From ZikV genome to vaccine: in silico approach for the epitope-based peptide vaccine against Zika virus envelope glycoprotein

Rational design of B-cell and T-cell multi epitope-based vaccine against Zika virus, an in silico study

The Zika virus (ZKV) is a single-stranded positive-sense, enveloped RNA virus. Zika infection during pregnancy can cause congenital microcephaly, Guillain-Barré syndrome, miscarriage, and other CNS abnormalities. The world needs safe and effective vaccinations to fight against ZIKV infection since vaccination is generally regarded as one of the most effective ways to prevent infectious diseases. In the present work, we used immunoinformatics and docking studies to construct a vaccine containing multi-epitopes using the structural and non-structural proteins of ZKV. The structural models of ZKV proteins (PrE, PrM, NS1, and NS2A) were constructed using Pyre2 and RaptorX servers. The epitopes of B-cell, T-cell (HTL and CTL), and IFN-γ were predicted, and each epitope's immunogenic nature and physiochemical properties were confirmed. As an adjuvant, the CPG-Oligodeoxynucleotide, an agonist of Toll-like receptor 9 (TLR9), is associated to cytotoxic T-lymphocytes (CTL) epitopes via PAPAP linker. To assess the binding affinity and the tendency of the designed vaccine to induce an immune response through TLR9, molecular docking was done. In the next step, molecular dynamics (MD) simulation to 100 nanoseconds (ns) was used to evaluate the stability of the interaction of the designed vaccine with TLR9. The designed vaccine is predicted to be highly antigenic, non-toxic, soluble, and stable with low flexibility in MD simulation. MD studies indicated that the finalized vaccine-TLR9 docked complex was stable during simulation time. The vaccine construct is able to stimulate both humoral and cellular immune responses. We suppose that our constructed model of the vaccine may have the ability to induce the host immune response against ZKV. Further studies, including in vitro and in vivo experimental analyses, are needed to prove the constructed vaccine's efficacy with multi-epitopes.Communicated by Ramaswamy H. Sarma.

Enhanced immunogenicity and protective efficacy in mice following a Zika DNA vaccine designed by modulation of membrane-anchoring regions and its association to adjuvants

Zika virus (ZIKV) is a re-emerging pathogen with high morbidity associated to congenital infection. Despite the scientific advances since the last outbreak in the Americas, there are no approved specific treatment or vaccines. As the development of an effective prophylactic approach remains unaddressed, DNA vaccines surge as a powerful and attractive candidate due to the efficacy of sequence optimization in achieving strong immune response. In this study, we developed four DNA vaccine constructs encoding the ZIKV prM/M (pre-membrane/membrane) and E (envelope) proteins in conjunction with molecular adjuvants. The DNA vaccine candidate (called ZK_ΔSTP), where the entire membrane-anchoring regions were completely removed, was far more immunogenic compared to their counterparts. Furthermore, inclusion of the tPA-SP leader sequence led to high expression and secretion of the target vaccine antigens, therefore contributing to adequate B cell stimulation. The ZK_ΔSTP vaccine induced high cellular and humoral response in C57BL/6 adult mice, which included high neutralizing antibody titers and the generation of germinal center B cells. Administration of ZK-ΔSTP incorporating aluminum hydroxide (Alum) adjuvant led to sustained neutralizing response. In consistency with the high and long-term protective response, ZK_ΔSTP+Alum protected adult mice upon viral challenge. Collectively, the ZK_ΔSTP+Alum vaccine formulation advances the understanding of the requirements for a successful and protective vaccine against flaviviruses and is worthy of further translational studies.

CRM197-conjugated peptides vaccine of HCMV pp65 and gH induce maturation of DC and effective viral-specific T cell responses

Human cytomegalovirus (HCMV) infection is prevalent worldwide, and there is currently no licenced HCMV vaccine to control it. Therefore, developing an effective HCMV vaccine is a significant priority. Because of their excellent immunogenicity, the crucial components of HCMV, phosphoprotein 65 (pp65) and glycoproteins H (gH) are potential target proteins for HCMV vaccine design. In this study, we predicted and screened the dominant antigenic epitopes of B and T cells from pp65 and gH conjugated with the carrier protein cross-reacting material 197 (CRM197) to form three peptide-CRM197 vaccines (pp65-CRM197, gH-CRM197, and pp65-CRM197+gH-CRM197). Furthermore, the immunogenicity of the peptide-CRM197 vaccines and their effects on dendritic cells (DCs) were explored. The results showed that three peptide-CRM197 vaccines could induce maturation of DCs through the p38 MAPK signalling pathway and promote the release of proinflammatory factors, such as TNF-α and interleukin (IL) -6. Meanwhile, the peptide-CRM197 vaccines could effectively activate T cell and humoral immunity, which were far better than the inactivated HCMV vaccine. In conclusion, we constructed three peptide-CRM197 vaccines, which could induce multiple immune effects, providing a novel approach for HCMV vaccine design.

Computational prediction of B and T-cell epitopes of Kyasanur Forest Disease virus marker proteins towards the development of precise diagnosis and potent subunit vaccine

Kyasanur Forest Disease (KFD), also known as 'monkey fever', caused by KFD Virus (KFDV), is a highly neglected tropical disease endemic to Western Ghat region of Karnataka, India. Recently, KFD, which is fatal for both monkeys and humans with a mortality rate of 2-10% has been found to spread from its epicenter to neighboring districts and states also. The current ELISA based KFD detection method is very non-specific due to cross-reactivity with other flaviviruses. Further, presently available formalin-inactivated vaccine has been found to be less effective leading to disease susceptibility and severity. To address these, the present study was aimed at predicting the potent specific B and T-cell epitopes of KFDV immunogenic marker proteins using diverse computational tools aiming at developing precise diagnostic method and an effective subunit vaccine. Here, we have chosen E, NS1 and NS5 proteins as markers of KFDV by taking into account of their differential and non-overlapping sequences with selected arboviruses. Based on the linear and nonlinear epitope prediction tools and important biophysical parameters, we identified three potential linear and ten nonlinear B-cell epitopes. We also predicted T-cell epitope peptides which binds to MHC class-I and class-II receptors for the effective T-cell activation. Thus, our molecular docking and molecular dynamics simulation analysis has identified six different TH-cell epitopes based on the distribution frequency of MHC-II haplotypes in the human population and one TC-cell epitope from NS5 protein that has maximum interaction with class-I MHC. Overall, we have successfully identified potential B and T-cell epitope marker peptides present in the envelope and two non-structural proteins.Communicated by Ramaswamy H. Sarma.

B-cell epitope discovery: The first protein flexibility-based algorithm-Zika virus conserved epitope demonstration

Antibody-antigen interaction-at antigenic local environments called B-cell epitopes-is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-nine enveloped virus-antibody structures), 2) two structurally characterized non-enveloped virus epitopes which evolved slowly (out of eight non-enveloped virus-antibody structures), and 3) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of five Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.

