Prediction of promiscuous T-cell epitopes in the Zika virus polyprotein: An in silico approach

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

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

Reverse engineering protection: A comprehensive survey of reverse vaccinology-based vaccines targeting viral pathogens

Vaccines have significantly reduced the impact of numerous deadly viral infections. However, there is an increasing need to expedite vaccine development in light of the recurrent pandemics and epidemics. Also, identifying vaccines against certain viruses is challenging due to various factors, notably the inability to culture certain viruses in cell cultures and the wide-ranging diversity of MHC profiles in humans. Fortunately, reverse vaccinology (RV) efficiently overcomes these limitations and has simplified the identification of epitopes from antigenic proteins across the entire proteome, streamlining the vaccine development process. Furthermore, it enables the creation of multiepitope vaccines that can effectively account for the variations in MHC profiles within the human population. The RV approach offers numerous advantages in developing precise and effective vaccines against viral pathogens, including extensive proteome coverage, accurate epitope identification, cross-protection capabilities, and MHC compatibility. With the introduction of RV, there is a growing emphasis among researchers on creating multiepitope-based vaccines aiming to stimulate the host's immune responses against multiple serotypes, as opposed to single-component monovalent alternatives. Regardless of how promising the RV-based vaccine candidates may appear, they must undergo experimental validation to probe their protection efficacy for real-world applications. The time, effort, and resources allocated to the laborious epitope identification process can now be redirected toward validating vaccine candidates identified through the RV approach. However, to overcome failures in the RV-based approach, efforts must be made to incorporate immunological principles and consider targeting the epitope regions involved in disease pathogenesis, immune responses, and neutralizing antibody maturation. Integrating multi-omics and incorporating artificial intelligence and machine learning-based tools and techniques in RV would increase the chances of developing an effective vaccine. This review thoroughly explains the RV approach, ideal RV-based vaccine construct components, RV-based vaccines designed to combat viral pathogens, its challenges, and future perspectives.

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.

Immuno-informatics guided designing of a multi-epitope vaccine against Dengue and Zika

Dengue and zika are amongst the most prevalent mosquito-borne diseases caused by closely related members Dengue virus (DENV) and Zika virus (ZIKV), respectively, of the Flaviviridae family. DENV and ZIKV have been reported to co-infect several people, resulting in fatalities across the world. A vaccine that can safeguard against both these pathogens concurrently, can offer several advantages. This study has employed immuno-informatics for devising a multi-epitope, multi-pathogenic vaccine against both these viruses. Since, the two viruses share a common vector source, whose salivary components are reported to aid viral pathogenesis; antigenic salivary proteins from Aedes aegypti were also incorporated into the design of the vaccine along with conserved structural and non-structural viral proteins. Conserved B- and T-cell epitopes were identified for all the selected antigenic proteins. These epitopes were merged and further supplemented with β-defensin as an adjuvant, to yield an immunogenic vaccine construct. In-silico 3D modeling and structural validation of the vaccine construct was conducted, followed by its molecular docking and molecular dynamics simulation studies with human TLR2. Immune simulation study was also performed, and it further provided support that the designed vaccine can mount an effective immune response and hence provide protection against both DENV and ZIKV. Communicated by Ramaswamy H. Sarma.

Prediction of Conformational and Linear B-Cell Epitopes on Envelop Protein of Zika Virus Using Immunoinformatics Approach

