Predicting epitopes for vaccine development using bioinformatics tools

X-Ray Crystallography Based Epitope Mapping of Glycoproteins and RNA in Chandipura Vesiculovirus for Vaccine Design

This study investigates potential epitopes in the glycoprotein and RNA of Chandipura vesiculovirus (CHPV) using MHC Class I (HLA-A0201) and MHC Class II (DRB1_0101) molecules with 3D structures derived from x-ray crystallography. Computationally derived peptides were mapped and subjected to in silico docking, revealing promising targets for CD8+ cytotoxic and CD4+ helper T cells. Key factors analysed include solvent accessible surface area (SASA), Debye-Waller factor (B-factor), and polar bond interactions. Post-docking, removal of N-acetylglucosamine (NAG) increased peptide stability and reduced B-factors, while SO4 presence had minimal impact. SASA values increased by up to 237.5% with MHC Class I, and RNA docking with MHC Class II displayed mixed SASA changes. Polar bond interactions also increased post-docking, indicating the strong potential of identified CHPV epitopes.

Pfs16: A Key Parasitophorous Vacuole Membrane Protein Crucial for Malaria Parasite Development and Transmission

Malaria remains a formidable challenge to global health, claiming the lives of nearly half a million individuals annually despite vigorous efforts to curb its spread. Among the myriad factors influencing the persistence and virulence of this disease, the role of specific proteins during the Plasmodium development cycle is critical. The protein of interest, Pfs16, is a Parasitophorous Vacuole Membrane Protein expressed from the earliest asexual stages, which encompass the development of Plasmodium falciparum in the host, to the final stage of the parasite's development in the mosquito, the sporozoite, playing a crucial role in this lifecycle. Understanding the function and mechanism of this conserved protein is pivotal for advancing our strategies to combat malaria. In this review, we examine the work on Pfs16 in both the asexual and sexual stages of parasite development, aiming to gain a better understanding of this protein as a promising candidate for drug and vaccine development.

Computational design of a fimbriae-derived multi-epitope vaccine candidate against Klebsiella pneumoniae

Klebsiella pneumoniae is a pathogen that causes infections in various parts of the body, with high mortality rates reported in antibiotic-resistant cases. Treating at-risk individuals requires crucial vaccination efforts due to the challenges that exist. This research involved designing a multi-epitope vaccine from K. pneumoniae's fimbriae antigens. Optimal T-cell and B-cell epitopes were chosen through in silico studies including epitope-HLAs molecular docking. The multi-epitope was created, featuring antigenic T- and B-cell epitopes, β-defensin as an adjuvant, the PADRE sequence to boost immunogenicity and well-suited linkers. The tertiary structure of the multi-epitope was achieved through modeling and molecular dynamics-based refinements. The construct underwent scrutiny for structural traits, physicochemical properties, conformational B epitope prediction, immune responses simulation, in silico cloning, molecular docking for assay binding to toll-like receptors (TLRs), and deformability studies. The outcomes indicated the vaccine candidate's positive attributes, encompassing immunogenicity, structure, physicochemical properties, solubility, TLR binding, toxicity, stability, allergenicity, and cross-reactivity. The multi-epitope vaccine candidate exhibits the potential for provoking diverse immune responses against K. pneumoniae. Nevertheless, additional in vitro and in vivo experimental tests are necessary to substantiate its efficacy.

Bioinformatics analysis of the in silico engineered protein vaccine with and without Escherichia coli heat labile enterotoxin adjuvant on the model of Klebsiella pneumoniae