Immunoinformatics Vaccine Design for Zika Virus

Zika virus (ZIKV) is an emerging virus from the Flaviviridae family and Flavivirus genus that has caused important outbreaks around the world. ZIKV infection is associated with severe neuropathology in newborns and adults. Until now, there is no licensed vaccine available for ZIKV infection. Therefore, the development of a safe and effective vaccine against ZIKV is an urgent need. Recently, we designed an in silico multi-epitope vaccine for ZIKV based on immunoinformatics tools. To construct this in silico ZIKV vaccine, we used a consensus sequence generated from ZIKV sequences available in databank. Then, we selected CD4+ and CD8+ T cell epitopes from all ZIKV proteins based on the binding prediction to class II and class I human leukocyte antigen (HLA) molecules, promiscuity, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the construct and B cell epitopes were identified. Adjuvants were associated to increase immunogenicity. Distinct linkers were used for connecting the CD4+ and CD8+ T cell epitopes, EDIII, and adjuvants. Several analyses, such as antigenicity, population coverage, allergenicity, autoimmunity, and secondary and tertiary structures of the vaccine, were evaluated using various immunoinformatics tools and online web servers. In this chapter, we present the protocols with the rationale and detailed steps needed for this in silico multi-epitope ZIKV vaccine design.

Alpinetin Suppresses Zika Virus-Induced Interleukin-1β Production and Secretion in Human Macrophages

Zika virus (ZIKV) infection has been recognized to cause adverse sequelae in the developing fetus. Specially, this virus activates the excessive release of IL-1β causing inflammation and altered physiological functions in multiple organs. Although many attempts have been invested to develop vaccine, antiviral, and antibody therapies, development of agents focusing on limiting ZIKV-induced IL-1β release have not gained much attention. We aimed to study the effects of alpinetin (AP) on IL-1β production in human macrophage upon exposure to ZIKV. Our study demonstrated that ZIKV stimulated IL-1β release in the culture supernatant of ZIKV-infected cells, and AP could effectively reduce the level of this cytokine. AP exhibited no virucidal activities against ZIKV nor caused alteration in viral production. Instead, AP greatly inhibited intracellular IL-1β synthesis. Surprisingly, this compound did not inhibit ZIKV-induced activation of NF-κB and its nuclear translocation. However, AP could significantly inhibit ZIKV-induced p38 MAPK activation without affecting the phosphorylation status of ERK1/2 and JNK. These observations suggest the possibility that AP may reduce IL-1β production, in part, through suppressing p38 MAPK signaling. Our current study sheds light on the possibility of using AP as an alternative agent for treating complications caused by ZIKV infection-induced IL-1β secretion.

Designing a multi-epitope vaccine against coxsackievirus B based on immunoinformatics approaches

Coxsackievirus B (CVB) is one of the major viral pathogens of human myocarditis and cardiomyopathy without any effective preventive measures; therefore, it is necessary to develop a safe and efficacious vaccine against CVB. Immunoinformatics methods are both economical and convenient as in-silico simulations can shorten the development time. Herein, we design a novel multi-epitope vaccine for the prevention of CVB by using immunoinformatics methods. With the help of advanced immunoinformatics approaches, we predicted different B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes, respectively. Subsequently, we constructed the multi-epitope vaccine by fusing all conserved epitopes with appropriate linkers and adjuvants. The final vaccine was found to be antigenic, non-allergenic, and stable. The 3D structure of the vaccine was then predicted, refined, and evaluated. Molecular docking and dynamics simulation were performed to reveal the interactions between the vaccine with the immune receptors MHC-I, MHC-II, TLR3, and TLR4. Finally, to ensure the complete expression of the vaccine protein, the sequence of the designed vaccine was optimized and further performed in-silico cloning. In conclusion, the molecule designed in this study could be considered a potential vaccine against CVB infection and needed further experiments to evaluate its safety and efficacy.

Current Advances in Zika Vaccine Development

Zika virus (ZIKV), an emerging arthropod-borne flavivirus, was first isolated in Uganda in 1947 from monkeys and first detected in humans in Nigeria in 1952; it has been associated with a dramatic burden worldwide. Since then, interventions to reduce the burden of ZIKV infection have been mainly restricted to mosquito control, which in the end proved to be insufficient by itself. Hence, the situation prompted scientists to increase research on antivirals and vaccines against the virus. These efforts are still ongoing as the pathogenesis and immune evasion mechanisms of ZIKV have not yet been fully elucidated. Understanding the viral disease mechanism will provide a better landscape to develop prophylactic and therapeutic strategies against ZIKV. Currently, no specific vaccines or drugs have been approved for ZIKV. However, some are undergoing clinical trials. Notably, different platforms have been evaluated for the design of vaccines, including DNA, mRNA, viral vectors, virus-like particles (VLPs), inactivated virus, live attenuated virus, peptide and protein-based vaccines, passive immunizations by using monoclonal antibodies (MAbs), and vaccines that target vector-derived antigens. These vaccines have been shown to induce specific humoral and cellular immune responses and reduce viremia and viral RNA titers, both in vitro and in vivo. This review provides a comprehensive summary of current advancements in the development of vaccines against Zika virus.

In silico construction of a multiepitope Zika virus vaccine using immunoinformatics tools

Zika virus (ZIKV) is an arbovirus from the Flaviviridae family and Flavivirus genus. Neurological events have been associated with ZIKV-infected individuals, such as Guillain-Barré syndrome, an autoimmune acute neuropathy that causes nerve demyelination and can induce paralysis. With the increase of ZIKV infection incidence in 2015, malformation and microcephaly cases in newborns have grown considerably, which suggested congenital transmission. Therefore, the development of an effective vaccine against ZIKV became an urgent need. Live attenuated vaccines present some theoretical risks for administration in pregnant women. Thus, we developed an in silico multiepitope vaccine against ZIKV. All structural and non-structural proteins were investigated using immunoinformatics tools designed for the prediction of CD4 + and CD8 + T cell epitopes. We selected 13 CD8 + and 12 CD4 + T cell epitopes considering parameters such as binding affinity to HLA class I and II molecules, promiscuity based on the number of different HLA alleles that bind to the epitopes, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the vaccine construct, creating a hybrid protein domain-multiepitope vaccine. Three high scoring continuous and two discontinuous B cell epitopes were found in EDIII. Aiming to increase the candidate vaccine antigenicity even further, we tested secondary and tertiary structures and physicochemical parameters of the vaccine conjugated to four different protein adjuvants: flagellin, 50S ribosomal protein L7/L12, heparin-binding hemagglutinin, or RS09 synthetic peptide. The addition of the flagellin adjuvant increased the vaccine's predicted antigenicity. In silico predictions revealed that the protein is a probable antigen, non-allergenic and predicted to be stable. The vaccine’s average population coverage is estimated to be 87.86%, which indicates it can be administered worldwide. Peripheral Blood Mononuclear Cells (PBMC) of individuals with previous ZIKV infection were tested for cytokine production in response to the pool of CD4 and CD8 ZIKV peptide selected. CD4 + and CD8 + T cells showed significant production of IFN-γ upon stimulation and IL-2 production was also detected by CD8 + T cells, which indicated the potential of our peptides to be recognized by specific T cells and induce immune response. In conclusion, we developed an in silico universal vaccine predicted to induce broad and high-coverage cellular and humoral immune responses against ZIKV, which can be a good candidate for posterior in vivo validation.

