Development of innovative multi-epitope mRNA vaccine against central nervous system tuberculosis using in silico approaches.

Tuberculosis(TB) of the Central nervous system (CNS) is a rare and highly destructive disease. The emergence of drug resistance has increased treatment difficulty, leaving the Bacillus Calmette-Guérin (BCG) vaccine as the only licensed preventative immunization available. This study focused on identifying the epitopes of PknD (Rv0931c) and Rv0986 from Mycobacterium tuberculosis(Mtb) strain H37Rv using an in silico method. The goal was to develop a therapeutic mRNA vaccine for preventing CNS TB. The vaccine was designed to be non-allergenic, non-toxic, and highly antigenic. Codon optimization was performed to ensure effective translation in the human host. Additionally, the secondary and tertiary structures of the vaccine were predicted, and molecular docking with TLR-4 was carried out. A molecular dynamics simulation confirmed the stability of the complex. The results indicate that the vaccine structure shows effectiveness. Overall, the constructed vaccine exhibits ideal physicochemical properties, immune response, and stability, laying a theoretical foundation for future laboratory experiments.

Structural Basis for Evasion of New SARS-CoV-2 Variants from the Potent Virus-Neutralizing Nanobody Targeting the S-Protein Receptor-Binding Domain.

COVID-19 has caused millions of deaths and many times more infections worldwide, emphasizing the unpreparedness of the global health system in the face of new infections and the key role for vaccines and therapeutics, including virus-neutralizing antibodies, in prevention and containment of the disease. Continuous evolution of the SARS-CoV-2 coronavirus has been causing its new variants to evade the action of the immune system, which highlighted the importance of detailed knowledge of the epitopes of already selected potent virus-neutralizing antibodies. A single-chain antibody ("nanobody") targeting the SARS-CoV-2 receptor-binding domain (RBD), clone P2C5, had exhibited robust virus-neutralizing activity against all SARS-CoV-2 variants and, being a major component of the anti-COVID-19 formulation "GamCoviMab", had successfully passed Phase I of clinical trials. However, after the emergence of the Delta and XBB variants, a decrease in the neutralizing activity of this nanobody was observed. Here we report on the successful crystal structure determination of the RBD:P2C5 complex at 3.1 Å, which revealed the intricate protein-protein interface, sterically occluding full ACE2 receptor binding by the P2C5-neutralized RBD. Moreover, the structure revealed the developed RBD:P2C5 interface centered around residues Leu452 and Phe490, thereby explaining the evasion of the Delta or Omicron XBB, but not Omicron B.1.1.529 variant, as a result of the single L452R or F490S mutations, respectively, from the action of P2C5. The structure obtained is expected to foster nanobody engineering in order to rescue neutralization activity and will facilitate epitope mapping for other neutralizing nanobodies by competition assays.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions.

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Conformational flexibility of HIV-1 envelope glycoproteins modulates transmitted/founder sensitivity to broadly neutralizing antibodies.

HIV-1 envelope glycoproteins (Envs) of most primary HIV-1 strains exist in closed conformation and infrequently sample open states, limiting access to internal epitopes. Thus, immunogen design aims to mimic the closed Env conformation as preferred target for eliciting broadly neutralizing antibodies (bnAbs). Here we identify incompletely closed Env conformations of 6 out of 13 transmitted/founder (T/F) strains that are sensitive to antibodies that recognize internal epitopes typically exposed on open Envs. A 3.6 Å cryo-electron microscopy structure of unliganded, incompletely closed T/F Envs (1059-SOSIP) reveals protomer motion that increased sampling of states with incompletely closed trimer apex. We reconstruct de novo the post-transmission evolutionary pathway of a second T/F. Evolved viruses exhibit increased Env resistance to cold, soluble CD4 and 19b, all of which correlate with closing of the adapted Env trimer. Lastly, we show that the ultra-broad N6 bnAb efficiently recognizes different Env conformations and exhibits improved antiviral breadth against VRC01-resistant Envs isolated during the first-in-humans antibody-mediated-prevention trial.

Designing a Candidate Multi-Epitope Vaccine against Transmissible Gastroenteritis Virus Based on Immunoinformatic and Molecular Dynamics.

