An In-Silico Investigation to Design a Multi-Epitopes Vaccine against Multi-Drug Resistant Hafnia alvei

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.

Multi-epitopes vaccine design for surface glycoprotein against SARS-CoV-2 using immunoinformatic approach

Background

The recent COVID vaccinations have successfully reduced death and severity but did not stop the transmission of viruses by the emerging SARS-CoV-2 strain. There is a need for better and long-lasting dynamic vaccines for numerous prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of broad-spectrum strains being used to stop infection by reducing the spread rate and re-infection. The spike (S) glycoprotein is one of the proteins expressed commonly in the early phases of SARS-CoV-2 infection. It has been identified as the most immunogenic protein of SARS-CoV-2.

Methods

In this study, advanced bioinformatics techniques have been exploited to design the novel multi-epitope vaccine using conserved S protein portions from widespread strains of SARS-CoV-2 to predict B cell and T cell epitopes. These epitopes were selected based on toxicity, antigenicity score and immunogenicity. Epitope combinations were used to construct the maximum potent multi-epitope construct with potential immunogenic features. EAAAK, AAY, and GPGPG were used as linkers to construct epitopes.

Results

The developed vaccine has shown positive results. After the chimeric vaccine construct was cloned into the PET28a (+) vector for expression screening in Escherichia coli, the potential expression of the construct was identified.

Conclusion

The construct vaccine performed well in computer-based immune response simulation and covered a variety of allelic populations. These computational results are more helpful for further analysis of our contract vaccine, which can finally help control and prevent SARS-CoV-2 infections worldwide.

The Promising Potential of Reverse Vaccinology-Based Next-Generation Vaccine Development over Conventional Vaccines against Antibiotic-Resistant Bacteria

The clinical use of antibiotics has led to the emergence of multidrug-resistant (MDR) bacteria, leading to the current antibiotic resistance crisis. To address this issue, next-generation vaccines are being developed to prevent antimicrobial resistance caused by MDR bacteria. Traditional vaccine platforms, such as inactivated vaccines (IVs) and live attenuated vaccines (LAVs), were effective in preventing bacterial infections. However, they have shown reduced efficacy against emerging antibiotic-resistant bacteria, including MDR M. tuberculosis. Additionally, the large-scale production of LAVs and IVs requires the growth of live pathogenic microorganisms. A more promising approach for the accelerated development of vaccines against antibiotic-resistant bacteria involves the use of in silico immunoinformatics techniques and reverse vaccinology. The bioinformatics approach can identify highly conserved antigenic targets capable of providing broader protection against emerging drug-resistant bacteria. Multi-epitope vaccines, such as recombinant protein-, DNA-, or mRNA-based vaccines, which incorporate several antigenic targets, offer the potential for accelerated development timelines. This review evaluates the potential of next-generation vaccine development based on the reverse vaccinology approach and highlights the development of safe and immunogenic vaccines through relevant examples from successful preclinical and clinical studies.