Data processing algorithms for the in silico SARS-CoV-2 epitope prediction and vaccine development

Based on literature analysis and own bioinformatics and virology research experience, authors propose multistep data processing algorithms, designed for the objectives of assisting the SARS-CoV-2 epitope vaccine production. Epitope vaccines are expected to provoke a weaker but safer response of the vaccinated person. Methodologies of reverse bioengineering, vaccinology and synthetic peptide manufacturing have a promising future to combat COVID-19 brutal disease. The significant mutational variability and evolution of the SARS-CoV-2, which is more typical for natural animal-borne viruses, are the hurdle for the effective and robust vaccine application and therefore require multidisciplinary research and prevention measures on the international level of cooperation. However, we can expect that other viruses with different nature and content may be labelled as SARS-CoV-2. In this case metagenomics is an important discipline for COVID-19 discovery. High quality reliable virus detection is still an unresolved question for improvement and optimization. It is of upmost importance to develop the in silico and in vitro methods for the vaccine recipient reaction prediction and monitoring as techniques of the so-called modern personalized medicine. Many questions can`t be solved applying exclusively in silico techniques and only can be discovered in vitro and in vivo, demanding significant time and money investments. Future experiments also should be directed at the discovery of optimal vaccine adjuvants, vectors and epitope ensembles, as well as the personal characteristics of citizens of a certain region. This research would require several more years of meticulous large-scale laboratory and clinical work in various centers of biomedical institutions worldwide

Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus

Dengue poses a global health threat, which will persist without therapeutic intervention. Immunity induced by exposure to one serotype does not confer long-term protection against secondary infection with other serotypes and is potentially capable of enhancing this infection. Although vaccination is believed to induce durable and protective responses against all the dengue virus (DENV) serotypes in order to reduce the burden posed by this virus, the development of a safe and efficacious vaccine remains a challenge. Immunoinformatics and computational vaccinology have been utilized in studies of infectious diseases to provide insight into the host-pathogen interactions thus justifying their use in vaccine development. Since vaccination is the best bet to reduce the burden posed by DENV, this study is aimed at developing a multi-epitope based vaccines for dengue control. Combined approaches of reverse vaccinology and immunoinformatics were utilized to design multi-epitope based vaccine from the sequence of DENV. Specifically, BCPreds and IEDB servers were used to predict the B-cell and T-cell epitopes, respectively. Molecular docking was carried out using Schrödinger, PATCHDOCK and FIREDOCK. Codon optimization and in silico cloning were done using JCAT and SnapGene respectively. Finally, the efficiency and stability of the designed vaccines were assessed by an in silico immune simulation and molecular dynamic simulation, respectively. The predicted epitopes were prioritized using in-house criteria. Four candidate vaccines (DV-1-4) were designed using suitable adjuvant and linkers in addition to the shortlisted epitopes. The binding interactions of these vaccines against the receptors TLR-2, TLR-4, MHC-1 and MHC-2 show that these candidate vaccines perfectly fit into the binding domains of the receptors. In addition, DV-1 has a better binding energies of - 60.07, - 63.40, - 69.89 kcal/mol against MHC-1, TLR-2, and TLR-4, with respect to the other vaccines. All the designed vaccines were highly antigenic, soluble, non-allergenic, non-toxic, flexible, and topologically assessable. The immune simulation analysis showed that DV-1 may elicit specific immune response against dengue virus. Moreover, codon optimization and in silico cloning validated the expressions of all the designed vaccines in E. coli. Finally, the molecular dynamic study shows that DV-1 is stable with minimum RMSF against TLR4. Immunoinformatics tools are now applied to screen genomes of interest for possible vaccine target. The designed vaccine candidates may be further experimentally investigated as potential vaccines capable of providing definitive preventive measure against dengue virus infection.

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

Multi-epitope based subunit vaccine construction against Banna virus targeting on two outer proteins (VP4 and VP9): A computational approach

Recently, RNA viruses have gained a mammoth concern for causing various outbreaks, and due to pandemics, they are acquiring additional attention throughout the world. An emerging RNA as well as vector-borne Banna Virus (BAV) is a human pathogen resulting in encephalitis, fever, headache, muscle aches, and severe coma. Besides human, pathogenic BAV was also detected from pigs, cattle, ticks, midges, and mosquitoes in Indonesia, China, and Vietnam. Due to high mutation tendency and dearth of a species barrier, this virus will consider as a significant threat in the near future throughout the planet, particularly in Africa. Despite of severe human case fatalities in several countries, there are no specific therapeutics, available vaccines, and other preventive measures against BAV. Thus, to find out the effective therapeutics and preventive strategies are crying exigency. In the present study, a unique multi-epitope-based peptide vaccine candidate is constructed using bioinformatics' tools that efficiently instigate immune cells for generating BAV antibodies. The potential vaccine candidates were developed using both T and B -cell epitopes. UniprotKB database was used to retrieve of two outer proteins (VP9 and VP4), and homologous sequences of BAV taxid: 7763, 649,604, 77,763, and 8453 were searched by NCBI BLAST. These serotypes are the most closely associated with the disease. Then combining the best-selected epitopes in various combinations with different adjuvants, three distinct vaccine candidates were formed. The validity tests were performed for the screened vaccine candidate regarding stability, allergenicity, and antigenicity parameters. Moreover, molecular dynamic simulations of the selected vaccine with TLR-8 immune receptor confirmed the stability of the binding pose and showed a significant response to immune cells. Thus, the results established that the designed chimeric peptide vaccine could enhance the immune response against BAV.

Integrative immunoinformatics paradigm for predicting potential B-cell and T-cell epitopes as viable candidates for subunit vaccine design against COVID-19 virulence

Background

The increase in global mortality rates from SARS-COV2 (COVID-19) infection has been alarming thereby necessitating the continual search for viable therapeutic interventions. Due to minimal microbial components, subunit (peptide-based) vaccines have demonstrated improved efficacies in stimulating immunogenic responses by host B- and T-cells.

Methods

Integrative immunoinformatics algorithms were used to determine linear and discontinuous B-cell epitopes from the S-glycoprotein sequence. End-point selection of the most potential B-cell epitope was based on highly essential physicochemical attributes. NetCTL-I and NetMHC-II algorithms were used to predict probable MHC-I and II T-cell epitopes for globally frequent HLA-A∗O2:01, HLA-B∗35:01, HLA-B∗51:01 and HLA-DRB1∗15:02 molecules. Highly probable T-cell epitopes were selected based on their high propensities for C-terminal cleavage, transport protein (TAP) processing and MHC-I/II binding.