Transmissible gastroenteritis virus (TGEV) is an etiological agent of enteric disease that results in high mortality rates in piglets. The economic impact of the virus is considerable, causing significant losses to the pig industry. The development of an efficacious subunit vaccine to provide promising protection against TGEV is of the utmost importance. The viral antigen, spike glycoprotein (S), is widely regarded as one of the most effective antigenic components for vaccine research. In this study, we employed immunoinformatics and molecular dynamics approaches to develop an 'ideal' multi-epitope vaccine. Firstly, the dominant, non-toxic, highly antigenic T (Th, CTL) and B cell epitopes predicted from the TGEV S protein were artificially engineered in tandem to design candidate subunit vaccines. Molecular docking and dynamic simulation results demonstrate that it exhibits robust interactions with toll-like receptor 4 (TLR4). Of particular significance was the finding that the vaccine was capable of triggering an immune response in mammals, as evidenced by the immune simulation results. The humoral aspect is typified by elevated levels of IgG and IgM, whereas the cellular immune aspect is capable of eliciting the robust production of interleukins and cytokines (IFN-γ and IL-2). Furthermore, the adoption of E. coli expression systems for the preparation of vaccines will also result in cost savings. This study offers logical guidelines for the development of a secure and efficacious subunit vaccine against TGEV, in addition to providing a novel theoretical foundation and strategy to prevent associated CoV infections.

Immunoinformatics design and synthesis of a multi-epitope vaccine against Helicobacter pylori based on lipid nanoparticles.

Helicobacter pylori (H. pylori) is responsible for various chronic or acute diseases, such as stomach ulcers, dyspepsia, peptic ulcers, gastroesophageal reflux, gastritis, lymphoma, and stomach cancers. Although specific drugs are available to treat the bacterium's harmful effects, there is an urgent need to develop a preventive or therapeutic vaccine. Therefore, the current study aims to create a multi-epitope vaccine against H. pylori using lipid nanoparticles. Five epitopes from five target proteins of H. pylori, namely, Urease, CagA, HopE, SabA, and BabA, were used. Immunogenicity, MHC (Major Histocompatibility Complex) bonding, allergenicity, toxicity, physicochemical analysis, and global population coverage of the entire epitopes and final construct were carefully examined. The study involved using various bioinformatic web tools to accomplish the following tasks: modeling the three-dimensional structure of a set of epitopes and the final construct and docking them with Toll-Like Receptor 4 (TLR4). In the experimental phase, the final multi-epitope construct was synthesized using the solid phase method, and it was then enclosed in lipid nanoparticles. After synthesizing the construct, its loading, average size distribution, and nanoliposome shape were checked using Nanodrop at 280 nm, dynamic light scattering (DLS), and atomic force microscope (AFM). The designed vaccine has been confirmed to be non-toxic and anti-allergic. It can bind with different MHC alleles at a rate of 99.05%. The construct loading was determined to be about 91%, with an average size of 54 nm. Spherical shapes were also observed in the AFM images. Further laboratory tests are necessary to confirm the safety and immunogenicity of the multi-epitope vaccine.

Predicting monoclonal antibody binding sequences from a sparse sampling of all possible sequences.

Previous work has shown that binding of target proteins to a sparse, unbiased sample of all possible peptide sequences is sufficient to train a machine learning model that can then predict, with statistically high accuracy, target binding to any possible peptide sequence of similar length. Here, highly sequence-specific molecular recognition is explored by measuring binding of 8 monoclonal antibodies (mAbs) with specific linear cognate epitopes to an array containing 121,715 near-random sequences about 10 residues in length. Network models trained on resulting sequence-binding values are used to predict the binding of each mAb to its cognate sequence and to an in silico generated one million random sequences. The model always ranks the binding of the cognate sequence in the top 100 sequences, and for 6 of the 8 mAbs, the cognate sequence ranks in the top ten. Practically, this approach has potential utility in selecting highly specific mAbs for therapeutics or diagnostics. More fundamentally, this demonstrates that very sparse random sampling of a large amino acid sequence spaces is sufficient to generate comprehensive models predictive of highly specific molecular recognition.

Molecular mimicry in multisystem inflammatory syndrome in children.

Multisystem inflammatory syndrome in children (MIS-C) is a severe, post-infectious sequela of SARS-CoV-2 infection1,2, yet the pathophysiological mechanism connecting the infection to the broad inflammatory syndrome remains unknown. Here we leveraged a large set of samples from patients with MIS-C to identify a distinct set of host proteins targeted by patient autoantibodies including a particular autoreactive epitope within SNX8, a protein involved in regulating an antiviral pathway associated with MIS-C pathogenesis. In parallel, we also probed antibody responses from patients with MIS-C to the complete SARS-CoV-2 proteome and found enriched reactivity against a distinct domain of the SARS-CoV-2 nucleocapsid protein. The immunogenic regions of the viral nucleocapsid and host SNX8 proteins bear remarkable sequence similarity. Consequently, we found that many children with anti-SNX8 autoantibodies also have cross-reactive T cells engaging both the SNX8 and the SARS-CoV-2 nucleocapsid protein epitopes. Together, these findings suggest that patients with MIS-C develop a characteristic immune response to the SARS-CoV-2 nucleocapsid protein that is associated with cross-reactivity to the self-protein SNX8, demonstrating a mechanistic link between the infection and the inflammatory syndrome, with implications for better understanding a range of post-infectious autoinflammatory diseases.