The current spread of Zika virus infection in India has become a public health issue due to the virus's possible link to birth abnormalities and neurological disorders. There is a need for enhanced vaccines or drugs as a result of its epidemic outbreak and the lack of potential medication. B-cell mediated adaptive immunity is capable of developing pathogen-specific memory that confers immunological protection. Therefore, in this study, the envelope protein of the Zika virus was retrieved from the NCBI protein database. The ABCpred and BepiPred software were used to discover linear B-cell epitopes on envelope protein. Conformational B-cell epitopes on envelope protein were identified using SEPPA 3.0 and Ellipro tools. Predicted B-cell epitopes were evaluated for allergenicity, toxicity, and antigenicity. Two consensus linear B-cell epitopes, envelope_165-180 (AKVEITPNSPRAEATL) and envelope_224-238 (PWHAGADTGTPHWNN) were identified using ABCpred and BepiPredtools. SEPPA 3.0 and Elliprotools predicted consensus conformational envelope_98-110 (DRGWGNGCGLFGK) and envelope_248-251 (AHAK) epitopes and one residue (⁷⁵PRO) within envelope protein as a component of B-cell epitopes. These predicted linear and conformational B-cell epitopes will help in designing peptide vaccines that will activate the humoral response. However, in-vitro and in-vivo laboratory experimental confirmations are still needed to prove the application's feasibility.

Candidate Multi-Epitope Vaccine against Corona B.1.617 Lineage: In Silico Approach

Various mutations have accumulated since the first genome sequence of SARS-CoV2 in 2020. Mutants of the virus carrying the D614G and P681R mutations in the spike protein are increasingly becoming dominant all over the world. The two mutations increase the viral infectivity and severity of the disease. This report describes an in silico design of SARS-CoV-2 multi-epitope carrying the spike D614G and P681R mutations. The designed vaccine harbors the D614G mutation that increases viral infectivity, fitness, and the P681R mutation that enhances the cleavage of S to S1 and S2 subunits. The designed multi-epitope vaccine showed an antigenic property with a value of 0.67 and the immunogenicity of the predicted vaccine was calculated and yielded 3.4. The vaccine construct is predicted to be non-allergenic, thermostable and has hydrophilic nature. The combination of the selected CTL and HTL epitopes in the vaccine resulted in 96.85% population coverage globally. Stable interactions of the vaccine with Toll-Like Receptor 4 were tested by docking studies. The multi-epitope vaccine can be a good candidate against highly infecting SARS-CoV-2 variants.

Reverse vaccinology-based prediction of a multi-epitope SARS-CoV-2 vaccine and its tailoring to new coronavirus variants

The genome feature of SARS-CoV-2 leads the virus to mutate and creates new variants of concern. Tackling viral mutations is also an important challenge for the development of a new vaccine. Accordingly, in the present study, we undertook to identify B- and T-cell epitopes with immunogenic potential for eliciting responses to SARS-CoV-2, using computational approaches and its tailoring to coronavirus variants. A total of 47 novel epitopes were identified as immunogenic triggering immune responses and no toxic after investigation with in silico tools. Furthermore, we found these peptide vaccine candidates showed a significant binding affinity for MHC I and MHC II alleles in molecular docking investigations. We consider them to be promising targets for developing peptide-based vaccines against SARS-CoV-2. Subsequently, we designed two efficient multi-epitopes vaccines against the SARS-CoV-2, the first one based on potent MHC class I and class II T-cell epitopes of S (FPNITNLCPF-NYNYLYRLFR-MFVFLVLLPLVSSQC), M (MWLSYFIASF-GLMWLSYFIASFRLF), E (LTALRLCAY-LLFLAFVVFLLVTLA), and N (SPRWYFYYL-AQFAPSASAFFGMSR). The second candidate is the result of the tailoring of the first designed vaccine according to three classes of SARS-CoV-2 variants. Molecular docking showed that the protein-protein binding interactions between the vaccines construct and TLR2-TLR4 immune receptors are stable complexes. These findings confirmed that the final multi-epitope vaccine could be easily adapted to new viral variants. Our study offers a shortlist of promising epitopes that can accelerate the development of an effective and safe vaccine against the virus and its adaptation to new variants.Communicated by Ramaswamy H. Sarma.