Results

Preferential epitope binding sites were further identified on the HLA molecules using a blind peptide-docking method. Phylogenetic analysis revealed close relativity between SARS-CoV-2 and SARS-CoV S-protein. LALHRSYLTPGDSSSGWTAGAA_242→263 was the most probable B-cell epitope with optimal physicochemical attributes. MHC-I antigenic presentation pathway was highly favourable for YLQPRTFLL_269-277 (HLA-A∗02:01), LPPAYTNSF_24-32 (HLA-B∗35:01) and IPTNFTISV7_14-721 (HLA-B∗51:01). Also, LTDEMIAQYTSALLA_865-881 exhibited the highest binding affinity to HLA-DR B1∗15:01 with core interactions mediated by IAQYTSALL_870-878. COVID-19 YLQPRTFLL_269-277 was preferentially bound to a previously undefined site on HLA-A∗02:01 suggestive of a novel site for MHC-I-mediated T-cell stimulation.

Conclusion

This study implemented combinatorial immunoinformatics methods to model B- and T-cell epitopes with high potentials to trigger immunogenic responses to the S protein of SARS-CoV-2.

In Silico study of Rosmarinic Acid Derivatives as Novel Insulin Fibril Inhibitors

The self-assembly of human insulin (HI) plays a crucial role in regulating amyloid fibrils. Therefore, it is a significant problem for the medical management of diabetes therapy and these findings have led us to investigate the amyloid formation and its inhibition. Few potential inhibitors have been identified to inhibit amyloid fibrils. Rosmarinic acid (RA) is one of the things that inhibits amyloid formation completely by increasing the resistivity of the amyloidogenic insulin (dimer) protein to thermal unfolding. Here, we choose different tested derivative compounds for designing amyloid inhibitors by substituting various functional groups of RA. These derivative compounds were subjected to in silico studies to determine the best drug candidates. In comparison to RA, 14 molecules have higher binding affinity and interactions with the target receptor. After frontier molecular orbitals study, ADME and toxicity analysis, the eight best compounds may act as the best inhibitors. The stability of the docked complexes was visualized by molecular dynamics (MD) simulations. This finding opens a new proposal to explore future studies with these best compounds to increase the thermal stability of the insulin dimers.

Molecular binding interaction of pyridinium based gemini surfactants with bovine serum albumin: Insights from physicochemical, multispectroscopic, and computational analysis

To study the interaction of the series of pyridinium based gemini surfactants (GS) (referred to as m-Py-m, m = 14, 16); 4,4'-(propane-1,3-diyl)bis(1-(2-(tetradecyloxy)-2-oxoethyl) dipyridinium chloride (14-Py-14), and 4,4'-(propane-1,3-diyl) bis(1-(2-(hexadecyloxy)-2-oxoethyl)dipyridinium chloride (16-Py-16) with bovine serum albumin (BSA), various physicochemical and spectroscopic tools such as tensiometry, steady-state fluorescence, synchronous fluorescence, pyrene fluorescence, UV-visible, far-UV circular dichroism (CD) were utilized at physiological pH (7.4) and 298 K in combination with computational molecular modeling analysis. The tensiometric results show significant modifications in interfacial and thermodynamic parameters for m-Py-m GS upon BSA combination, deciphering the gemini surfactant-BSA interaction. Steady-state fluorescence analysis evaluates the structural alterations of BSA with the addition of m-Py-m GS. The plots of Stern-Volmer, modified Stern-Volmer, and thermodynamic parameters were used to determine the binding type of m-Py-m GS to BSA. The synchronous fluorescence spectra state a mild effect of gemini surfactants on the emission intensity of tyrosine (Tyr) residues, on the other hand, tryptophan (Trp) residues showed a significant effect. Post addition of GS, the plot of pyrene fluorescence reveals the mild micropolarity fluctuations via the probe (pyrene) molecules encapsulated in BSA. UV-visible experiments support the complex formation between the BSA and m-Py-m GS. Far-UV CD measurements revealed the modifications in the secondary structure of protein produced by m-Py-m GS. Furthermore, we also used the computational molecular modeling for attaining deep insight into BSA and m-Py-m GS binding and the results are supported with our experimental results.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

Evaluation of potential MHC-I allele-specific epitopes in Zika virus proteins and the effects of mutations on peptide-MHC-I interaction studied using in silico approaches

Zika virus (ZIKV) infection is a global health concern due to its association with microcephaly and neurological complications. The development of a T-cell vaccine is important to combat this disease. In this study, we propose ZIKV major histocompatibility complex I (MHC-I) epitopes based on in silico screening consensus followed by molecular docking, PRODIGY, and molecular dynamics (MD) simulation analyses. The effects of the reported mutations on peptide-MHC-I (pMHC-I) complexes were also evaluated. In general, our data indicate an allele-specific peptide-binding human leukocyte antigen (HLA) and potential epitopes. For HLA-B44, we showed that the absence of acidic residue Glu at P2, due to the loss of the electrostatic interaction with Lys45, has a negative impact on the pMHC-I complex stability and explains the low free energy estimated for the immunodominant peptide E-4 (IGVSNRDFV). Our MD data also suggest the deleterious effects of acidic residue Asp at P1 on the pMHC-I stability of HLA-B8 due to destabilization of the α-helix and β-strand. Free energy estimation further indicated that the mutation from Val to Ala at P9 of peptide E-247 (DAHAKRQTV), which was found exclusively in microcephaly samples, did not reduce HLA-B8 affinity. In contrast, the mutation from Thr to Pro at P2 of the peptide NS5-832 (VTKWTDIPY) decreased the interaction energy, number of intermolecular interactions, and adversely affected its binding mode with HLA-A1. Overall, our findings are important with regard to the design of T-cell peptide vaccines and for understanding how ZIKV escapes recognition by CD8 + T-cells.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening

BACKGROUND

Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2.

METHODS

In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis.

RESULTS

The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant.

CONCLUSION

Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Immunoinformatics prediction of overlapping CD8+ T-cell, IFN-γ and IL-4 inducer CD4+ T-cell and linear B-cell epitopes based vaccines against COVID-19 (SARS-CoV-2)

At the beginning of the year 2020, the world was struck with a global pandemic virus referred to as SARS-CoV-2 (COVID-19) which has left hundreds of thousands of people dead. To control this virus, vaccine design becomes imperative. In this study, potential epitopes-based vaccine candidates were explored. Six hundred (600) genomes of SARS-CoV-2 were retrieved from the viPR database to generate CD8⁺ T-cell, CD4+ T-cell and linear B-cell epitopes which were screened for antigenicity, immunogenicity and non-allergenicity. The results of this study provide 19 promising candidate CD8⁺ T-cell epitopes that strongly overlap with 8 promising B-cells epitopes. Another 19 CD4⁺ T-cell epitopes were also identified that can induce IFN-γ and IL-4 cytokines. The most conserved MHC-I and MHC-II for both CD8⁺ and CD4⁺ T-cell epitopes are HLA-A*02:06 and HLA-DRB1*01:01 respectively. These epitopes also bound to Toll-like receptor 3 (TLR3). The population coverage of the conserved Major Histocompatibility Complex Human Leukocyte Antigen (HLA) for both CD8⁺ T-cell and CD4⁺ T-cell ranged from 65.6% to 100%. The detailed analysis of the potential epitope-based vaccine and their mapping to the complete COVID-19 genome reveals that they are predominantly found in the location of the surface (S) and membrane (M) glycoproteins suggesting the potential involvement of these structural proteins in the immunogenic response and antigenicity of the virus. Since the majority of the potential epitopes are located on M protein, the design of multi-epitope vaccine with the structural protein is highly promising though the whole M protein could also serve as a viable epitope for the development of an attenuated vaccine. Our findings provide a baseline for the experimental design of a suitable vaccine against SARS-CoV-2.

