Immunoinformatics approach to designing a multi-epitope vaccine against Saint Louis Encephalitis Virus

Designing of an innovative conserved multiepitope subunit vaccine targeting SARS-CoV-2 glycoprotein and nucleoprotein through immunoinformatic

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has imposed substantial challenges on our society due to the COVID-19 pandemic. This virus relies heavily on its surface glycoprotein (S-glycoprotein) to facilitate attachment, fusion, and entry into host cells. While the nucleoprotein (N) in the ribonucleoprotein core binds to the viral RNA genome. Therefore, our objective is to develop a novel vaccine candidate targeting the dominant T-cell and B-cell epitopes of the immune system. On the S-glycoprotein and nucleoprotein. Employing an immunoinformatic approach, we constructed a vaccine candidate with 13 highly antigenic B-cell epitopes, 19 HTL antigenic epitopes, and 18 CTL epitopes following a rigorous assessment. The multi-epitope construct successfully passed three-fold toxicity, allergenicity, and antigenicity tests, affirming its non-toxic, non-allergenic, and antigenic nature. This demonstrates the potentiality of the vaccine design to trigger an immunological response. Furthermore, the vaccine-ACE-2 receptor complex was tested, confirming its ability to interact with ACE-2's core pocket and induce an immunological response. Additionally, the vaccine's binding prowess for human toll-like receptors (TLR) (1, 3, 4, and 8) was investigated. According to the Ramachandran plot, 77.46% of the construct's amino acid residues fall within a favorable zone, establishing it as a viable vaccine candidate.

Structure-based virtual screening of Trachyspermum ammi metabolites targeting acetylcholinesterase for Alzheimer's disease treatment

In recent decades, Alzheimer's disease (AD) has garnered significant attention due to its rapid global prevalence. The cholinergic hypothesis posits that the degradation of acetylcholine by acetylcholinesterase (AChE) contributes to AD development. Despite existing anti-AChE drugs, their adverse side effects necessitate new agents. This study analyzed 150 bioactive phytochemicals from Trachyspermum ammi using structure-based drug design and various in-silico tools to identify potent anti-AChE compounds. Compounds were screened for drug-likeness (QEDw ≥50%) and bioavailability (≥55%) and underwent toxicity profiling via the ProTox-II server. Selected compounds were prepared for molecular docking with the human AChE protein as the receptor. Viridifloral, 2-Methyl-3-glucosyloxy-5-isopropyl phenol, Alpha-Curcumene, and Sterol emerged as top candidates with high AChE affinity. These results were validated by molecular dynamics simulations, confirming stable interactions. The hit compounds were further evaluated for drug-likeness using Lipinski's rule and ADMET properties, confirming favorable pharmacokinetic profiles. DFT optimization analyzed frontier molecular orbitals and electrostatic potential, demonstrating favorable chemical reactivity and stability. This study suggests that these identified compounds could be novel nature-derived AChE inhibitors, potentially contributing to AD treatment. However, further in-vitro and in-vivo studies are necessary to confirm their efficacy in biological systems. Future research will focus on developing these compounds into safe and effective drugs to combat Alzheimer's disease.

Developing a chimeric multiepitope vaccine against Nipah virus (NiV) through immunoinformatics, molecular docking and dynamic simulation approaches