Klebsiella pneumoniae (K. pneumoniae) has been identified as a major cause of nosocomial infections with multidrug-resistant phenotypes. Vaccination is one of the most effective methods to prevent infectious diseases. We aim to design a vaccine candidate based on the epitope-rich domains of the OmpA, OMPK17, and fimb proteins of K. pneumoniae that could protect against this infection. A vaccine structure was constructed by selecting five epitope-rich domains from three proteins. We decided to add the heat-labile toxin (LT) of Escherichia coli as an adjuvant to the designed protein structure. The evaluation of the vaccine candidates' interaction with the immune system's receptors showed an appropriate interaction of the specially adjuvated protein with TLR2 and TLR4. The stability of the interactions was also studied by molecular dynamics (MD) for to 100 ns. All parameters showed that the structure of the candidate proteins alone and in complex with TLR2 and TLR4 are stable, especially the adjuvanted protein. Immune response simulations showed that both candidates induce acceptable protective immune responses. Overall, the LT-adjuvanted design protein may have the potential to induce more favorable protective immune responses. However, further in vitro and in vivo studies are required to obtain more definitive results.

Navigating the landscape: A comprehensive overview of computational approaches in therapeutic antibody design and analysis

Immunotherapy, harnessing components like antibodies, cells, and cytokines, has become a cornerstone in treating diseases such as cancer and autoimmune disorders. Therapeutic antibodies, in particular, have transformed modern medicine, providing a targeted approach that destroys disease-causing cells while sparing healthy tissues, thereby reducing the side effects commonly associated with chemotherapy. Beyond oncology, these antibodies also hold promise in addressing chronic infections where conventional therapeutics may fall short. However, antibodies identified through in vivo or in vitro methods often require extensive engineering to enhance their therapeutic potential. This optimization process, aimed at improving affinity, specificity, and reducing immunogenicity, is both challenging and costly, often involving trade-offs between critical properties. Traditional methods of antibody development, such as hybridoma technology and display techniques, are resource-intensive and time-consuming. In contrast, computational approaches offer a faster, more efficient alternative, enabling the precise design and analysis of therapeutic antibodies. These methods include sequence and structural bioinformatics approaches, next-generation sequencing-based data mining, machine learning algorithms, systems biology, immuno-informatics, and integrative approaches. These approaches are advancing the field by providing new insights and enhancing the accuracy of antibody design and analysis. In conclusion, computational approaches are essential in the development of therapeutic antibodies, significantly improving the precision and speed of discovery, optimization, and validation. Integrating these methods with experimental approaches accelerates therapeutic antibody development, paving the way for innovative strategies and treatments for various diseases ranging from cancers to autoimmune and infectious diseases.

Immunoinformatics design of a novel multiepitope vaccine candidate against non-typhoidal salmonellosis caused by Salmonella Kentucky using outer membrane proteins A, C, and F

The global public health risk posed by Salmonella Kentucky (S. Kentucky) is rising, particularly due to the dissemination of antimicrobial resistance genes in human and animal populations. This serovar, widespread in Africa, has emerged as a notable cause of non-typhoidal gastroenteritis in humans. In this study, we used a bioinformatics approach to develop a peptide-based vaccine targeting epitopes from the outer membrane proteins A, C, and F of S. Kentucky. Additionally, we employed flagellin protein (fliC) from Salmonella Typhimurium (S. Typhimurium) as an adjuvant to enhance the vaccine's effectiveness. Through this approach, we identified 14 CD8+ and 7 CD4+ T-cell epitopes, which are predicted to be restricted by various MHC class I and MHC class II alleles. The predicted epitopes are expected to achieve a population coverage of 94.91% when used in vaccine formulations. Furthermore, we identified seven highly immunogenic linear B-cell epitopes and three conformational B-cell epitopes. These T-cell and B-cell epitopes were then linked using appropriate linkers to create a multi-epitope vaccine (MEV). To boost the immunogenicity of the peptide construct, fliC from S. Typhimurium was included at the N-terminal. The resulting MEV construct demonstrated high structural quality and favorable physicochemical properties. Molecular docking studies with Toll-like receptors 1, 2, 4, and 5, followed by molecular dynamic simulations, suggested that the vaccine-receptor complexes are energetically feasible, stable, and robust. Immune simulation results showed that the MEV elicited significant responses, including IgG, IgM, CD8+ T-cells, CD4+ T-cells, and various cytokines (IFN-γ, TGF-β, IL-2, IL-10, and IL-12), along with a noticeable reduction in antigen levels. Despite these promising in-silico findings, further validation through preclinical and clinical trials is required to confirm the vaccine's efficacy and safety.