Molecular docking simulation and in vitro studies on estrogenic activities of flavonoids from leaves of Carya cathayensis Sarg

The main purpose of this study was to evaluate the estrogenic properties of total flavonoids (TFs) and five flavonoid monomers (cardamonin (Car), pinostrobin chalcone (PC), wogonin (Wo), chrysin (Chr) and Pinocembrin (PI)) from leaves of Carya cathayensis Sarg (LCC). TFs from LCC were isolated and determined using HPLC. The 3-[4,5-dimethylthiazole-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay and flow cytometry were performed to assess the effects of flavonoids on cell proliferation and cell cycle, respectively. The molecular docking technique was applied to investigate binding conformations of the monomers from LCC to the estrogen receptor ERα and ERβ. Gene and protein expression patterns were assessed using quantitative real-time PCR (qRT-PCR) and western blot, respectively. The results showed that TFs, Car, PC, Wo and Chr promoted proliferation of MCF-7 cells and cell transition from the G1 to S phase, and inhabitation of MCF-7 cell proliferation was observed after the treatment of PI. Molecular docking studies confirmed ERs as molecular targets for the monomers. TFs, Car, PC, Wo and Chr from LCC promoted gene expression of ERα, ERβ, progesterone receptor (PR) and pS2. Our collective results demonstrated that TFs and monomers from LCC may exert ER agonist activity through competitively bind to ER, inducing ER upregulation and active ER to estrogen response element (ERE)- independent gene regulation. As an abundant natural product, LCC may provide a novel medicinal source for treatment of diseases caused by estrogen deficiency.

Determine the Potential Epitope Based Peptide Vaccine Against Novel SARS-CoV-2 Targeting Structural Proteins Using Immunoinformatics Approaches

Coronaviruses (CoVs) belong to the Coronaviridae-family. The genus Beta-coronaviruses, are enveloped positive strand RNA viruses with club-like spikes at the surface with a unique replication process and a large RNA genome (∼25 kb). CoVs are known as one of the major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Recently, a new strain of coronavirus has been identified and named as SARS-CoV-2. A large number of COVID-19 (disease caused by SARS-CoV-2) cases are being diagnosed all over the World especially in China (Wuhan). COVID-19 showed high mortality rate exponentially, however, not even a single effective cure is being introduced yet against COVID-19. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against COVID-19 for the development of a coronavirus peptide vaccine. Cytotoxic T-lymphocyte (CTL) and B-cell epitopes were predicted for SARS-CoV-2 coronavirus structural proteins (Spikes, Membrane, Envelope, and Nucleocapsid). The docking complexes of the top 10 epitopes having antigenic sites were analyzed led by binding affinity and binding interactional analyses of top ranked predicted peptides with the MHC-I HLA molecule. The predicted peptides may have potential to be used as peptide vaccine against COVID-19.

Immunoinformatics-guided design of an epitope-based vaccine against severe acute respiratory syndrome coronavirus 2 spike glycoprotein

AIMS

With a large number of fatalities, coronavirus disease-2019 (COVID-19) has greatly affected human health worldwide. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes COVID-19. The World Health Organization has declared a global pandemic of this contagious disease. Researchers across the world are collaborating in a quest for remedies to combat this deadly virus. It has recently been demonstrated that the spike glycoprotein (SGP) of SARS-CoV-2 is the mediator by which the virus enters host cells.

MAIN METHODS

Our group comprehensibly analyzed the SGP of SARS-CoV-2 through multiple sequence analysis and a phylogenetic analysis. We predicted the strongest immunogenic epitopes of the SGP for both B cells and T cells.

KEY FINDINGS

We focused on predicting peptides that would bind major histocompatibility complex class I. Two optimal epitopes were identified, WTAGAAAYY and GAAAYYVGY. They interact with the HLA-B*15:01 allele, which was further validated by molecular docking simulation. This study also found that the selected epitopes are able to be recognized in a large percentage of the world's population. Furthermore, we predicted CD4+ T-cell epitopes and B-cell epitopes.

SIGNIFICANCE

Our study provides a strong basis for designing vaccine candidates against SARS-CoV-2. However, laboratory work is required to validate our theoretical results, which would lay the foundation for the appropriate vaccine manufacturing and testing processes.

Exploring membrane proteins of Leishmania major to design a new multi-epitope vaccine using immunoinformatics approach

Leishmaniasis is one of the major global endemic diseases. Among all the different forms of the disease, cutaneous Leishmaniasis has the highest prevalence worldwide. Treatment with current drugs has not had a significant effect on the improvement of the disease. An attempt to replace an appropriate vaccine that can stimulate host cellular immunity and induce the response of Major histocompatibility complex I (MHCI) and Major histocompatibility complex II (MHCII) against Leishmania is essential. Vaccine production remains a challenge despite the use of different antigens for vaccination against Leishmania major. Hence, we were used the immunoinformatics approach to design a new multi-epitope vaccine against L. major using immunogenic outer membrane proteins. Helper T-lymphocyte (HTL) and Cytotoxic T lymphocyte (CTL) epitopes were predicted and for final confirmation of the selected epitopes, docking analysis, and molecular dynamics simulation was performed. Then, GDGDG linker and profilin adjuvant were added to enhance the immunity of vaccines. The designed vaccine was evaluated in terms of molecular weight, PI, immunogenicity, and allergenicity. Moreover, the secondary and three-dimensional structure of the final construct was identified. In silico cloning approach was carried out to improve expression of the vaccine construct. Finally, molecular docking, followed by molecular dynamic was performed to determine the interaction between multi-epitope vaccine and TLR11. We hope that the designed vaccine can be a good candidate for the development of cutaneous leishmaniasis vaccine. but its effectiveness should be assessed in vivo.

Global transmission and evolutionary dynamics of the Chikungunya virus

Chikungunya virus (CHIKV) is a re-emerging pathogen of global importance. We attempted to gain an insight into the organisation, distribution and mutational load of the virus strains reported from different parts of the world. We describe transmission dynamics and genetic characterisation of CHIKV across the globe during the last 65 years from 1952 to 2017. The evolutionary pattern of CHIKV was analysed using the E1 protein gene through phylogenetic, Bayesian and Network methods with a dataset of 265 sequences from various countries. The time to most recent common ancestor of the virus was estimated to be 491 years ago with an evolutionary rate of 2.78 × 10-4 substitutions/site/year. Genetic characterisation of CHIKV strains was carried out in terms of variable sites, selection pressure and epitope mapping. The neutral selection pressure on the E1 gene of the virus suggested a stochastic process of evolution. We identified six potential epitope peptides in the E1 protein showing substantial interaction with human MHC-I and MHC-II alleles. The present study augments global epidemiological and population dynamics of CHIKV warranting undertaking of appropriate control measures. The identification of epitopic peptides can be useful in the development of epitope-based vaccine strategies against this re-emerging viral pathogen.