Nipah virus (NiV) is a highly lethal zoonotic pathogen that poses a significant threat to human and animal health. Unfortunately, no effective treatments have been developed for this deadly zoonotic disease. Therefore, we designed a chimeric multiepitope vaccine targeting the Nipah virus (NiV) glycoprotein and fusion protein through immunoinformatic approaches. Therefore, the vaccine was developed by combining promising and potential antigenic MHC-I, MHC-II, and B-cell epitopes obtained from the selected proteins. When combined, the MHC-I and MHC-II epitopes offered 100 % global population coverage. The physicochemical characterization also exhibited favorable properties, including solubility and potential functional stability of the vaccine within the body (GRAVY score of -0.308). Structural analyses unveiled a well-stabilized secondary and tertiary structure with a Ramachandran score of 84.4 % and a Z score of -5.02. Findings from docking experiments with TLR-2 (-1089.3 kJ/mol) and TLR-4 (-1016.7 kJ/mol) showed a strong affinity of the vaccine towards the receptor. Molecular dynamics simulations revealed unique conformational dynamics among the "vaccine-apo," "vaccine-TLR-2," and "vaccine-TLR-4″ complexes. Consequently, the complexes exhibited significant compactness, flexibility, and exposure to solvents. The results of the codon optimization were remarkable, as the vaccine showed a significant amount of expression in the E. coli vector (GC content of 45.36 % and a CAI score of 1.0). The results of immune simulations, however, showed evidence of both adaptive and innate immune responses induced by the vaccine. Therefore, we highly recommend further research on this chimeric multiepitope vaccine to establish its efficacy and safety against the Nipah virus (NiV).

A pan-genomic analysis based multi-epitope vaccine development by targeting Stenotrophomonas maltophilia using reverse vaccinology method: an in-silico approach

Antibiotic resistance in bacteria leads to high mortality rates and healthcare costs, a significant concern for public health. A colonizer of the human respiratory system, Stenotrophomonas maltophilia is frequently associated with hospital-acquired infections in individuals with cystic fibrosis, cancer, and other chronic illnesses. The importance of this study is underscored by its capacity to meet the critical demand for effective preventive strategies against this pathogen, particularly among susceptible groups of cystic fibrosis and those undergoing cancer treatment. In this study, we engineered a multi-epitope vaccine targeting S. maltophilia through genomic analysis, reverse vaccination strategies, and immunoinformatic techniques by examining a total of 81 complete genomes of S. maltophilia strains. Our investigation revealed 1945 core protein-coding genes alongside their corresponding proteomic sequences, with 191 of these genes predicted to exhibit virulence characteristics. Out of the filtered proteins, three best antigenic proteins were selected for epitope prediction while seven epitopes each from CTL, HTL, and B cell were chosen for vaccine development. The vaccine was refined and validated, showing highly antigenic and desirable physicochemical features. Molecular docking assessments revealed stable binding with TLR-4. Molecular dynamic simulation demonstrated stable dynamics with minor alterations. The originality of this investigation is rooted in the thorough techniques aimed at designing a vaccine that directly targets S. maltophilia, a microorganism of considerable clinical relevance that currently lacks an available vaccine. This study not only responds to a pressing public health crisis but also lays the groundwork for subsequent research endeavors focused on the prevention of S. maltophilia outbreaks. Further evidence from studies in mice models is needed to confirm immune protection against S. maltophilia.

Design and characterization of a multi-epitope vaccine targeting Chlamydia abortus using immunoinformatics approach

Chlamydiosis is a widespread ailment affecting humans, livestock, and wildlife, caused by C. abortus, a member of the Chlamydia genus. This disease leads to reproductive disorders in bovines and poses a zoonotic risk, resulting in adverse outcomes such as abortion, stillbirths, weak offspring, endometritis, repeat breeding, and perinatal mortality. However, current chlamydiosis vaccines have limitations in terms of safety, efficacy, and stability, necessitating the development of effective and safe alternatives. In this study, our objective was to design a multi-epitope vaccine (MEV) targeting all strains of C. abortus using bioinformatics and immunoinformatics approaches. We identified highly antigenic and non-allergic proteins (yidC, yajC, secY, CAB503, and CAB746) using VaxiJen and AlgPred tools. Physicochemical analyses and secondary structure predictions confirmed protein stability through ProtParam and SOPMA methods. Furthermore, we employed IEDB-AR, NETMHCpan, and ToxinPred2 tools to predict cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, resulting in the identification of conserved epitopes for further analysis. The MEV construct, consisting of 545 amino acids, incorporated the adjuvant Beta defensin-3, along with 9 CTL epitopes and 21 HTL epitopes linked by EAAAK, KK, and AAY linkers. We assessed the safety and immunogenicity of the vaccine through comprehensive evaluations of antigenicity, toxicity, allergenicity, and physicochemical properties. Structural stability and quality were examined using 3D modeling via the ab initio approach with the Robetta platform. Molecular docking analysis explored the compatibility of the MEV with Toll-like receptor 9 (TLR9) using ClusPro, while molecular dynamics simulation with the DESMOND Maestro software predicted the stability and flexibility of the docked complex. Despite promising in silico findings, further wet lab investigations are crucial to validate the safety and efficacy of the MEV. Successful development and validation of this MEV hold significant potential in combatting chlamydiosis in both animal and human populations.Communicated by Ramaswamy H. Sarma.

Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6-16 mers linear B-cell epitopes, 4-9 mer CTL epitopes, and 8-15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3-which has been reported to induce the same TLRs as EBV-as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY - 0.710; and a ProsaWeb Z score of - 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases-which include various malignancies, tumors, and cancers-preventively.

Development of membrane protein-based vaccine against lumpy skin disease virus (LSDV) using immunoinformatic tools

INTRODUCTION

Lumpy skin disease, an economically significant bovine illness, is now found in previously unheard-of geographic regions. Vaccination is one of the most important ways to stop its further spread.

AIM

Therefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine.

METHODS

The membrane glycoprotein was selected for prediction of the different B- and T-cell epitopes by using the immune epitope database. The selected B- and T-cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll-like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores.

RESULTS

The in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide-based LSDV vaccine.

CONCLUSION

Thus, the present findings may indicate that the engineered multi-epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

Proteome based analysis of circulating SARS-CoV-2 variants: approach to a universal vaccine candidate

The discovery of the first infectious variant in Wuhan, China, in December 2019, has posed concerns over global health due to the spread of COVID-19 and subsequent variants. While the majority of patients experience flu-like symptoms such as cold and fever, a small percentage, particularly those with compromised immune systems, progress from mild illness to fatality. COVID-19 is caused by a RNA virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our approach involved utilizing immunoinformatic to identify vaccine candidates with multiple epitopes and ligand-binding regions in reported SARS-CoV-2 variants. Through analysis of the spike glycoprotein, we identified dominant epitopes for T-cells and B-cells, resulting in a vaccine construct containing two helper T-cell epitopes, six cytotoxic T-cell epitopes, and four linear B-cell epitopes. Prior to conjugation with adjuvants and linkers, all epitopes were evaluated for antigenicity, toxicity, and allergenicity. Additionally, we assessed the vaccine Toll-Like Receptors complex (2, 3, and 4). The vaccine construct demonstrated antigenicity, non-toxicity, and non-allergenicity, thereby enabling the host to generate antibodies with favorable physicochemical characteristics. Furthermore, the 3D structure of the B-cell construct exhibited a ProSA-web z-score plot with a value of -1.71, indicating the reliability of the designed structure. The Ramachandran plot analysis revealed that 99.6% of the amino acid residues in the vaccine subunit were located in the high favored observation region, further establishing its strong candidacy as a vaccination option.

Immunoinformatics Study: Multi-Epitope Based Vaccine Design from SARS-CoV-2 Spike Glycoprotein

The coronavirus disease 2019 outbreak has become a huge challenge in the human sector for the past two years. The coronavirus is capable of mutating at a higher rate than other viruses. Thus, an approach for creating an effective vaccine is still needed to induce antibodies against multiple variants with lower side effects. Currently, there is a lack of research on designing a multiepitope of the COVID-19 spike protein for the Indonesian population with comprehensive immunoinformatic analysis. Therefore, this study aimed to design a multiepitope-based vaccine for the Indonesian population using an immunoinformatic approach. This study was conducted using the SARS-CoV-2 spike glycoprotein sequences from Indonesia that were retrieved from the GISAID database. Three SARS-CoV-2 sequences, with IDs of EIJK-61453, UGM0002, and B.1.1.7 were selected. The CD8+ cytotoxic T-cell lymphocyte (CTL) epitope, CD4+ helper T lymphocyte (HTL) epitope, B-cell epitope, and IFN-γ production were predicted. After modeling the vaccines, molecular docking, molecular dynamics, in silico immune simulations, and plasmid vector design were performed. The designed vaccine is antigenic, non-allergenic, non-toxic, capable of inducing IFN-γ with a population reach of 86.29% in Indonesia, and has good stability during molecular dynamics and immune simulation. Hence, this vaccine model is recommended to be investigated for further study.