In silico designed Staphylococcus aureus B-cell multi-epitope vaccine did not elicit antibodies against target antigens suggesting multi-domain approach

The vaccine development strategies have evolved from using an entire organism as an immunogen to a single antigen and further towards an epitope. Since an epitope is a relatively tiny and immunologically relevant part of an antigen, it has the potential to stimulate more robust and specific immune responses while causing minimal adverse effects. As a result, the recent focus of vaccine development has been to develop multi-epitope vaccines that can target multiple virulence mechanisms. Accordingly, we designed multi-epitope vaccine candidates B (multi-B-cell epitope immunogen) and CTB-B (an adjuvant - cholera toxin subunit B (CTB) - attached to immunogen B) against S. aureus by employing immunoinformatics approaches. The designed vaccines are composed of B-cell epitope segments (20-mer) of the eight well-characterized S. aureus virulence factors, namely ClfB, FnbpA, Hla, IsdA, IsdB, LukE, SdrD, and SdrE connected in series. The designed vaccines were expressed, purified, and administered to C57BL/6 mice with Freund adjuvant to evaluate the immunogenicity and protective efficacy. The results revealed that the immunized mice showed high IgG titer for the immunogen, and the antibody titer increased significantly following the second immunization. However, the generated antibodies did not protect mice from infection. The interaction of anti-B antibodies with source virulence factors showed that the generated antibodies have no binding affinity with any of the corresponding virulence factors. Our results demonstrate the limitation of in silico designed B-cell multi-epitope vaccine and suggest that a protein domain carrying both linear and conformational B-cell epitopes might be a better choice for developing an effective multi-epitope vaccine against S. aureus.

Computational vaccinology guided design of multi-epitope subunit vaccine against a neglected arbovirus of the Americas

Mayaro virus (MAYV) is an arbovirus found in the Americas that can cause debilitating arthritogenic disease. Although it is an emerging virus, the only current approach is vector control, as there are no approved vaccines to prevent MAYV infection nor therapeutics to treat it. In search of an effective vaccine candidate against MAYV, we used immunoinformatics and molecular modeling to attempt to identify promiscuous T-cell epitopes of the nonstructural polyproteins (nsP1, nsP2, nsP3, and nsP4) from 127 MAYV genomes sequenced in the Americas (08 Bolivia, 72 Brazil, 04 French Guiana, 05 Haiti, 20 Peru, 04 Trinidad and Tobago, and 14 Venezuela). For this purpose, consensus sequences of 360 proteins were used to identify short protein sequences that can bind to MHC I class (MHC II). Our analysis revealed 56 potential MHC-I/TCD8+ (29 MHC-II/TCD4+) epitopes, but only 6 (16) TCD8+ (TCD4+) epitopes showed high antigenicity and conservation, non-allergenicity, non-toxicity, and excellent population coverage. Finally, classical and quantum mechanical calculations (QM:MM) were used to improve the quality of the docking calculations, with the QM part of the simulations performed using the density functional theory formalism (DFT). These results provide insights for the advancement of diagnostic platforms, vaccine development, and immunotherapeutic interventions.Communicated by Ramaswamy H. Sarma.

Rapid Identification of MHCII-Binding Peptides Through Microsphere-Assisted Peptide Screening (MAPS)

CD4⁺ T cells play a vital role in the immune response, and their function requires T cell receptor (TCR) recognition of peptide epitopes presented in complex with MHC class II (MHCII) molecules. Consequently, rapidly identifying peptides that bind MHCII is critical to understanding and treating infectious disease, cancer, autoimmunity, allergy, and transplant rejection. Computational methods provide a fast, ultrahigh-throughput approach to predict MHCII-binding peptides but lack the accuracy of experimental methods. In contrast, experimental methods offer accurate, quantitative results at the expense of speed. To address the gap between these two approaches, we developed a high-throughput, semiquantitative experimental screening strategy termed microsphere-assisted peptide screening (MAPS). Here, we use the Zika virus envelope protein as an example to demonstrate the rapid identification of MHCII-binding peptides from a single pathogenic protein using MAPS. This process involves several key steps including peptide library design, peptide exchange into MHCII, peptide-MHCII loading onto microspheres, flow cytometry screening, and data analysis to identify peptides that bind to one or more MHCII alleles.