Evaluating the Immunoprotective and Diagnostic Potential of Schistosoma mansoni Epitopes from Sm050890 and Sm141290 Proteins Identified Through Reverse Vaccinology

PURPOSE

Schistosomiasis remains a parasitic disease affecting millions of people worldwide, requiring interventions like vaccination. In previous work, our group used reverse vaccinology to identify two epitopes from the Schistosoma mansoni proteins, Sm050890 (44-58) and Sm141290 (225-239). This study evaluated the immune response profile and protection induced by peptides, as a mixture of immunogens, in murine vaccination trials. Additionally, the diagnostic potential of these peptides was assessed on immunoassays.

METHODS

Mice were immunized with a formulation containing the mixture of the peptides, subsequently infected, and perfused for worm burden recovery and quantification. Liver and blood samples from animals were used to evaluate the effect of immunization on the formation of granulomas and specific anti-peptide antibodies (IgG). Additionally, cytokine measurement was performed in splenocyte cultures from immunized mice, and peripheral blood serum from individuals infected with S. mansoni was used to assess the recognition of the peptides by IgG antibodies.

RESULTS

The vaccine stimulated an increase in the production of IgG and IgG2c antibodies, associated with a significant reduction of 44 - 29% in worm burden. Although the vaccine did not reduce liver pathology, it enhanced the production of IFN-γ while decreasing IL-10 production by splenocytes. Furthermore, the peptides Sm050890 (44-58) and Sm141290 (225-239) were not recognized by IgG antibodies in the serum from infected individuals.

CONCLUSION

Overall, our data suggest that the peptides Sm050890 (44-58) and Sm141290 (225-239) are promising vaccine candidates against schistosomiasis and can be used to compose a multiepitope/chimeric vaccine in future studies.

Design of a Multiepitope Pan-Proteomic mRNA Vaccine Construct Against African Swine Fever Virus: A Reverse Vaccinology Approach

African swine fever (ASF), caused by African swine fever virus (ASFV), is a highly contagious disease with devastating effects on the global pig industry. This warrants the development of effective control strategies, such as vaccines. However, previously developed inactivated vaccines have proven ineffective, while live-attenuated vaccines carry inherent safety risks. The use of mRNA vaccines eliminates these risks offering a safe, cost-effective, and efficient vaccine strategy against ASFV. In this study, a reverse vaccinology approach was used to design a multiepitope pan-proteomic mRNA vaccine against ASFV. Various bioinformatics tools were employed to predict epitopes for cytotoxic T lymphocytes, helper T lymphocytes, and B lymphocytes. A 50S ribosomal L7/L12 protein adjuvant, 5' cap, poly(A) tail, signal peptide, and MHC-I-targeting domain were incorporated into the design using appropriate linkers to increase immunogenicity, stability, and recognition efficiency. The physicochemical properties of the final construct were evaluated, and docking analyses were done with Toll-like receptors (TLRs) 3, 4, and 7 to evaluate binding affinity. A molecular dynamics simulation was then performed to determine binding stability, while immune simulations evaluated host's immune response. Based on 100 ASFV proteomes, six epitopes that induce cytotoxic T-cell responses, five epitopes that induce helper T-cell responses, and four epitopes that induce antibody production were predicted. The designed vaccine construct was found to be nonallergenic, antigenic, and stable when bound to TLR4 while the binding pocket analyses of the vaccine construct to TLR3 and TLR7 indicate high translation efficiency. Immune simulations demonstrated successful induction of immune responses and generation of antigen-specific memory cells. In conclusion, this study introduces an mRNA vaccine construct as a potential disease control strategy against ASF for in vitro confirmation.

