Towards personalized vaccines.

The emergence of vaccinomics and system vaccinology represents a transformative shift in immunization strategies, advocating for personalized vaccines tailored to individual genetic and immunological profiles. Integrating insights from genomics, transcriptomics, proteomics, and immunology, personalized vaccines offer the promise of enhanced efficacy and safety, revolutionizing the field of vaccinology. However, the development of personalized vaccines presents multifaceted challenges, including technical, ethical, economic, and regulatory considerations. Addressing these challenges is essential to ensure equitable access and safety of personalized vaccination strategies. Despite these hurdles, the potential of personalized vaccines to optimize responses and mitigate disease burden underscores the significance of ongoing research and collaboration in advancing precision medicine in immunization.

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.

Transforming vaccinology.

The COVID-19 pandemic placed the field of vaccinology squarely at the center of global consciousness, emphasizing the vital role of vaccines as transformative public health tools. The impact of vaccines was recently acknowledged by the award of the 2023 Nobel Prize in Physiology or Medicine to Katalin Kariko and Drew Weissman for their seminal contributions to the development of mRNA vaccines. Here, we provide a historic perspective on the key innovations that led to the development of some 27 licensed vaccines over the past two centuries and recent advances that promise to transform vaccines in the future. Technological revolutions such as reverse vaccinology, synthetic biology, and structure-based design transformed decades of vaccine failures into successful vaccines against meningococcus B and respiratory syncytial virus (RSV). Likewise, the speed and flexibility of mRNA vaccines profoundly altered vaccine development, and the advancement of novel adjuvants promises to revolutionize our ability to tune immunity. Here, we highlight exciting new advances in the field of systems immunology that are transforming our mechanistic understanding of the human immune response to vaccines and how to predict and manipulate them. Additionally, we discuss major immunological challenges such as learning how to stimulate durable protective immune response in humans.

Design of multivalent-epitope vaccine models directed toward the world's population against HIV-Gag polyprotein: Reverse vaccinology and immunoinformatics.

Significant progress has been made in HIV-1 research; however, researchers have not yet achieved the objective of eradicating HIV-1 infection. Accordingly, in this study, eucaryotic and procaryotic in silico vaccines were developed for HIV-Gag polyproteins from 100 major HIV subtypes and CRFs using immunoinformatic techniques to simulate immune responses in mice and humans. The epitopes located in the conserved domains of the Gag polyprotein were evaluated for allergenicity, antigenicity, immunogenicity, toxicity, homology, topology, and IFN-γ induction. Adjuvants, linkers, CTLs, HTLs, and BCL epitopes were incorporated into the vaccine models. Strong binding affinities were detected between HLA/MHC alleles, TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9, and vaccine models. Immunological simulation showed that innate and adaptive immune cells elicited active and consistent responses. The human vaccine model was matched with approximately 93.91% of the human population. The strong binding of the vaccine to MHC/HLA and TLR molecules was confirmed through molecular dynamic stimulation. Codon optimization ensured the successful translation of the designed constructs into human cells and E. coli hosts. We believe that the HIV-1 Gag vaccine formulated in our research can reduce the challenges faced in developing an HIV-1 vaccine. Nevertheless, experimental verification is necessary to confirm the effectiveness of these vaccines in these models.

Advanced vaccinomic, immunoinformatic, and molecular modeling strategies for designing Multi- epitope vaccines against the Enterobacter cloacae complex.

The increasing and ongoing issue of antibiotic resistance in bacteria is of huge concern globally, mainly to healthcare facilities. It is now crucial to develop a vaccine for therapeutic and preventive purposes against the bacterial species causing hospital-based infections. Among the many antibiotic- resistant bacterial pathogens, the Enterobacter cloacae complex (ECC) including six species, E. Colcae, E. absuriae, E. kobie, E. hormaechei, E. ludwigii, and E. nimipressuralis, are dangerous to public health and may worsen the situation. Vaccination plays a vital role in the prevention of infections and infectious diseases. This research highlighted the construction and design of a multi-epitope vaccine for the E. cloacae complex by retrieving their complete sequenced proteome. The retrieved proteome was assessed to opt for potential vaccine candidates using immunoinformatic tools. Both B and T-cell epitopes were predicted in order to create both humoral and cellular immunity and further scrutinized for antigenicity, allergenicity, water solubility, and toxicity analysis. The final potential epitopes were subjected to population coverage analysis. Major histocompatibility complex (MHC) class combined, and MHC Class I and II world population coverage was obtained as 99.74%, and 98.55% respectively while a combined 81.81% was covered. A multi-epitope peptide-based vaccine construct consisting of the adjuvant, epitopes, and linkers was subjected to the ProtParam tool to calculate its physiochemical properties. The total amino acids were 236, the molecular weight was 27.64kd, and the vaccine construct was stable with an instability index of 27.01. The Grand Average of Hydropathy (GRAVY) (hydrophilicity) value obtained was -0.659, being more negative and depicting the hydrophilic character. It was non-allergen antigenic with an antigenicity of 0.8913. The vaccine construct was further validated for binding efficacy with immune cell receptors MHC-I, MHC-II, and Toll-like receptor (TLR)-4. The molecular docking results depict that the designed vaccine has good binding potency with immune receptors crucial for antigen presentation and processing. Among the Vaccine-MHC-I, Vaccine-MHC-II, and Vaccine-TLR-4 complexes, the best-docked poses were identified based on their lowest binding energy scores of -886.8, -995.6, and -883.6, respectively. Overall, we observed that the designed vaccine construct can evoke a proper immune response and the construct could help experimental researchers in the formulation of a vaccine against the targeted pathogens.