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.

New Computational Approach for Peptide Vaccine Design Against SARS-COV-2

The design for vaccines using in silico analysis of genomic data of different viruses has taken many different paths, but lack of any precise computational approach has constrained them to alignment methods and some alignment-free techniques. In this work, a precise computational approach has been established wherein two new mathematical parameters have been suggested to identify the highly conserved and surface-exposed regions which are spread over a large region of the surface protein of the virus so that one can determine possible peptide vaccine candidates from those regions. The first parameter, w, is the sum of the normalized values of the measure of surface accessibility and the normalized measure of conservativeness, and the second parameter is the area of a triangle formed by a mathematical model named 2D Polygon Representation. This method has been, therefore, used to determine possible vaccine targets against SARS-CoV-2 by considering its surface-situated spike glycoprotein. The results of this model have been verified by a parallel analysis using the older approach of manually estimating the graphs describing the variation of conservativeness and surface-exposure across the protein sequence. Furthermore, the working of the method has been tested by applying it to find out peptide vaccine candidates for Zika and Hendra viruses respectively. A satisfactory consistency of the model results with pre-established results for both the test cases shows that this in silico alignment-free analysis proposed by the model is suitable not only to determine vaccine targets against SARS-CoV-2 but also ready to extend against other viruses.

A vaccine inducing solely cytotoxic T lymphocytes fully prevents Zika virus infection and fetal damage

As vaccine-induced non-neutralizing antibodies may cause antibody-dependent enhancement of Zika virus (ZIKV) infection, we test a vaccine that induces only specific cytotoxic T lymphocytes (CTLs) without specific antibodies. We construct a DNA vaccine expressing a ubiquitinated and rearranged ZIKV non-structural protein 3 (NS3). The protein is immediately degraded and processed in the proteasome for presentation via major histocompatibility complex (MHC) class I for CTL generation. We immunize Ifnar1^-/- adult mice with the ubiquitin/NS3 vaccine, impregnate them, and challenge them with ZIKV. Our data show that the vaccine greatly reduces viral titers in reproductive organs and other tissues of adult mice. All mice immunized with the vaccine survived after ZIKV challenge. The vaccine remarkably reduces placenta damage and levels of pro-inflammatory cytokines, and it fully protects fetuses from damage. CD8⁺ CTLs are essential in protection, as demonstrated via depletion experiments. Our study provides a strategy to develop safe and effective vaccines against viral infections.

Pichia pastoris displaying ZIKV protein epitopes from the Envelope and NS1 induce in vitro immune activation

The severe consequences of ZIKV infection and its emergence and re-emergence in several countries have boosted vaccines' development. Yeasts such as Pichia pastoris has been widely employed as antigen carriers for immunization against infectious agents. Components of the yeast cell wall have immunostimulatory properties, and recombinant antigens can be anchored to the cell surface to enhance the presentation to the immune system. Here we aimed at producing and anchoring ZIKV proteins in the P. pastoris surface as a vaccine approach. Expression cassettes were designed with epitopes of the Envelope and NS1 proteins. Immunofluorescence microscopy confirmed the anchoring of recombinant proteins. Yeasts' ability to stimulate immune cells was evaluated in vitro by incubation with lymphocytes and monocytes isolated from mouse spleen. P. pastoris expressing EnvNS1 epitopes promoted increased levels of IL-6, IL-10, and TNF-α cytokines and an increase in the number of CD4+, CD8+, and CD16+ lymphocytes, similarly to ZIKV. This profile is indicative of the activation of immunological cells and suggests an immunogenic potential of the proposed yeast vaccines against ZIKV, reinforcing the possibility of P. pastoris as adjuvant and carrier of antigens.