Maternal-Fetal Interplay in Zika Virus Infection and Adverse Perinatal Outcomes

During pregnancy, the organization of complex tolerance mechanisms occurs to assure non-rejection of the semiallogeneic fetus. Pregnancy is a period of vulnerability to some viral infections, mainly during the first and second trimesters, that may cause congenital damage to the fetus. Recently, Zika virus (ZIKV) infection has gained great notoriety due to the occurrence of congenital ZIKV syndrome, characterized by fetal microcephaly, which results from the ability of ZIKV to infect placental cells and neural precursors in the fetus. Importantly, in addition to the congenital effects, studies have shown that perinatal ZIKV infection causes a number of disorders, including maculopapular rash, conjunctivitis, and arthralgia. In this paper, we contextualize the immunological aspects involved in the maternal-fetal interface and vulnerability to ZIKV infection, especially the alterations resulting in perinatal outcomes. This highlights the need to develop protective maternal vaccine strategies or interventions that are capable of preventing fetal or even neonatal infection.

Both beta sheet breaker and alpha helix forming pentapeptide inhibits protein fibrillation: Implication for the treatment of amyloid disorders

For the first time, the effect of two novel designed pentapeptides on amyloid growth of human insulin using combined biophysical, microscopic, cell viability and computational approaches. Collective experimental data from ThT, ANS, and TEM demonstrate that in spite of having contrasting features, both peptides can effectively inhibit amyloid formation by prolonging lag phase, slowing down aggregation rate, and reducing final fibril formation (up to 84.26% and 85.24% by P1 and P7 respectively). Although pure amyloid caused profound cellular toxicity in SH-SY5Y neuronal cells, amyloid formed in the presence of peptides showed much reduced cellular toxicity. Such an inhibitory effect can be attributed to interference with the structural transition of insulin toward β-sheet structure by peptides. Furthermore, molecular dynamic simulations confirm that peptide preferentially binds to nearby region which is more prone to form aggregates that consequently disrupts self-assembly into amyloid fibrils (P1 and P7 possess inhibition constant value of 0.000183 and 0.000216 nm, respectively), supporting our experimental observations. This study underscores the information about the sequence based inhibition mechanism of peptides that might dictate their inhibition or modulation capacity, which might be helpful in designing anti-amyloid therapeutics.

Epitope-Based Vaccine Designing of Nocardia asteroides Targeting the Virulence Factor Mce-Family Protein by Immunoinformatics Approach

Nocardia asteroides is the main causative agent responsible for nocardiosis disease in immunocompromised patient viz. Acquired Immunodeficiency Syndrome (AIDS), malignancy, diabetic, organ recipient and genetic disorders. The virulence factor and outer membrane protein pertains immense contribution towards the designing of epitopic vaccine and limiting the robust outbreak of diseases. While epitopic based vaccine element carrying B and T cell epitope along with adjuvant is highly immunoprophylactic in nature. Present research equips immunoinformatics to figure out the suitable epitopes for effective vaccine designing. The selected epitopes VLGSSVQTA, VNIELKPEF and VVPSNLFAV amino acids sequence are identified by HLA-DRB alleles of both MHC class (MHC-I and II) molecules. Simultaneously, these also accessible to B-cell, confirmed through the ABCPred server. Antigenic property expression is validated by the Vaxijen antigenic prediction web portal. Molecular docking between the epitopes and T cell receptor delta chain authenticate the accurate interaction between epitope and receptor with significantly low binding energy. Easy access of epitopes to immune system also be concluded as transmembrane nature of the protein verified by using of TMHMM server. Appropriate structural identity of the virulence factor Mce-family protein generated through Phyre2 server and subsequently validated by ProSA and PROCHECK program suite. The structural configuration of theses epitopes also shaped using DISTILL web server. Both the structure of epitopes and protein will contribute a significant step in designing of epitopic vaccine against N. asteroides. Therefore, such immunoinformatics based computational drive definitely provides a conspicuous impel towards the development of epitopic vaccine as a promising remedy of nocardiosis.

Epitope Prediction by Novel Immunoinformatics Approach: A State-of-the-art Review

Immunoinformatics is a science that helps to create significant immunological information using bioinformatics softwares and applications. One of the most important applications of immunoinformatics is the prediction of a variety of specific epitopes for B cell recognition and T cell through MHC class I and II molecules. This method reduces costs and time compared to laboratory tests. In this state-of-the-art review, we review about 50 papers to find the latest and most used immunoinformatic tools as well as their applications for predicting the viral, bacterial and tumoral structural and linear epitopes of B and T cells. In the clinic, the main application of prediction of epitopes is for designing peptide-based vaccines. Peptide-based vaccines are a considerably potential alternative to low-cost vaccines that may reduce the risks related to the production of common vaccines.

In silico analysis for development of epitopes-based peptide vaccine against Alkhurma hemorrhagic fever virus

Alkhurma hemorrhagic fever virus (ALKV) causes a fatal clinical disease in human beings of different tropical and sub-tropical regions. Recently, the ALKV epidemics have raised a great public health concern with the room for improvement in the essential therapeutic interventions. Despite increased realistic clinical cases of ALKV infection, the efficient vaccine or immunotherapy is not yet available to-date. Therefore, the current study aimed to analyze the envelope glycoprotein of ALKV for the development of B-cells and T-cells epitope-based peptide vaccine using the computational in silico method. Utilizing various immunoinformatics approaches, a total of 5 B-cells and 25 T-cells (MHC-I = 17, MHC-II = 8) epitope-based peptides were predicted in the current study. All predicted peptides had highest antigenicity and immunogenicity scores along with high binding affinity to human leukocyte antigen (HLA) class II alleles. Among 25T-cell epitopes, three peptides were found alike to have affinity to bind both MHC-I and MHC-II alleles. These outcomes suggested that these predicted epitopes could potentially be used in the development of an efficient vaccine against ALKV, which may enable to elicit both humoral and cell-mediated immunity. Although, these predicted peptides could be useful in designing a candidate vaccine for the prevention of ALKV; however, it's in vitro and in vivo assessments are prerequisite.Communicated by Ramaswamy H. Sarma.