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.

Disulfide-stabilized trimeric hemagglutinin ectodomains provide enhanced heterologous influenza protection.

Influenza virus infection poses a continual menace to public health. Here, we developed soluble trimeric HA ectodomain vaccines by establishing interprotomer disulfide bonds in the stem region, which effectively preserve the native antigenicity of stem epitopes. The stable trimeric H1 ectodomain proteins exhibited higher thermal stabilities in comparison with unmodified HAs and showed strong binding activities towards a panel of anti-stem cross-reactive antibodies that recognize either interprotomer or intraprotomer epitopes. Negative stain transmission electron microscopy (TEM) analysis revealed the stable trimer architecture of the interprotomer disulfide-stapled WA11#5, NC99#2, and FLD#1 proteins as well as the irregular aggregation of unmodified HA molecules. Immunizations of mice with those trimeric HA ectodomain vaccines formulated with incomplete Freund's adjuvant elicited significantly more potent cross-neutralizing antibody responses and offered broader immuno-protection against lethal infections with heterologous influenza strains compared to unmodified HA proteins. Additionally, the findings of our study indicate that elevated levels of HA stem-specific antibody responses correlate with strengthened cross-protections. Our design strategy has proven effective in trimerizing HA ectodomains derived from both influenza A and B viruses, thereby providing a valuable reference for designing future influenza HA immunogens.

Structure of anellovirus-like particles reveal a mechanism for immune evasion.

Anelloviruses are nonpathogenic viruses that comprise a major portion of the human virome. Despite being ubiquitous in the human population, anelloviruses (ANVs) remain poorly understood. Basic features of the virus, such as the identity of its capsid protein and the structure of the viral particle, have been unclear until now. Here, we use cryogenic electron microscopy to describe the first structure of an ANV-like particle. The particle, formed by 60 jelly roll domain-containing ANV capsid proteins, forms an icosahedral particle core from which spike domains extend to form a salient part of the particle surface. The spike domains come together around the 5-fold symmetry axis to form crown-like features. The base of the spike domain, the P1 subdomain, shares some sequence conservation between ANV strains while a hypervariable region, forming the P2 subdomain, is at the spike domain apex. We propose that this structure renders the particle less susceptible to antibody neutralization by hiding vulnerable conserved domains while exposing highly diverse epitopes as immunological decoys, thereby contributing to the immune evasion properties of anelloviruses. These results shed light on the structure of anelloviruses and provide a framework to understand their interactions with the immune system.

Development of novel humanized VHH synthetic libraries based on physicochemical analyses.

Due to the high affinity and specificity of antibodies toward antigens, various antibody-based applications have been developed. Recently, variable antigen-binding domains of heavy-chain antibodies (VHH) have become an attractive alternative to conventional fragment antibodies due to their unique molecular characteristics. As an antibody-generating strategy, synthetic VHH libraries (including humanized VHH libraries) have been developed using distinct strategies to constrain the diversity of amino acid sequences. In this study, we designed and constructed several novel synthetic humanized VHH libraries based on biophysical analyses conducted using the complementarity determining region-grafting method and comprehensive sequence analyses of VHHs deposited in the protein data bank. We obtained VHHs from the libraries, and hit clones exhibited considerable thermal stability. We also found that VHHs from distinct libraries tended to have different epitopes. Based on our results, we propose a strategy for generating humanized VHHs with distinct epitopes toward various antigens by utilizing our library combinations.

Oriented surface imprinting of epitopes anchored on silica nanoparticles containing quantum dots by thiol-disulfide exchange reactions for the enhanced fluorescence detection of proteins.

As artificial receptors for protein recognition, epitope-imprinted polymers combined with fluorescence sensing based on quantum dots (QDs) can be potentially used for biological analysis and disease diagnosis. However, the usual way for fabrication of QD sensors through unoriented epitope imprinting is confronted with the problems of disordered imprinting sites and low template utilization. In this context, a facile and efficient oriented epitope surface imprinting was put forward based on immobilization of the epitope templates via thiol-disulfide exchange reactions. With N-succinimidyl 3-(2-pyridyldithio)-propionate (SPDP) as a heterobifunctional reagent, cysteine-modified epitopes of cytochrome c were anchored on the surface of pyridyl disulfide functionalized silica nanoparticles sandwiching CdTe QDs. After surface imprinting via a sol-gel process, the epitope templates were removed from the surface-imprinted layers simply by reduction of the thiol-disulfide, affording oriented epitope-imprinted sites. By this method, the amount of epitope templates was only 1/20 of traditionally unoriented epitopes. The resulting sensors demonstrated significantly enhanced imprinting performance and high sensitivity, with the imprinting factor increasing from 2.6 to 3.9, and the limit of detection being 91 nM. Such epitope-oriented surface-imprinted method may offer a new design strategy for the construction of high-affinity protein recognition nanomaterials with fluorescence sensing.