Identification of promiscuous T cell epitopes on Mayaro virus structural proteins using immunoinformatics, molecular modeling, and QM:MM approaches

The Mayaro virus (MAYV) belongs to genus Alphavirus (family Togaviridae) and has been reported in several countries, especially in tropical regions of America. Due to its outbreaks and potential lack of medication, an effective vaccine formulation is strongly required. This study aimed to predict promiscuous T cell epitopes from structural polyproteins of MAYV using an immunoinformatics approach. For this purpose, consensus sequences were used to identify short protein sequences capable of binding to MHC class I and class II alleles. Our analysis pointed out 4 MHC-I/TCD8+ and 21 MHC-II/TCD4+ epitopes on capside (1;3), E1 (2;5), E2 (1;10), E3 (0;2), and 6 K (0;1) proteins. These predicted epitopes were characterized by high antigenicity, immunogenicity, conservancy, non-allergenic, non-toxic, and good population coverage rate values for North and South American geographical areas. Afterwards, we used the crystal structure of human toll-like receptor 3 (TLR3) ectodomain as a template to predict, through docking essays, the placement of a vaccine prototype at the TLR3 receptor binding site. Finally, classical and quantum mechanics/molecular mechanics (QM:MM) computations were employed to improve the quality of docking calculations, with the QM part of the simulations being accomplished by using the density functional theory (DFT) formalism. These results provide important insights into the advancement of diagnostic platforms, the development of vaccines, and immunotherapeutic interventions.

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 Driven Prediction of Multiepitopic Vaccine Against Klebsiella pneumoniae and Mycobacterium tuberculosis Coinfection and Its Validation via In Silico Expression

Klebsiella pneumoniae and Mycobacterium tuberculosis coinfection is one of the most lethal combinations that has been becoming frequent yet, not diagnosed and reported properly. Due to the simultaneous occurrence of both infections, diagnosis is delayed leading to inadequate treatments and mortality. With the rise of MDR Klebsiella and Mycobacterium, a prophylactic and an immunotherapeutic vaccine has to be entailed for preemptive and adroit therapeutic approach. In this study, we aim to implement reverse vaccinology approach that encompasses a comprehensive evaluation of vital aspects of the pathogens to explore immunogenic epitopes against Omp A of Klebsiella and Rv1698, Rv1973 of Mtb that may help in vaccine development. The designed multi-epitopic vaccine was assessed for antigenicity, allergenicity and various physiochemical parameters. Molecular docking and simulations were executed to assess the immunogenicity and complex stability of the vaccine. The final multi-epitopic vaccine is validated to be highly immunogenic and can serve as a valuable proactive remedy for subject pathogens.

Immuno-informatics-based identification of novel potential B cell and T cell epitopes to fight Zika virus infections

BACKGROUND

Globally, the recent outbreak of Zika virus (ZIKV) in Brazil, Asia Pacific, and other countries highlighted the unmet medical needs. Currently, there are neither effective vaccines nor therapeutics available to prevent or treat ZIKV infection.

OBJECTIVE

In this study, we aimed to design an epitope-based vaccine for ZIKV using an in silico approach to predict and analyze B- and T-cell epitopes.

METHODS

The prediction of the most antigenic epitopes has targeted the capsid and the envelope proteins as well as nonstructural proteins NS5 and NS3 using immune-informatics tools PROTPARAM, CFSSP, PSIPRED, and Vaxijen v2.0. B and T-cell epitopes were predicted using ABCpred, IEDB, TepiTool, and their toxicity were evaluated using ToxinPred. The 3-dimensional epitope structures were generated by PEP-FOLD. Energy minimization was performed using Swiss-Pdb Viewer, and molecular docking was conducted using PatchDock and FireDock server.