Peptide-Based Subunit Vaccine Design of T- and B-Cells Multi-Epitopes against Zika Virus Using Immunoinformatics Approaches

The Zika virus disease, also known as Zika fever is an arboviral disease that became epidemic in the Pacific Islands and had spread to 18 territories of the Americas in 2016. Zika virus disease has been linked to several health problems such as microcephaly and the Guillain-Barré syndrome, but to date, there has been no vaccine available for Zika. Problems related to the development of a vaccine include the vaccination target, which covers pregnant women and children, and the antibody dependent enhancement (ADE), which can be caused by non-neutralizing antibodies. The peptide vaccine was chosen as a focus of this study as a safer platform to develop the Zika vaccine. In this study, a collection of Zika proteomes was used to find the best candidates for T- and B-cell epitopes using the immunoinformatics approach. The most promising T-cell epitopes were mapped using the selected human leukocyte antigen (HLA) alleles, and further molecular docking and dynamics studies showed a good peptide-HLA interaction for the best major histocompatibility complex-II (MHC-II) epitope. The most promising B-cell epitopes include four linear peptides predicted to be cross-reactive with T-cells, and conformational epitopes from two proteins accessible by antibodies in their native biological assembly. It is believed that the use of immunoinformatics methods is a promising strategy against the Zika viral infection in designing an efficacious multiepitope vaccine.

A common conserved peptide harboring predicted T and B cell epitopes in domain III of envelope protein of Japanese Encephalitis Virus and West Nile Virus for potential use in epitope based vaccines

Japanese encephalitis virus (JEV) and West Nile virus (WNV) are two major mosquito borne flaviviruses belonging to same serocomplex. JEV is transmitted by Culex mosquitoes and the reservoir host for the virus is pigs and/or water birds. WNV is also transmitted by Culex mosquitoes and reservoir host in this case is birds. It can also be transmitted through contact with other infected animals, their blood, or other tissues. The envelope protein of these viruses is the major source of epitopes and provides protective immunity. Bioinformatics tools were used to identify conserved epitopes in the envelope protein of these viruses. A conserved peptide "TPVGRLVTVNPFV" present in both the viruses containing predicted T and B cell epitopes was found. The model of one of the predicted epitope was generated and upon docking it bound in the groove of HLA-A0201 Class I MHC molecule. Further, it was amenable to proteasomal cleavage enhancing its chances of processing by cytosolic pathway. The peptide was found to be non toxic, non allergenic and stable in mammalian cells based on database search. The population coverage was pan world and nearly 70% identity of the peptide was found in the Zika virus envelope protein. The peptide was located in the domain III of envelope protein which is the exposed domain therefore B cell receptors may recognize this peptide easily. The conserved peptide containing T and B cell epitopes can have future application for designing epitope based vaccines for both JEV and WNV.

In vitro and in silico Models to Study Mosquito-Borne Flavivirus Neuropathogenesis, Prevention, and Treatment

Mosquito-borne flaviviruses can cause disease in the nervous system, resulting in a significant burden of morbidity and mortality. Disease models are necessary to understand neuropathogenesis and identify potential therapeutics and vaccines. Non-human primates have been used extensively but present major challenges. Advances have also been made toward the development of humanized mouse models, but these models still do not fully represent human pathophysiology. Recent developments in stem cell technology and cell culture techniques have allowed the development of more physiologically relevant human cell-based models. In silico modeling has also allowed researchers to identify and predict transmission patterns and discover potential vaccine and therapeutic candidates. This review summarizes the research on in vitro and in silico models used to study three mosquito-borne flaviviruses that cause neurological disease in humans: West Nile, Dengue, and Zika. We also propose a roadmap for 21st century research on mosquito-borne flavivirus neuropathogenesis, prevention, and treatment.

IL-1 receptor antagonist therapy mitigates placental dysfunction and perinatal injury following Zika virus infection

Zika virus (ZIKV) infection during pregnancy causes significant adverse sequelae in the developing fetus, and results in long-term structural and neurologic defects. Most preventive and therapeutic efforts have focused on the development of vaccines, antivirals, and antibodies. The placental immunologic response to ZIKV, however, has been largely overlooked as a target for therapeutic intervention. The placental inflammatory response, specifically IL-1β secretion and signaling, is induced by ZIKV infection and represents an environmental factor that is known to increase the risk of perinatal developmental abnormalities. We show in a mouse model that maternally administrated IL-1 receptor antagonist (IRA; Kineret, or anakinra), following ZIKV exposure, can preserve placental function (by improving trophoblast invasion and placental vasculature), increase fetal viability, and reduce neurobehavioral deficits in the offspring. We further demonstrate that while ZIKV RNA is highly detectable in placentas, it is not correlated with fetal viability. Beyond its effects in the placenta, we show that IL-1 blockade may also directly decrease fetal neuroinflammation by mitigating fetal microglial activation in a dose-dependent manner. Our studies distinguish the role of placental inflammation during ZIKV-infected pregnancies, and demonstrate that maternal IRA may attenuate fetal neuroinflammation and improve perinatal outcomes.

Identification of relevant regions on structural and nonstructural proteins of Zika virus for vaccine and diagnostic test development: an in silico approach

Zika virus (ZIKV) is an arbovirus belonging to the Flaviviridae family and the genus Flavivirus. Infection with ZIKV causes a mild, self-limiting febrile illness called Zika fever. However, ZIKV infection has been recently associated with microcephaly and Guillain-Barré syndrome. Vaccines for the disease are a high priority of World Health Organization. Several studies are currently being conducted to develop a vaccine against ZIKV, but until now there is no licensed ZIKV vaccine. This study used a novel immunoinformatics approach to identify potential T-cell immunogenic epitopes present in the structural and nonstructural proteins of ZIKV. Fourteen T-cell candidate epitopes were identified on ZIKV structural and nonstructural proteins: pr^36-50; C^61-75; C^103-117; E^374-382; E^477-491; NS2a^90-104; NS2a^174-188; NS2a^179-193; NS2a^190-204; NS2a^195-209; NS2a^200-214; NS3^175-189; and NS4a^82-96; NS4a^99-113. Among these epitopes, only E^374-382 is a human leukocyte antigen (HLA) type I restricted epitope. All identified epitopes showed a low similarity with other important flaviviruses but had a high conservation rate among the ZIKV strains and a high population coverage rate. Therefore, these predicted T-cell epitopes are potential candidates targets for development of vaccines to prevent ZIKV infection.

Immunoinformatics Approach for Epitope-Based Peptide Vaccine Design and Active Site Prediction against Polyprotein of Emerging Oropouche Virus

Oropouche virus (OROV) is an emerging pathogen which causes Oropouche fever and meningitis in humans. Several outbreaks of OROV in South America, especially in Brazil, have changed its status as an emerging disease, but no vaccine or specific drug target is available yet. Our approach was to identify the epitope-based vaccine candidates as well as the ligand-binding pockets through the use of immunoinformatics. In this report, we identified both T-cell and B-cell epitopes of the most antigenic OROV polyprotein with the potential to induce both humoral and cell-mediated immunity. Eighteen highly antigenic and immunogenic CD8⁺ T-cell epitopes were identified, including three 100% conserved epitopes (TSSWGCEEY, CSMCGLIHY, and LAIDTGCLY) as the potential vaccine candidates. The selected epitopes showed 95.77% coverage for the mixed Brazilian population. The docking simulation ensured the binding interaction with high affinity. A total of five highly conserved and nontoxic linear B-cell epitopes "NQKIDLSQL," "HPLSTSQIGDRC," "SHCNLEFTAITADKIMSL," "PEKIPAKEGWLTFSKEHTSSW," and "HHYKPTKNLPHVVPRYH" were selected as potential vaccine candidates. The predicted eight conformational B-cell epitopes represent the accessibility for the entered virus. In the posttherapeutic strategy, ten ligand-binding pockets were identified for effective inhibitor design against emerging OROV infection. Collectively, this research provides novel candidates for epitope-based peptide vaccine design against OROV.