Immunoinformatic approach for multi-epitope vaccine design against Staphylococcus aureus based on hemolysin proteins.

Staphylococcus aureus is a common bacterium that causes a variety of infections in humans. This microorganism produces several virulence factors, including hemolysins, which contribute to its disease-causing ability. The treatment of S. aureus infections typically involves the use of antibiotics. However, the emergence of antibiotic-resistant strains has become a major concern. Therefore, vaccination against S. aureus has gained attention as an alternative approach. Vaccination has the advantage of stimulating the immune system to produce specific antibodies that can neutralize bacteria and prevent infection. However, developing an effective vaccine against S. aureus has proven to be challenging. This study aimed to use in silico methods to design a multi-epitope vaccine against S. aureus infection based on hemolysin proteins. The designed vaccine contained four B-cell epitopes, four CTL epitopes, and four HTL epitopes, as well as the ribosomal protein L7/L12 and pan-HLA DR-binding epitope, included as adjuvants. Furthermore, the vaccine was non-allergenic and non-toxic with the potential to stimulate the TLR2-, TLR-4, and TLR-6 receptors. The predicted vaccine exhibited a high degree of antigenicity and stability, suggesting potential for further development as a viable vaccine candidate. The population coverage of the vaccine was 94.4 %, indicating potential widespread protection against S. aureus. Overall, these findings provide valuable insights into the design of an effective multi-epitope vaccine against S. aureus infection and pave the way for future experimental validations.

Characterization, Epitope Confirmation, and Cross-Reactivity Analysis of Parvalbumin from Lateolabrax maculatus by Multiomics Technologies.

Spotted seabass (Lateolabrax maculatus) is the second largest maricultural fish species in China and is the main trigger of food-related allergic reactions. Nevertheless, studies on the allergens of L. maculatus are limited. This study aimed to characterize pan-allergen parvalbumin from L. maculatus. Two proteins of about 11 kDa were purified and confirmed as parvalbumins by mass spectrometry. The IgG- and IgE-binding activities were evaluated through an immunoblotting assay. The molecular characteristics of ß-parvalbumin were investigated by combining proteomics, genomics, and immunoinformatics approaches. The results indicated that ß-parvalbumin consists of 109 amino acids with a molecular weight of 11.5 kDa and is the major allergen displaying strong IgE-binding capacity. In silico analysis and a dot blotting assay confirmed seven linear B cell epitopes distributed mainly on α-helixes and the calcium-binding loops. In addition, the cross-reactivity among 26 commonly consumed fish species was analyzed. The in-house generated anti-L. maculatus parvalbumin polyclonal antibody recognized 100% of the 26 fish species, demonstrating cross-reactivity and better binding capacity than the anticod parvalbumin antibody. Together, this study provides an efficient protocol to characterize allergens with multiomics methods and supports parvalbumin from L. maculatus as a candidate for fish allergen determination and allergy diagnosis.

A novel mRNA multi-epitope vaccine of Acinetobacter baumannii based on multi-target protein design in immunoinformatic approach.

Acinetobacter baumannii is a gram-negative bacillus prevalent in nature, capable of thriving under various environmental conditions. As an opportunistic pathogen, it frequently causes nosocomial infections such as urinary tract infections, bacteremia, and pneumonia, contributing to increased morbidity and mortality in clinical settings. Consequently, developing novel vaccines against Acinetobacter baumannii is of utmost importance. In our study, we identified 10 highly conserved antigenic proteins from the NCBI and UniProt databases for epitope mapping. We subsequently screened and selected 8 CTL, HTL, and LBL epitopes, integrating them into three distinct vaccines constructed with adjuvants. Following comprehensive evaluations of immunological and physicochemical parameters, we conducted molecular docking and molecular dynamics simulations to assess the efficacy and stability of these vaccines. Our findings indicate that all three multi-epitope mRNA vaccines designed against Acinetobacter baumannii are promising; however, further animal studies are required to confirm their reliability and effectiveness.