RESULTS

As a result, we predicted 307 epitopes of MHCI (major histocompatibility complex class I) and 102 epitopes of MHCII (major histocompatibility complex class II). Based on immunogenicity and antigenicity scores, we identified the four most antigenic MHC I epitopes: MVLAILAFLR (HLA-A*68 :01), ETLHGTVTV (HLA-A*68 :02), DENHPYRTW (HLA-B*44 :02),QEGVFHTMW (HLA-B*44 :03) and TASGRVIEEW (HLA-B*58:01), and MHC II epitopes: IIKKFKKDLAAMLRI (HLA-DRB3*02 :02), ENSKMMLELDPPFGD (HLA-DRB3*01:01), HAETWFFDENHPYRT (HLA-DRB3*01:01), TDGVYRVMTRRLLGS (HLA-DRB1*11 :01), and DGCWYGMEIRPRKEP (HLA-DRB5*01:01).

CONCLUSION

This study provides novel potential B cell and T cell epitopes to fight Zika virus infections and may prompt further development of vaccines against ZIKV and other emerging infectious diseases. However, further investigations for protective immune response by in vitro and in vivo studies to ratify the immunogenicity, safety of the predicted structure, and ultimately the vaccine properties to prevent ZIKV infections are warranted.

Anti-COVID-19 multi-epitope vaccine designs employing global viral genome sequences

BACKGROUND

The coronavirus SARS-CoV-2 is a member of the Coronaviridae family that has caused a global public health emergency. Currently, there is no approved treatment or vaccine available against it. The current study aimed to cover the diversity of SARS-CoV-2 strains reported from all over the world and to design a broad-spectrum multi-epitope vaccine using an immunoinformatics approach.

METHODS

For this purpose, all available complete genomes were retrieved from GISAID and NGDC followed by genome multiple alignments to develop a global consensus sequence to compare with the reference genome. Fortunately, comparative genomics and phylogeny revealed a significantly high level of conservation between the viral strains. All the Open Reading Frames (ORFs) of the reference sequence NC_045512.2 were subjected to epitope mapping using CTLpred and HLApred, respectively. The predicted CTL epitopes were then screened for antigenicity, immunogenicity and strong binding affinity with HLA superfamily alleles. HTL predicted epitopes were screened for antigenicity, interferon induction potential, overlapping B cell epitopes and strong HLA DR binding potential. The shortlisted epitopes were arranged into two multi-epitope sequences, Cov-I-Vac and Cov-II-Vac, and molecular docking was performed with Toll-Like Receptor 8 (TLR8).

RESULTS

The designed multi-epitopes were found to be antigenic and non-allergenic. Both multi-epitopes were stable and predicted to be soluble in an Escherichia coli expression system. The molecular docking with TLR8 also demonstrated that they have a strong binding affinity and immunogenic potential. These in silico analyses suggest that the proposed multi-epitope vaccine can effectively evoke an immune response.

Designing Multi-Epitope Vaccines to Combat Emerging Coronavirus Disease 2019 (COVID-19) by Employing Immuno-Informatics Approach

A recent pandemic caused by a single-stranded RNA virus, COVID-19, initially discovered in China, is now spreading globally. This poses a serious threat that needs to be addressed immediately. Genome analysis of SARS-CoV-2 has revealed its close relation to SARS-coronavirus along with few changes in its spike protein. The spike protein aids in receptor binding and viral entry within the host and therefore represents a potential target for vaccine and therapeutic development. In the current study, the spike protein of SARS-CoV-2 was explored for potential immunogenic epitopes to design multi-epitope vaccine constructs. The S1 and S2 domains of spike proteins were analyzed, and two vaccine constructs were prioritized with T-cell and B-cell epitopes. We adapted a comprehensive predictive framework to provide novel insights into immunogenic epitopes of spike proteins, which can further be evaluated as potential vaccine candidates against COVID-19. Prioritized epitopes were then modeled using linkers and adjuvants, and respective 3D models were constructed to evaluate their physiochemical properties and their possible interactions with ACE2, HLA Superfamily alleles, TLR2, and TLR4.