Immunoinformatics and molecular docking studies reveal potential epitope-based peptide vaccine against DENV-NS3 protein

Dengue, still a "Neglected Tropical Disease" is somehow injustice and remains uncontrolled globally. World Health Organization (2012-2020) reported that the world's half population is living in dengue-affected regions. Therefore, effective drug candidates or promising vaccines are urgently needed to control the dengue. It is an acute febrile disease caused by mosquito borne dengue viruses (DENVs) which belong to the genus Flavivirus with four serotypes. In present work, immunoinformatics approach was utilized to predict the antigenic epitopes of dengue proteins for the development of DENV vaccine. B-cell and cytotoxic T-lymphocyte epitopes were predicted for NS3 dengue protein. Docking complexes of 17 antigenic B-cell epitopes of various lengths and 4 CTL epitopes with antigenic sites were investigated followed by binding interaction analyses of top predicted peptides with MHC-I HLA-A2 molecule. These predicted epitopes with antigenic amino acids might present a preliminary set of peptides for future vaccine development against DENV.

A Web Resource for Designing Subunit Vaccine Against Major Pathogenic Species of Bacteria

Evolution has led to the expansion of survival strategies in pathogens including bacteria and emergence of drug resistant strains proved to be a major global threat. Vaccination is a promising strategy to protect human population. Reverse vaccinology is a more robust vaccine development approach especially with the availability of large-scale sequencing data and rapidly dropping cost of the techniques for acquiring such data from various organisms. The present study implements an immunoinformatic approach for screening the possible antigenic proteins among various pathogenic bacteria to systemically arrive at epitope-based vaccine candidates against 14 pathogenic bacteria. Thousand four hundred and fifty nine virulence factors and Five hundred and forty six products of essential genes were appraised as target proteins to predict potential epitopes with potential to stimulate different arms of the immune system. To address the self-tolerance, self-epitopes were identified by mapping on 1000 human proteome and were removed. Our analysis revealed that 21proteins from 5 bacterial species were found as virulent as well as essential to their survival, proved to be most suitable vaccine target against these species. In addition to the prediction of MHC-II binders, B cell and T cell epitopes as well as adjuvants individually from proteins of all 14 bacterial species, a stringent criteria lead us to identify 252 unique epitopes, which are predicted to be T-cell epitopes, B-cell epitopes, MHC II binders and Vaccine Adjuvants. In order to provide service to scientific community, we developed a web server VacTarBac for designing of vaccines against above species of bacteria. This platform integrates a number of tools that includes visualization tools to present antigenicity/epitopes density on an antigenic sequence. These tools will help users to identify most promiscuous vaccine candidates in a pathogenic antigen. This server VacTarBac is available from URL (http://webs.iiitd.edu.in/raghava/vactarbac/).

Improved Immune Responses Against Zika Virus After Sequential Dengue and Zika Virus Infection in Humans

The high levels of dengue-virus (DENV) seroprevalence in areas where the Zika virus (ZIKV) is circulating and the cross-reactivity between these two viruses have raised concerns on the risk of increased ZIKV disease severity for patients with a history of previous DENV infections. To determine the role of DENV preimmunity in ZIKV infection, we analyzed the T- and B-cell responses against ZIKV in donors with or without previous DENV infection. Using peripheral blood mononuclear cells (PBMCs) from donors living in an endemic area in Colombia, we have identified, by interferon (IFN)-γ enzyme-linked immunospot (ELISPOT) assay, most of the immunodominant ZIKV T-cell epitopes in the nonstructural (NS) proteins NS1, NS3, and NS5. Analyses of the T- and B-cell responses in the same donors revealed a stronger T-cell response against peptides conserved between DENV and ZIKV, with a higher level of ZIKV-neutralizing antibodies in DENV-immune donors in comparison with DENV-naïve donors. Strikingly, the potential for antibody-mediated enhancement of ZIKV infection was reduced in donors with sequential DENV and ZIKV infection in comparison with donors with DENV infection only. Altogether, these data suggest that individuals with DENV immunity present improved immune responses against ZIKV.

Elucidating the Inhibitory Potential of Designed Peptides Against Amyloid Fibrillation and Amyloid Associated Cytotoxicity

Inhibition of fibrillation process and disaggregation of mature fibrils using small peptide are the promising remedial strategies to combat neurodegenerative diseases. However, designing peptide-based drugs to target β-sheet-rich amyloid has been a major challenge. The current work describes, for the first time, the amyloid inhibitory potential of the two short peptides (selected on the basis of predisposition of their amino acid residues toward β-sheet formation) using combination of biophysical, imaging methods, and docking approaches. Results showed that peptides employed different mechanisms to inhibit the amyloid fibrillation. Furthermore, they were also effective in blocking the amyloid fibrillation pathway. In contrary to the insulin fibrillar mesh, significantly less fibrillar species appeared in the presence of peptides, as confirmed by transmission electron microscopy. Circular dichroism analysis indicated that although peptides did not stabilize the native state of insulin, they inhibited amyloid aggregation by reducing the formation of β-sheet rich structures. Hemolytic assay revealed the non-hemolytic nature of the species formed when insulin was co-incubated with the peptides. Therefore, despite the inherent potential to form β-sheet structure, these peptides inhibited the amyloid formation and potentially can be used as therapeutics for the treatment of amyloid-related diseases.

Expression of a Zika virus antigen in microalgae: Towards mucosal vaccine development

Zika virus (ZIKV) infection has extended rapidly all over the world in the last decades affecting humans of all ages, inducing severe illness such as the autoimmune Guillain-Barré syndrome as well as fetal neurodevelopmental defects. Despite the epidemiological importance of ZIKV, today there are no commercially available drugs or vaccines to combat or prevent this infection. Microalgae are attractive hosts to produce and deliver vaccines, with some candidates under preclinical evaluation. Herein, algae-based expression was assessed for the production of a new vaccine candidate against ZIKV called ZK. The Algevir technology was applied to express an antigenic protein called ZK comprising the B subunit of the heat labile Escherichia coli enterotoxin along with 3 epitopes from the ZIKV envelope glycoprotein. Efficient expression of the ZK antigen was achieved in Schizochytrium sp. with yields of up to 365 μg g^-1 microalgae fresh weight. Upon oral administration in mice, the microalgae-made ZK protein elicited significant humoral responses at a higher magnitude to those induced upon subcutaneous immunization. The algae-made ZK vaccine represents a promising candidate to formulate attractive vaccines against ZIKV.