 85.45%, respectively. Candidate epitopes were linked by GGGGS, GPGPG, and KK linkers. Different epitopes were permutated several times to form different peptides and then screened for antigenicity, allergenicity, and toxicity. The vaccine construct was analyzed for physicochemical properties, conformational B-cell epitopes, interaction with Toll-like receptors, and IFN-gamma-induced. Immune stimulation response of final construct was evaluated using C-IMMSIM. Eventually, the final construct sequence was codon-optimized for Escherichia coli K12 and Homo sapiens to design a multi-epitope vaccine and mRNA vaccine. In conclusion, due to the variable nature of SARS-CoV-2 and influenza proteins, the design of a multi-epitope vaccine can protect against all their standard variants, but laboratory validation is required.Communicated by Ramaswamy H. Sarma.