Computational Methodology for Peptide Vaccine Design for Zika Virus: A Bioinformatics Approach

With the increasing frequency of viral epidemics, vaccines to augment the human immune response system have been the medium of choice to combat viral infections. The tragic consequences of the Zika virus pandemic in South and Central America a few years ago brought the issues into sharper focus. While traditional vaccine development is time-consuming and expensive, recent advances in information technology, immunoinformatics, genetics, bioinformatics, and related sciences have opened the doors to new paradigms in vaccine design and applications.Peptide vaccines are one group of the new approaches to vaccine formulation. In this chapter, we discuss the various issues involved in the design of peptide vaccines and their advantages and shortcomings, with special reference to the Zika virus for which no drugs or vaccines are as yet available. In the process, we outline our work in this field giving a detailed step-by-step description of the protocol we follow for such vaccine design so that interested researchers can easily follow them and do their own designing. Several flowcharts and figures are included to provide a background of the software to be used and results to be anticipated.

Rapid microsphere-assisted peptide screening (MAPS) of promiscuous MHCII-binding peptides in Zika virus envelope protein

Despite promising developments in computational tools, peptide-class II MHC (MHCII) binding predictors continue to lag behind their peptide-class I MHC counterparts. Consequently, peptide-MHCII binding is often evaluated experimentally using competitive binding assays, which tend to sacrifice throughput for quantitative binding detail. Here, we developed a high-throughput semiquantitative peptide-MHCII screening strategy termed microsphere-assisted peptide screening (MAPS) that aims to balance the accuracy of competitive binding assays with the throughput of computational tools. Using MAPS, we screened a peptide library from Zika virus envelope (E) protein for binding to four common MHCII alleles (DR1, DR4, DR7, DR15). Interestingly, MAPS revealed a significant overlap between peptides that promiscuously bind multiple MHCII alleles and antibody neutralization sites. This overlap was also observed for rotavirus outer capsid glycoprotein VP7, suggesting a deeper relationship between B cell and CD4+ T cell specificity which can facilitate the design of broadly protective vaccines to Zika and other viruses.

Computer-Assisted and Data Driven Approaches for Surveillance, Drug Discovery, and Vaccine Design for the Zika Virus

Human life has been at the edge of catastrophe for millennia due diseases which emerge and reemerge at random. The recent outbreak of the Zika virus (ZIKV) is one such menace that shook the global public health community abruptly. Modern technologies, including computational tools as well as experimental approaches, need to be harnessed fast and effectively in a coordinated manner in order to properly address such challenges. In this paper, based on our earlier research, we have proposed a four-pronged approach to tackle the emerging pathogens like ZIKV: (a) Epidemiological modelling of spread mechanisms of ZIKV; (b) assessment of the public health risk of newly emerging strains of the pathogens by comparing them with existing strains/pathogens using fast computational sequence comparison methods; (c) implementation of vaccine design methods in order to produce a set of probable peptide vaccine candidates for quick synthesis/production and testing in the laboratory; and (d) designing of novel therapeutic molecules and their laboratory testing as well as validation of new drugs or repurposing of drugs for use against ZIKV. For each of these stages, we provide an extensive review of the technical challenges and current state-of-the-art. Further, we outline the future areas of research and discuss how they can work together to proactively combat ZIKV or future emerging pathogens.

In silico epitope prediction and immunogenic analysis for penton base epitope-focused vaccine against hydropericardium syndrome in chicken