Multi-epitope peptide vaccines targeting dengue virus serotype 2 created via immunoinformatic analysis.

The Middle East has witnessed a greater spread of infectious Dengue viruses, with serotype 2 (DENV-2) being the most prevalent form. Through this work, multi-epitope peptide vaccines against DENV-2 that target E and nonstructural (NS1) proteins were generated through an immunoinformatic approach. MHC class I and II and LBL epitopes among NS1 and envelope E proteins sequences were predicted and their antigenicity, toxicity, and allergenicity were investigated. Studies of the population coverage denoted the high prevalence of NS1 and envelope-E epitopes among different countries where DENV-2 endemic. Further, both the CTL and HTL epitopes retrieved from NS1 epitopes exhibited high conservancies' percentages with other DENV serotypes (1, 3, and 4). Three vaccine constructs were created and the expected immune responses for the constructs were estimated using C-IMMSIM and HADDOCK (against TLR 2,3,4,5, and 7). Molecular dynamics simulation for vaccine construct 2 with TLR4 denoted high binding affinity and stability of the construct with the receptor which might foretell favorable in vivo interaction and immune responses.

Predicting immune response targets in orthoflaviviruses through sequence homology and computational analysis.

CONTEXT: Flaviviruses cause severe encephalitic or hemorrhagic diseases in humans. Its members, Kyasanur forest disease virus (KFDV) and Alkhumra hemorrhagic fever virus (ALKV), cause hemorrhagic fever and are prevalent in India and Saudi Arabia, respectively, while the tick-borne encephalitis virus (TBEV) causes a dangerous encephalitic infection in Europe and Asia. However, little information is available about the targets of immune responses for these deadly viruses. Here, we predict potential antigenic peptide epitopes of viral envelope protein for inducing a cell-mediated and humoral immune response. METHODS: Using the Immune Epitope Database and Analysis Resource (IEDB-AR), we identified 13 MHC-I and two MHC-II dominant conserved epitopes in KFDV and ALKV and six MHC-I and three MHC-II epitopes in TBEV envelope proteins. Parallelly, we also predicted B-cell linear and discontinuous envelope protein epitopes for these viruses. Interestingly, the epitopes are conserved in all three viral envelope proteins. Further, the discontinuous epitopes are structurally compared with the available DENV, ZIKV, WNV, TBEV, and LIV envelope protein antibody structures. Overall structural comparison analyses highlight (i) lateral ridge epitope in the ED-III domain of E protein, and (ii) envelope dimer epitope (EDE) could be targeted for developing potent vaccine candidates as well as therapeutic antibody production. Moreover, existing structural and biochemical functions of the same epitopes in homologous viruses are predicted to have a reduced antibody-dependent enhancement (ADE) effect on flaviviral infection.

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.

Differential epitope prediction across diverse circulating variants of SARS-COV-2 in Brazil.

COVID-19, caused by the SARS-COV-2 virus, induces numerous immunological reactions linked to the severity of the clinical condition of those infected. The surface Spike protein (S protein) present in Sars-CoV-2 is responsible for the infection of host cells. This protein presents a high rate of mutations, which can increase virus transmissibility, infectivity, and immune evasion. Therefore, we propose to evaluate, using immunoinformatic techniques, the predicted epitopes for the S protein of seven variants of Sars-CoV-2. MHC class I and II epitopes were predicted and further assessed for their immunogenicity, interferon-gamma (IFN-γ) inducing capacity, and antigenicity. For B cells, linear and structural epitopes were predicted. For class I MHC epitopes, 40 epitopes were found for the clades of Wuhan, Clade 2, Clade 3, and 20AEU.1, Gamma, and Delta, in addition to 38 epitopes for Alpha and 44 for Omicron. For MHC II, there were differentially predicted epitopes for all variants and eight equally predicted epitopes. These were evaluated for differences in the MHC II alleles to which they would bind. Regarding B cell epitopes, 16 were found in the Wuhan variant, 14 in 22AEU.1 and in Clade 3, 15 in Clade 2, 11 in Alpha and Delta, 13 in Gamma, and 9 in Omicron. When compared, there was a reduction in the number of predicted epitopes concerning the Spike protein, mainly in the Delta and Omicron variants. These findings corroborate the need for updates seen today in bivalent mRNA vaccines against COVID-19 to promote a targeted immune response to the main circulating variant, Omicron, leading to more robust protection against this virus and avoiding cases of reinfection. When analyzing the specific epitopes for the RBD region of the spike protein, the Omicron variant did not present a B lymphocyte epitope from position 390, whereas the epitope at position 493 for MHC was predicted only for the Alpha, Gamma, and Omicron variants.