Advances of computational methods enhance the development of multi-epitope vaccines

Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.

Multi-Epitopic Peptide Vaccine Against Newcastle Disease Virus: Molecular Dynamics Simulation and Experimental Validation

BACKGROUND

Newcastle disease virus (NDV) is a highly contagious and economically devastating pathogen affecting poultry worldwide, leading to significant losses in the poultry industry. Despite existing vaccines, outbreaks continue to occur, highlighting the need for more effective vaccination strategies. Developing a multi-epitopic peptide vaccine offers a promising approach to enhance protection against NDV.

OBJECTIVES

Here, we aimed to design and evaluate a multi-epitopic vaccine against NDV using molecular dynamics (MD) simulation.

METHODOLOGY

We retrieved NDV sequences for the fusion (F) protein and hemagglutinin-neuraminidase (HN) protein. Subsequently, B-cell and T-cell epitopes were predicted. The top potential epitopes were utilized to design the vaccine construct, which was subsequently docked against chicken TLR4 and MHC1 receptors to assess the immunological response. The resulting docked complex underwent a 1 microsecond (1000 ns) MD simulation. For experimental evaluation, the vaccine's efficacy was assessed in mice and chickens using a controlled study design, where animals were randomly divided into groups receiving either a local ND vaccine or the peptide vaccine or a control treatment.

RESULTS

The 40 amino acid peptide vaccine demonstrated strong binding affinity and stability within the TLR4 and MHC1 receptor-peptide complexes. The root mean square deviation of peptide vaccine and TLR4 receptor showed rapid stabilization after an initial repositioning. The root mean square fluctuation revealed relatively low fluctuations (below 3 Å) for the TLR4 receptor, while the peptide exhibited higher fluctuations. The overall binding energy of the peptide vaccine with TLR4 and MHC1 receptors amounted to -15.7 kcal·mol-1 and -36.8 kcal·mol-1, respectively. For experimental evaluations in mice and chicken, the peptide vaccine was synthesized using services of GeneScript Biotech® (Singapore) PTE Limited. Experimental evaluations showed a significant immune response in both mice and chickens, with the vaccine eliciting robust antibody production, as evidenced by increasing HI titers over time. Statistical analysis was performed using an independent t-test with Type-II error to compare the groups, calculating the p-values to determine the significance of the immune response between different groups.

CONCLUSIONS

Multi-epitopic peptide vaccine has demonstrated a good immunological response in natural hosts.

Identification and characterization of linear B-cell epitopes on African swine fever virus H171R protein

African swine fever virus (ASFV) is a highly contagious and often fatal pathogen that poses a significant threat to the swine industry worldwide. The H171R protein, a structural component of ASFV, plays crucial roles in viral assembly, host cell entry, and modulation of the host immune response. This study aimed to comprehensively characterize the linear B-cell epitopes on the H171R protein to facilitate the development of diagnostic tools and subunit vaccines against ASFV. A combined approach involving bioinformatics analysis and experimental techniques was employed. The recombinant H171R protein was expressed and purified, and specific monoclonal antibodies were generated through immunization and hybridoma technology. Systematic epitope mapping using overlapping peptide fragments and alanine-scanning mutagenesis revealed four minimal linear epitopes: 84HPLLPYQQSSDEQP97, 93SDEQPMMPYQQPPG106, 111PYEQIYHKKHASQQ124, and 129LNDYYQHILALGDED143. The identified epitopes exhibited strong immunogenicity, as demonstrated by their reactivity with ASFV-positive swine sera. Critical amino acid residues within each epitope were identified through mutational analysis. Structural modeling and visualization of the H171R protein provided insights into the spatial distribution and accessibility of the epitope regions. These findings contribute to a better understanding of the H171R protein's antigenic properties and lay the foundation for developing effective diagnostic assays and subunit vaccines against African swine fever.