Designing of cytotoxic T lymphocyte-based multi-epitope vaccine against SARS-CoV2: a reverse vaccinology approach

SARS-CoV2 is a single-stranded RNA virus, gaining much attention after it out broke in China in December 2019. The virus rapidly spread to several countries around the world and caused severe respiratory illness to humans. Since the outbreak, researchers around the world have devoted maximum resources and effort to develop a potent vaccine that would offer protection to uninfected individuals against SARS-CoV2. Reverse vaccinology is a relatively new approach that thrives faster in vaccine research. In this study, we constructed Cytotoxic T Lymphocytes (CTL)-based multi-epitope vaccine using hybrid epitope prediction methods. A total of 121 immunogenic CTL epitopes were screened by various sequence-based prediction methods and docked with their respective HLA alleles using the AutoDock Vina v1.1.2. In all, 17 epitopes were selected based on their binding affinity, followed by the construction of multi-epitope vaccine by placing the appropriate linkers between the epitopes and tuberculosis heparin-binding hemagglutinin (HBHA) adjuvant. The final vaccine construct was modeled by the I-TASSER server and the best model was further validated by ERRAT, ProSA, and PROCHECK servers. Furthermore, the molecular interaction of the constructed vaccine with TLR4 was assessed by ClusPro 2.0 and PROtein binDIng enerGY prediction (PRODIGY) server. The immune simulation analysis confirms that the constructed vaccine was capable of inducing long-lasting memory T helper (Th) and CTL responses. Finally, the nucleotide sequence was codon-optimized by the JCAT tool and cloned into the pET21a (+) vector. The current results reveal that the candidate vaccine is capable of provoking robust CTL response against the SARS-CoV2.Communicated by Ramaswamy H. Sarma.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

Immunoinformatic design of a putative multi-epitope vaccine candidate against Trypanosoma brucei gambiense

Human African trypanosomiasis (HAT) is a neglected tropical disease that is caused by flagellated parasites of the genus Trypanosoma. HAT imposes a significant socio-economic burden on many countries in sub-Saharan Africa and its control is hampered by several drawbacks ranging from the ineffectiveness of drugs, complex dosing regimens, drug resistance, and lack of a vaccine. Despite more than a century of research and investigations, the development of a vaccine to tackle HAT is still challenging due to the complex biology of the pathogens. Advancements in computational modeling coupled with the availability of an unprecedented amount of omics data from different organisms have allowed the design of new generation vaccines that offer better antigenicity and safety profile. One of such new generation approaches is a multi-epitope vaccine (MEV) designed from a collection of antigenic peptides. A MEV can stimulate both cellular and humoral immune responses as well as avoiding possible allergenic reactions. Herein, we take advantage of this approach to design a MEV from conserved hypothetical plasma membrane proteins of Trypanosoma brucei gambiense, the trypanosome subspecies that is responsible for the west and central African forms of HAT. The designed MEV is 402 amino acids long (41.5 kDa). It is predicted to be antigenic, non-toxic, to assume a stable 3D conformation, and to interact with a key immune receptor. In addition, immune simulation foresaw adequate immune stimulation by the putative antigen and a lasting memory. Therefore, the designed chimeric vaccine represents a potential candidate that could be used to target HAT.

Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

Predicting epitopes for vaccine development using bioinformatics tools

Epitope-based DNA vaccine development is one application of bioinformatics or in silico studies, that is, computational methods, including mathematical, chemical, and biological approaches, which are widely used in drug development. Many in silico studies have been conducted to analyze the efficacy, safety, toxicity effects, and interactions of drugs. In the vaccine design process, in silico studies are performed to predict epitopes that could trigger T-cell and B-cell reactions that would produce both cellular and humoral immune responses. Immunoinformatics is the branch of bioinformatics used to study the relationship between immune responses and predicted epitopes. Progress in immunoinformatics has been rapid and has led to the development of a variety of tools that are used for the prediction of epitopes recognized by B cells or T cells as well as the antigenic responses. However, the in silico approach to vaccine design is still relatively new; thus, this review is aimed at increasing understanding of the importance of in silico studies in the design of vaccines and thereby facilitating future research in this field.

Immunoinformatics and Biophysics Approaches to Design a Novel Multi-Epitopes Vaccine Design against Staphylococcus auricularis

Due to the misuse of antibiotics in our daily lives, antimicrobial resistance (AMR) has become a major health problem. Penicillin, the first antibiotic, was used in the 1930s and led to the emergence of AMR. Due to alterations in the microbe’s genome and the evolution of new resistance mechanisms, antibiotics are losing efficacy against microbes. There are high rates of mortality and morbidity due to antibiotic resistance, so addressing this major health issue requires new approaches. Staphylococcus auricularis is a Gram-positive cocci and is capable of causing opportunistic infections and sepsis. S. auricularis is resistant to several antibiotics and does not currently have a licensed vaccine. In this study, we used bacterial pan-genome analysis (BPGA) to study S. auricularis pan-genome and applied a reverse immunology approach to prioritize vaccine targets against S. auricularis. A total of 15,444 core proteins were identified by BPGA analysis, which were then used to identify good vaccine candidates considering potential vaccine filters. Two vaccine candidates were evaluated for epitope prediction including the superoxide dismutase and gamma-glutamyl transferase protein. The epitope prediction phase involved the prediction of a variety of B-Cell and T-cell epitopes, and the epitopes that met certain criteria, such as antigenicity, immunogenicity, non-allergenicity, and non-toxicity were chosen. A multi-epitopes vaccine construct was then constructed from all the predicted epitopes, and a cholera toxin B-subunit adjuvant was also added to increase vaccine antigenicity. Three-dimensional models of the vaccine were used for downward analyses. Using the best-modeled structure, binding potency was tested with MHC-I, MHC-II and TLR-4 immune cells receptors, proving that the vaccine binds strongly with the receptors. Further, molecular dynamics simulations interpreted strong intermolecular binding between the vaccine and receptors and confirmed the vaccine epitopes exposed to the host immune system. The results support that the vaccine candidate may be capable of eliciting a protective immune response against S. auricularis and may be a promising candidate for experimental in vitro and in vivo studies.

Designing of a Recombinant Multi-Epitopes Based Vaccine against Enterococcus mundtii Using Bioinformatics and Immunoinformatics Approaches

Enterococcus species are an emerging group of bacterial pathogens that have a significant role in hospital-associated infections and are associated with higher mortality and morbidity rates. Among these pathogens, Enterococcus mundtii is one of the causative agents of multiple hospital associated infections. Currently, no commercially available licensed vaccine is present, and multi-drug resistant strains of the pathogen are prominent. Due to several limitations of experimental vaccinology, computational vaccine designing proved to be helpful in vaccine designing against several bacterial pathogens. Herein, we designed a multi-epitope-based vaccine against E. mundtii using in silico approaches. After an in-depth analysis of the core genome, three probable antigenic proteins (lytic polysaccharide monooxygenase, siderophore ABC transporter substrate-binding protein, and lytic polysaccharide monooxygenase) were shortlisted for epitope prediction. Among predicted epitopes, ten epitopes-GPADGRIAS, TTINHGGAQA, SERTALSVTT, GDGGNGGGEV, GIKEPDLEK, KQADDRIEA, QAIGGDTSN, EPLDEQTASR, AQWEPQSIEA, QPLKFSDFEL-were selected for multi-epitope vaccine construct designing. The screened B- and T-cell epitopes were joined with each other via specific linkers and linked to the cholera toxin B subunit as an adjuvant to enhance vaccine immune protection efficacy. The designed vaccine construct induced cellular and humoral immune responses. Blind docking with immune cell receptors, followed by molecular dynamic simulation results confirms the good binding potency and stability of the vaccine in providing protection against the pathogen.