Reverse vaccinology approaches to design a potent multiepitope vaccine against the HIV whole genome: immunoinformatic, bioinformatics, and molecular dynamics approaches.

Substantial advances have been made in the development of promising HIV vaccines to eliminate HIV-1 infection. For the first time, one hundred of the most submitted HIV subtypes and CRFs were retrieved from the LANL database, and the consensus sequences of the eleven HIV proteins were obtained to design vaccines for human and mouse hosts. By using various servers and filters, highly qualified B-cell epitopes, as well as HTL and CD8 + epitopes that were common between mouse and human alleles and were also located in the conserved domains of HIV proteins, were considered in the vaccine constructs. With 90% coverage worldwide, the human vaccine model covers a diverse allelic population, making it widely available. Codon optimization and in silico cloning in prokaryotic and eukaryotic vectors guarantee high expression of the vaccine models in human and E. coli hosts. Molecular dynamics confirmed the stable interaction of the vaccine constructs with TLR3, TLR4, and TLR9, leading to a substantial immunogenic response to the designed vaccine. Vaccine models effectively target the humoral and cellular immune systems in humans and mice; however, experimental validation is needed to confirm these findings in silico.

VacSol-ML(ESKAPE): Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.

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.

Putative new combination vaccine candidates identified by reverse vaccinology and genomic approaches to control enteric pathogens.

OBJECTIVES: The pathogenic microorganisms that cause intestinal diseases can significantly jeopardize people's health. Currently, there are no authorized treatments or vaccinations available to combat the germs responsible for intestinal disease. METHODS: Using immunoinformatics, we developed a potent multi-epitope Combination (combo) vaccine versus Salmonella and enterohemorrhagic E. coli. The B and T cell epitopes were identified by performing a conservancy assessment, population coverage analysis, physicochemical attributes assessment, and secondary and tertiary structure assessment of the chosen antigenic polypeptide. The selection process for vaccine development included using several bioinformatics tools and approaches to finally choose two linear B-cell epitopes, five CTL epitopes, and two HTL epitopes. RESULTS: The vaccine had strong immunogenicity, cytokine production, immunological properties, non-toxicity, non-allergenicity, stability, and potential efficacy against infections. Disulfide bonding, codon modification, and computational cloning were also used to enhance the stability and efficacy of expression in the host E. coli. The vaccine's structure has a strong affinity for the TLR4 ligand and is very durable, as shown by molecular docking and molecular modeling. The results of the immunological simulation demonstrated that both B and T cells had a heightened response to the vaccination component. CONCLUSIONS: The comprehensive in silico analysis reveals that the proposed vaccine will likely elicit a robust immune response against pathogenic bacteria that cause intestinal diseases. Therefore, it is a promising option for further experimental testing.

In silico design of multi-epitope vaccines against the hantaviruses by integrated structural vaccinology and molecular modeling approaches.