IMPORTANCE

African swine fever virus (ASFV) poses a severe threat to the global swine industry. This study characterizes linear B-cell epitopes on the crucial ASFV H171R protein, facilitating the development of improved diagnostics and subunit vaccines. Four immunogenic epitopes were identified, offering valuable information for designing sensitive diagnostic assays and potential subunit vaccine candidates. By advancing the understanding of H171R's antigenic landscape, this research contributes to controlling ASFV's devastating impacts, safeguarding the swine industry, and ensuring food security.

Immunopeptidomics of Salmonella enterica Serovar Typhimurium-Infected Pig Macrophages Genotyped for Class II Molecules

Salmonellosis is a zoonotic infection that has a major impact on human health; consuming contaminated pork products is the main source of such infection. Vaccination responses to classic vaccines have been unsatisfactory; that is why peptide subunit-based vaccines represent an excellent alternative. Immunopeptidomics was used in this study as a novel approach for identifying antigens coupled to major histocompatibility complex class II molecules. Three homozygous individuals having three different haplotypes (Lr-0.23, Lr-0.12, and Lr-0.21) were thus selected as donors; peripheral blood macrophages were then obtained and stimulated with Salmonella typhimurium (MOI 1:40). Although similarities were observed regarding peptide length distribution, elution patterns varied between individuals; in total, 1990 unique peptides were identified as follows: 372 for Pig 1 (Lr-0.23), 438 for Pig 2 (Lr.0.12) and 1180 for Pig 3 (Lr.0.21). Thirty-one S. typhimurium unique peptides were identified; most of the identified peptides belonged to outer membrane protein A and chaperonin GroEL. Notably, 87% of the identified bacterial peptides were predicted in silico to be elution ligands. These results encourage further in vivo studies to assess the immunogenicity of the identified peptides, as well as their usefulness as possible protective vaccine candidates.

Genomics and Bioinformatics in One Health: Transdisciplinary Approaches for Health Promotion and Disease Prevention

The One Health concept underscores the interconnectedness of human, animal, and environmental health, necessitating an integrated, transdisciplinary approach to tackle contemporary health challenges. This perspective paper explores the pivotal role of genomics and bioinformatics in advancing One Health initiatives. By leveraging genomic technologies and bioinformatics tools, researchers can decode complex biological data, enabling comprehensive insights into pathogen evolution, transmission dynamics, and host-pathogen interactions across species and environments (or ecosystems). These insights are crucial for predicting and mitigating zoonotic disease outbreaks, understanding antimicrobial resistance patterns, and developing targeted interventions for health promotion and disease prevention. Furthermore, integrating genomic data with environmental and epidemiological information enhances the precision of public health responses. Here we discuss case studies demonstrating successful applications of genomics and bioinformatics in One Health contexts, such as including data integration, standardization, and ethical considerations in genomic research. By fostering collaboration among geneticists, bioinformaticians, epidemiologists, zoologists, and data scientists, the One Health approach can harness the full potential of genomics and bioinformatics to safeguard global health. This perspective underscores the necessity of continued investment in interdisciplinary education, research infrastructure, and policy frameworks to effectively employ these technologies in the service of a healthier planet.

Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines

Introduction

BCoV is one of the significant causes of enteritis in young calves; it may also be responsible for many respiratory outbreaks in young calves. BCoV participates in the development of bovine respiratory disease complex in association with other bacterial pathogens. Our study aimed (1) to map the immunogenic epitopes (B and T cells) within the major BCoV structural proteins. These epitopes are believed to induce a robust immune response through the interaction with major histocompatibility complex (MHC class II) molecules (2) to design some novel BCoV multiepitope-based vaccines.

Materials and Methods

The goal is achieved through several integrated in silico prediction computational tools to map these epitopes within the major BCoV structural proteins. The final vaccine was constructed in conjugation with the Choleratoxin B toxin as an adjuvant. The tertiary structure of each vaccine construct was modeled through the AlphaFold2 tools. The constructed vaccine was linked to some immunostimulants such as Toll-like receptors (TLR2 and TLR4). We also predicted the affinity binding of these vaccines with this targeted protein using molecular docking. The stability and purity of each vaccine construct were assessed using the Ramachandran plot and the Z-score values. We created the in silico cloning vaccine constructs using various expression vectors through vector builder and Snap gene.