Immunomodulatory peptides-A promising source for novel functional food production and drug discovery

Immunomodulatory peptides are a complex class of bioactive peptides that encompasses substances with different mechanisms of action. Immunomodulatory peptides could also be used in vaccines as adjuvants which would be extremely desirable, especially in response to pandemics. Thus, immunomodulatory peptides in food of plant origin could be regarded both as valuable suplements of novel functional food preparation and/or as precursors or possible active ingredients for drugs design for treatment variety of conditions arising from impaired function of immune system. Given variety of mechanisms, different tests are required to assess effects of immunomodulatory peptides. Some of those effects show good correlation with in vivo results but others, less so. Certain plant peptides, such as defensins, show both immunomodulatory and antimicrobial effect, which makes them interesting candidates for preparation of functional food and feed, as well as templates for design of synthetic peptides.

Immunoinformatics Analysis of SARS-CoV-2 ORF1ab Polyproteins to Identify Promiscuous and Highly Conserved T-Cell Epitopes to Formulate Vaccine for Indonesia and the World Population

SARS-CoV-2 and its variants caused the COVID-19 pandemic. Vaccines that target conserved regions of SARS-CoV-2 and stimulate protective T-cell responses are important for reducing symptoms and limiting the infection. Seven cytotoxic (CTL) and five helper T-cells (HTL) epitopes from ORF1ab were identified using NetCTLpan and NetMHCIIpan algorithms, respectively. These epitopes were generated from ORF1ab regions that are evolutionary stable as reflected by zero Shannon's entropy and are presented by 56 human leukocyte antigen (HLA) Class I and 22 HLA Class II, ensuring good coverage for the Indonesian and world population. Having fulfilled other criteria such as immunogenicity, IFNγ inducing ability, and non-homology to human and microbiome peptides, the epitopes were assembled into a vaccine construct (VC) together with β-defensin as adjuvant and appropriate linkers. The VC was shown to have good physicochemical characteristics and capability of inducing CTL as well as HTL responses, which stem from the engagement of the vaccine with toll-like receptor 4 (TLR4) as revealed by docking simulations. The most promiscuous peptide 899WSMATYYLF907 was shown via docking simulation to interact well with HLA-A*24:07, the most predominant allele in Indonesia. The data presented here will contribute to the in vitro study of T-cell epitope mapping and vaccine design in Indonesia.

Designing of a Multi-epitope Vaccine against the Structural Proteins of Marburg Virus Exploiting the Immunoinformatics Approach