Hantaviruses are single-stranded RNA viruses belonging to the family Bunyaviridae that causes hantavirus cardiopulmonary syndrome (HCPS) and hemorrhagic fever with renal syndrome (HFRS) worldwide. Currently, there is no effective vaccination or therapy available for the treatment of hantavirus, hence there is a dire need for research to formulate therapeutics for the disease. Computational vaccine designing is currently a highly accurate, time and cost-effective approach for designing effective vaccines against different diseases. In the current study, we shortlisted highly antigenic proteins i.e., envelope, and nucleoprotein from the proteome of hantavirus and subjected to the selection of highly antigenic epitopes to design of next-generation multi-epitope vaccine constructs. A highly antigenic and stable adjuvant was attached to the immune epitopes (T-cell, B-cell, and HTL) to design Env-Vac, NP-Vac, and Com-Vac constructs, which exhibit stronger antigenic, non-allergenic, and favorable physiochemical properties. Moreover, the 3D structures were predicted and docking analysis revealed robust interactions with the human Toll-like receptor 3 (TLR3) to initiate the immune cascade. The total free energy calculated for Env-Vac, NP-Vac, and Com-Vac was -50.02 kcal/mol, -24.13 kcal/mol, and -62.30 kcal/mol, respectively. In silico cloning, results demonstrated a CAI value for the Env-Vac, NP-Vac, and Com-Vac of 0.957, 0.954, and 0.956, respectively, while their corresponding GC contents were 65.1%, 64.0%, and 63.6%. In addition, the immune simulation results from three doses of shots released significant levels of IgG, IgM, interleukins, and cytokines, as well as antigen clearance over time, after receiving the vaccine and two booster doses. Our vaccines against Hantavirus were found to be highly immunogenic, inducing a robust immune response that demands experimental validation for clinical usage.

Reverse vaccinology-based multi-epitope vaccine design against Indian group A rotavirus targeting VP7, VP4, and VP6 proteins.

Rotavirus, a primary contributor to severe cases of infantile gastroenteritis on a global scale, results in significant morbidity and mortality in the under-five population, particularly in middle to low-income countries, including India. WHO-approved live-attenuated vaccines are linked to a heightened susceptibility to intussusception and exhibit low efficacy, primarily attributed to the high genetic diversity of rotavirus, varying over time and across different geographic regions. Herein, molecular data on Indian rotavirus A (RVA) has been reviewed through phylogenetic analysis, revealing G1P[8] to be the prevalent strain of RVA in India. The conserved capsid protein sequences of VP7, VP4 and VP6 were used to examine helper T lymphocyte, cytotoxic T lymphocyte and linear B-cell epitopes. Twenty epitopes were identified after evaluation of factors such as antigenicity, non-allergenicity, non-toxicity, and stability. These epitopes were then interconnected using suitable linkers and an N-terminal beta defensin adjuvant. The in silico designed vaccine exhibited structural stability and interactions with integrins (αvß3 and αIIbß3) and toll-like receptors (TLR2 and TLR4) indicated by docking and normal mode analyses. The immune simulation profile of the designed RVA multiepitope vaccine exhibited its potential to trigger humoral as well as cell-mediated immunity, indicating that it is a promising immunogen. These computational findings indicate potential efficacy of the designed vaccine against rotavirus infection.

Designing multi-epitope vaccine against human cytomegalovirus integrating pan-genome and reverse vaccinology pipelines.

Human cytomegalovirus (HCMV) is accountable for high morbidity in neonates and immunosuppressed individuals. Due to the high genetic variability of HCMV, current prophylactic measures are insufficient. In this study, we employed a pan-genome and reverse vaccinology approach to screen the target for efficient vaccine candidates. Four proteins, envelope glycoprotein M, UL41A, US23, and US28, were shortlisted based on cellular localization, high solubility, antigenicity, and immunogenicity. A total of 29 B-cell and 44 T-cell highly immunogenic and antigenic epitopes with high global population coverage were finalized using immunoinformatics tools and algorithms. Further, the epitopes that were overlapping among the finalized B-cell and T-cell epitopes were linked with suitable linkers to form various combinations of multi-epitopic vaccine constructs. Among 16 vaccine constructs, Vc12 was selected based on physicochemical and structural properties. The docking and molecular simulations of VC12 were performed, which showed its high binding affinity (-23.35 kcal/mol) towards TLR4 due to intermolecular hydrogen bonds, salt bridges, and hydrophobic interactions, and there were only minimal fluctuations. Furthermore, Vc12 eliciting a good response was checked for its expression in Escherichia coli through in silico cloning and codon optimization, suggesting it to be a potent vaccine candidate.

Reverse vaccinology approach for identification of epitopes from E1 protein as peptide vaccine against HCV: A proof of concept.

The development of effective vaccines against Hepatitis C Virus (HCV) remains a global health priority and challenge. In this study, we employed an integrative approach combining computational epitope prediction with experimental validation to identify immunogenic peptides targeting the E1 glycoprotein of HCV. In the present report, computational data from various epitope prediction algorithms such as IEDB and SYFPEITHI, followed by molecular dynamics (MD) simulations and immuno-informatics analysis is presented. Through computational screening, we identified potential epitope candidates, with QVRNSSGLY (P3) and QLFTFSPRH (P7) emerging as promising candidates. MD simulations revealed stable interactions between these epitopes and MHC molecule, further validated by free energy estimations using MMPBSA method. Immuno-informatics analysis supported these findings, showing high binding potential and immunogenicity scores for the selected peptides. Subsequent synthesis and characterization of epitope peptides confirmed their structural integrity and purity required for conducting immune activation assays. Experimental immunological assays carried out in this study involved epitope peptide induced activation of CD8 + and CD4 + T cells from healthy human subjects and HCV- recovered patients. Data from experimental validation revealed significant cytokine release upon exposure to epitope peptides, particularly TNF-a, IL-6, and GM-CSF, indicative of robust immune responses. Notably, peptides P3 and P7 exhibited the most pronounced cytokine induction profiles, underscoring their potential as vaccine candidates. Further investigations addressing the mechanism of action of these epitope peptides under preclinical and clinical settings may help in developing effective vaccine against HCV.