Results and discussion

The average range of major BCoV structural proteins was detected within the range of 0.4 to 0.5, which confirmed their antigen and allergic properties. The binding energy values were detected between -7.9 and -9.4 eV and also confirmed their best interaction between our vaccine construct and Toll-like receptors. Our in silico cloning method expedited the creation of vaccine constructs and established a strong basis for upcoming clinical trials and experimental validations.

Conclusion

Our designed multiepitope vaccine candidates per each BCoV structural protein showed high antigenicity, immunogenicity, non-allergic, non-toxic, and high-water solubility. Further studies are highly encouraged to validate the efficacy of these novel BCoV vaccines in the natural host.

Advancing one health vaccination: In silico design and evaluation of a multi-epitope subunit vaccine against Nipah virus for cross-species immunization using immunoinformatics and molecular modeling

The resurgence of the Nipah virus (NiV) in 2023 has raised concerns for another potentially severe pandemic, given its history of high mortality from previous outbreaks. Unfortunately, no therapeutics and vaccines have been available for the virus. This study used immunoinformatics and molecular modeling to design and evaluate a multi-epitope subunit vaccine targeting NiV. The designed vaccine construct aims to stimulate immune responses in humans and two other intermediate animal hosts of the virus-swine and equine. Using several epitope prediction tools, ten peptides that induced B-lymphocyte responses, 17 peptides that induced cytotoxic T-lymphocyte (CTL) responses, and 12 peptides that induced helper T-lymphocyte (HTL) responses were mapped from nine NiV protein sequences. However, the CTL and HTL-inducing peptides were reduced to ten and eight, respectively, following molecular docking and dynamics. These screened peptides exhibited stability with 30 common major histocompatibility complex (MHC) receptors found in humans, swine, and equine. All peptides were linked using peptide linkers to form the multi-epitope construct and various adjuvants were tested to enhance its immunogenicity. The vaccine construct with resuscitation-promoting factor E (RpfE) adjuvant was selected as the final design based on its favorable physicochemical properties and superior immune response profile. Molecular docking was used to visualize the interaction of the vaccine to toll-like receptor 4 (TLR4), while molecular dynamics confirmed the structural stability of this interaction. Physicochemical property evaluation and computational simulations showed that the designed vaccine construct exhibited favorable properties and elicited higher antibody titers than the six multi-epitope NiV vaccine designs available in the literature. Further in vivo and in vitro experiments are necessary to validate the immunogenicity conferred by the designed vaccine construct and its epitope components. This study demonstrates the capability of computational methodologies in rational vaccine design and highlights the potential of cross-species vaccination strategies for mitigating potential NiV threats.

Design of multi-epitope vaccine candidate based on OmpA, CarO and ZnuD proteins against multi-drug resistant Acinetobacter baumannii

Acinetobacter baumannii has been identified as a major cause of nosocomial infections. Acinetobacter infections are often difficult to treat with multidrug resistant phenotypes. One of the most effective ways to combat infectious diseases is through vaccination. In this study, an attempt was made to select the most protective and potent immunostimulatory epitopes based on the epitope-rich domains of the ZnuD, OmpA and CarO proteins of Acinetobacter baumannii to design a vaccine that can protect against this infection. After predicting the epitope of B- and T-cells, seven antigenic regions of three proteins CarO, ZnuD and OmpA, were selected. These regions were bound by a GGGS linker. The binding affinity and molecular interactions of the vaccine with the immune receptors TLR2 and TLR4 were studied using molecular docking analysis. This vaccine design was subjected to in silico immune simulations using C-ImmSim. The designed vaccine was highly antigenic, non-allergenic and stable. TLR2 and TLR4 were selected to analyze the ability of the modeled chimeric protein to interact with immune system receptors. The results showed strong interaction between the designed protein vaccine with TLR2 (-18.8 kcal mol-1) and TLR4 (-15.1 kcal mol-1). To verify the stability of the interactions and the structure of the designed protein, molecular dynamics (MD) simulations were performed for 200 ns. Various analyses using MD showed that the protein structure is stable alone and in interaction with TLR2 and TLR4. The ability of the vaccine candidate protein to stimulate the immune system to produce the necessary cytokines and antibodies against Acinetobacter baumannii was also demonstrated by the ability of the protein designed using the C-ImmSim web server to induce an immune response. Therefore, the designed protein vaccine may be a suitable candidate for in vivo as well as in vitro studies against Acinetobacter baumannii infections.

Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.

Immunoinformatics approach for designing a multiepitope subunit vaccine against porcine epidemic diarrhea virus genotype IIA spike protein

Background

Porcine epidemic diarrhea (PED), caused by the porcine epidemic diarrhea virus (PEDV), is associated with high mortality and morbidity rates, especially in neonatal pigs. This has resulted in significant economic losses for the pig industry. PEDV genotype II-based vaccines were found to confer better immunity against both heterologous and homologous challenges; specifically, spike (S) proteins, which are known to play a significant role during infection, are ideal for vaccine development.

Aim

This study aims to design a multi-epitope subunit vaccine targeting the S protein of the PEDV GIIa strain using an immunoinformatics approach.

Methods

Various bioinformatics tools were used to predict HTL, CTL, and B-cell epitopes. The epitopes were connected using appropriate linkers and conjugated with the CTB adjuvant and M-ligand. The final multiepitope vaccine construct (fMEVc) was then docked to toll-like receptor 4 (TLR4). The stability of the fMEVc-TLR4 complex was then simulated using GROMACS. C-immsim was then used to predict the in vitro immune response of the fMEVc.

Results

Six epitopes were predicted to induce antibody production, ten epitopes were predicted to induce CTL responses, and four epitopes were predicted to induce HTL responses. The assembled epitopes conjugated with the CTB adjuvant and M-ligand, fMEVc, is antigenic, non-allergenic, stable, and soluble. The construct showed a favorable binding affinity for TLR4, and the protein complex was shown to be stable through molecular dynamics simulations. A robust immune response was induced after immunization, as demonstrated through immune stimulation.

Conclusion

In conclusion, the multi-epitope subunit vaccine construct for PEDV designed in this study exhibits promising antigenicity, stability, and immunogenicity, eliciting robust immune responses and suggesting its potential as a candidate for further vaccine development.

Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction

The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.

Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities

The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.

Computational Drug Design Strategies for Fighting the COVID-19 Pandemic

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

Reverse vaccinology and immunoinformatics approaches to design multi-epitope based vaccine against oncogenic KRAS

Mutant KRAS-induced tumorigenesis is highly involved in the progression of pancreatic, lung, and breast cancer. Comparatively, KRAS G12D and KRAS G12C are the most frequent mutations that promote cancer progression and aggressiveness. Although KRAS mutant inhibitors exhibit significant therapeutic potential, day by day, they are becoming resistant among patients. Multi-epitope based cancer vaccines are a promising alternative strategy that induces an immune response against tumor antigens. In the present study, we have designed, constructed, and validated a novel multi-epitope vaccine construct against KRAS G12D and G12C mutants using reverse vaccinology and immunoinformatics approaches. In addition, the vaccine construct was structurally refined and showed significant physiochemical properties, and could induce an immune response. Furthermore, the optimized vaccine construct was cloned into a pET‑28a (+) expression vector through in silico cloning. Conclusively, the multi-epitope vaccine construct is structurally stable, soluble, antigenic, non‑allergic, and non‑toxic. Further, it has to be studied in in vitro and in vivo to evaluate its therapeutic efficacy against KRAS-mutated cancers in the near future.