Marburg virus disease (MVD) caused by the Marburg virus (MARV) generally appears with flu-like symptoms and leads to severe hemorrhagic fever. It spreads via direct contact with infected individuals or animals. Despite being considered to be less threatening in terms of appearances and the number of infected patients, the high fatality rate of this pathogenic virus is a major concern. Until now, no vaccine has been developed to combat this deadly virus. Therefore, vaccination for this virus is necessary to reduce its mortality. Our current investigation focuses on the design and formulation of a multi-epitope vaccine based on the structural proteins of MARV employing immunoinformatics approaches. The screening of potential T-cell and B-cell epitopes from the seven structural proteins of MARV was carried out through specific selection parameters. Afterward, we compiled the shortlisted epitopes by attaching them to an appropriate adjuvant and linkers. Population coverage analysis, conservancy analysis, and MHC cluster analysis of the shortlisted epitopes were satisfactory. Importantly, physicochemical characteristics, human homology assessment, and structure validation of the vaccine construct delineated convenient outcomes. We implemented disulfide bond engineering to stabilize the tertiary or quaternary interactions. Furthermore, stability and physical movements of the vaccine protein were explored using normal-mode analysis. The immune simulation study of the vaccine complexes also exhibited significant results. Additionally, the protein-protein docking and molecular dynamics simulation of the final construct exhibited a higher affinity toward toll-like receptor-4 (TLR4). From simulation trajectories, multiple descriptors, namely, root mean square deviations (rmsd), radius of gyration (Rg), root mean square fluctuations (RMSF), solvent-accessible surface area (SASA), and hydrogen bonds, have been taken into account to demonstrate the inflexible and rigid nature of receptor molecules and the constructed vaccine. Inclusively, our findings suggested the vaccine constructs' ability to regulate promising immune responses against MARV pathogenesis.

A Computational Reverse Vaccinology Approach for the Design and Development of Multi-Epitopic Vaccine Against Avian Pathogen Mycoplasma gallisepticum

Avian mycoplasma is a bacterial disease causing chronic respiratory disease (CRD) in poultry industries with high economic losses. The eradication of this disease still remains as a challenge. A multi-epitope prophylactic vaccine aiming the antigenic proteins of Mycoplasma gallisepticum can be a capable candidate to eradicate this infection. The present study is focused to design a multi-epitope vaccine candidate consisting of cytotoxic T-cell (CTL), helper T-cell (HTL), and B-cell epitopes of antigenic proteins, using immunoinformatics strategies. The multi-epitopic vaccine was designed, and its tertiary model was predcited, which was further refined and validated by computational tools. After initial validation, molecular docking was performed between multi-epitope vaccine construct and chicken TLR-2 and 5 receptors, which predicted effective binding. The in silico results specify the structural stability, precise specificity, and immunogenic response of the designed multi-epitope vaccine, and it could be an appropriate vaccine candidate for the M. gallisepticum infection.

In silico vaccine design and epitope mapping of New Delhi metallo-beta-lactamase (NDM): an immunoinformatics approach

BACKGROUND

Antibiotic resistance is a global health crisis. The adage that "prevention is better than cure" is especially true regarding antibiotic resistance because the resistance appears and spreads much faster than the production of new antibiotics. Vaccination is an important strategy to fight infectious agents; however, this strategy has not attracted sufficient attention in antibiotic resistance prevention. New Delhi metallo-beta-lactamase (NDM) confers resistance to many beta-lactamases, including important carbapenems like imipenem. Our goal in this study is to use an immunoinformatics approach to develop a vaccine that can elicit strong and specific immune responses against NDMs that prevent the development of antibiotic-resistant bacteria.

RESULTS

In this study, 2194 NDM sequences were aligned to obtain a conserved sequence. One continuous B cell epitope and three T cell CD4+ epitopes were selected from NDMs conserved sequence. Epitope conservancy for B cell and HLA-DR, HLA-DQ, and HLA-DP epitopes was 100.00%, 99.82%, 99.41%, and 99.86%, respectively, and population coverage of MHC II epitopes for the world was 99.91%. Permutation of the four epitope fragments resulted in 24 different peptides, of which 6 peptides were selected after toxicity, allergenicity, and antigenicity assessment. After primary vaccine design, only one vaccine sequence with the highest similarity with discontinuous B cell epitope in NDMs was selected. The final vaccine can bind to various Toll-like receptors (TLRs). The prediction implied that the vaccine would be stable with a good half-life. An immune simulation performed by the C-IMMSIM server predicted that two doses of vaccine injection can induce a strong immune response to NDMs. Finally, the GC-Content of the vaccine was designed very similar to E. coli K12.

CONCLUSIONS

In this study, immunoinformatics strategies were used to design a vaccine against different NDM variants that could produce an effective immune response against this antibiotic-resistant factor.