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.

Designing an mRNA Vaccine against P. jirovecii Involved in Fatal Pneumonia Infections via Comparative Proteomics and Reverse Vaccinology Approaches.

BACKGROUND: Pneumocystis jirovecii is the most emerging life-threating health problem that causes acute and fatal pneumonia infection. It is rare and more contagious for patients with leukemia and immune-deficiency disorders. Until now there is no treatment available for this infection therefore, it is needed to develop any treatment against this pathogen. METHODS: In this work, we used comparative proteomics, robust immune-informatics, and reverse vaccinology to create an mRNA vaccine against Pneumocystis jirovecii by targeting outer and transmembrane proteins. Using a comparative subtractive proteomic analysis of two Pneumocystis jirovecii proteomes, a distinct non-redundant Pneumocystis jirovecii (strain SE8) proteome was chosen. Seven Pneumocystis jirovecii transmembrane proteins were chosen from this proteome based on hydrophilicity, essentiality, virulence, antigenicity, pathway interaction, protein-protein network analysis, and allergenicity. OBJECTIVE: The reverse vaccinology approach was used to predict the immunogenic and antigenic epitopes of major histocompatibility complex (MHC) I, II and B-cells from the selected proteins on the basis of their antigenicity, toxicity and allergenicity. These immunogenic epitopes were linked together to construct the mRNA-based vaccine. To enhance the immunogenicity, suitable adjuvant, linkers (GPGPG, KK, and CYY), and PRDRE sequences were used. RESULTS: Through predictive modeling and confirmation via the Ramachandran plot, we assessed secondary and 3D structures. The adjuvant RpfE was incorporated to enhance the vaccine construct's immunogenicity (GRAVY index: -0.271, instability index: 39.53, antigenicity: 1.0428). The physiochemical profiling of vaccine construct was predicted it an antigenic, efficient, and potential vaccine. Notably, strong interactions were observed between the vaccine construct and TLR-3/TLR-4 (-1301.7 kcal/mol-1 and -1374.7 kcal/mol-1). CONCLUSIONS: The results predicted that mRNA-based vaccines trigger a cellular and humoral immune response, making the vaccine potential candidate against Pneumocystis jirovecii and it is more suitable for in-vitro analysis and validation to prove its effectiveness.

An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology.

Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.

An MCDM approach for Reverse vaccinology model to predict bacterial protective antigens.

Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets.

Advancing molecular modeling and reverse vaccinology in broad-spectrum yellow fever virus vaccine development.

Yellow fever outbreaks are prevalent, particularly in endemic regions. Given the lack of an established treatment for this disease, significant attention has been directed toward managing this arbovirus. In response, we developed a multiepitope vaccine designed to elicit an immune response, utilizing advanced immunoinformatic and molecular modeling techniques. To achieve this, we predicted B- and T-cell epitopes using the sequences from all structural (E, prM, and C) and nonstructural proteins of 196 YFV strains. Through comprehensive analysis, we identified 10 cytotoxic T-lymphocyte (CTL) and 5T-helper (Th) epitopes that exhibited overlap with B-lymphocyte epitopes. These epitopes were further evaluated for their affinity to a wide range of human leukocyte antigen system alleles and were rigorously tested for antigenicity, immunogenicity, allergenicity, toxicity, and conservation. These epitopes were linked to an adjuvant ( ß -defensin) and to each other using ligands, resulting in a vaccine sequence with appropriate physicochemical properties. The 3D structure of this sequence was created, improved, and quality checked; then it was anchored to the Toll-like receptor. Molecular Dynamics and Quantum Mechanics/Molecular Mechanics simulations were employed to enhance the accuracy of docking calculations, with the QM portion of the simulations carried out utilizing the density functional theory formalism. Moreover, the inoculation model was able to provide an optimal codon sequence that was inserted into the pET-28a( +) vector for in silico cloning and could even stimulate highly relevant humoral and cellular immunological responses. Overall, these results suggest that the designed multi-epitope vaccine can serve as prophylaxis against the yellow fever virus.