Certain strains of fowl adenovirus serotype 4 (FAdV-4) of the family Adenoviridae are recognized to be the causative agents of Hydropericardium Syndrome (HPS) in broiler chicken. Despite the significantly spiking mortality in broilers due to HPS, not much effort has been made to design an effective vaccine against FAdV-4. The combination of immuno- and bioinformatics tools for immunogenic epitope prediction is the most recent concept of vaccine design. It reduces the time and effort required for hunting a potent vaccine candidate and is economical. Previously, we have reported the penton base protein of FAdV-4 to be a candidate for subunit vaccine against HPS. In the present study, we have computationally pre-screened promising B- and T-cell epitopes of the penton base. Multiple methods were employed for linear B-cell epitope identification; BepiPred and five other methods based on physicochemical properties of the amino acids. The penton base was homology modeled by means of Modeller 9.17 and after refinement of the model (by GalaxyRefine web server) ElliPro web tool was used to predict the discontinuous epitopes. NetMHCcons 1.1 and NetMHCIIpan 3.1 servers were used for the likelihood of peptide binding to Major Histocompatibility Complex (MHC) class I & II molecules respectively for T-cell epitope forecast. As a result, we identified the peptide stretch of 1-225  as the most promiscuous B- and T-cell epitope region in penton base Full Length (FL) protein sequence. Escherichia coli based expression vectors were generated containing cloned peptide stretch 1-225 (penton base^1-225) and penton base FL gene sequence. The recombinant penton base^1-225 and penton base FL proteins were expressed and purified using Escherichia coli-based expression system. Purification yield of penton base^1-225 was 3-fold higher compared to penton base FL. These proteins were injected in chickens to determine their competence in protection against HPS. The results showed equal protection level of the two proteins and the commercial inactivated vaccine against FAdV-4 infection. The results suggest the peptide stretch 1-225 of penton base as a valuable candidate for developing an epitope-driven vaccine to combat HPS.

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.

Detecting Vertical Zika Transmission: Emerging Diagnostic Approaches for an Emerged Flavivirus

Zika virus (Zika) was recently responsible for a massive epidemic that spread throughout Latin America and beyond. Though Zika is typically asymptomatic or self-limiting, the sheer numbers of Zika infections led to the identification of unexpected phenotypes including sexual transmission, Guillain-Barré syndrome, and teratogenicity. Thousands of infants in South, Central, and North America have now been born with microcephaly or one of a number of fetal anomalies constituting the congenital Zika syndrome (CZS). Diagnosing CZS is based on a combination of clinical risk assessment and laboratory testing (which includes determining whether the mother has experienced a possible Zika infection during her pregnancy). A newborn suspected of having congenital Zika infection (due to maternal Zika infection or a birth defect described in association with congenital Zika infection) is then specifically tested for presence of Zika virus in neonatal tissue or anti-Zika IgM in the blood or cerebrospinal fluid. Though the guidelines are clear, there is room for considerable practice variation to emerge from individualized patient-provider encounters, largely due to limitations in diagnostic testing for Zika. The natural history of Zika further obscures our ability to know who, when, and how to test. Molecular diagnostics are highly specific but may not serve well those with asymptomatic infection. Serologic assays expand the diagnostic window but are complicated by cross-reactivity among related flaviviruses and passive immunity transferred from mother to baby. Furthermore, existing and emerging diagnostic tools may not be widely available due to limitations in resources and infrastructure of health systems in affected areas. Improvements in assay parameters as well as advances in platforms and deployability hold promise for optimizing diagnostic approaches for congenital Zika infection. The diagnostic tools and technologies under development must be integrated with forthcoming clinical knowledge of congenital Zika infection to fully realize the value that laboratory testing holds for diagnosing in utero mother to child transmission but also for understanding, predicting, and managing the health outcomes due to congenital Zika infection.

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.

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.

Enhancing Antibody Serodiagnosis Using a Controlled Peptide Coimmobilization Strategy

Antigen immunoreactivity is often determined by surface regions defined by the 3D juxtapositions of amino acids stretches that are not continuous in the linear sequence. As such, mimicking an antigen immunoreactivity by means of putative linear peptide epitopes for diagnostic purposes is not trivial. Here we present a straightforward and robust method to extend the reach of immune-diagnostic probes design by copresenting peptides belonging to the same antigenic surface. In this case study focused on a computationally predicted Zika virus NS1 protein putative antigenic region, we reached a diagnostic confidence by the oriented and spatially controlled coimmobilization of peptide sequences found adjacent within the protein fold, that cooperatively interacted to provide enhanced immunoreactivity with respect to single linear epitopes. Through our method, we were able to differentiate Zika infected individuals from healthy controls. Remarkably, our strategy fits well with the requirements to build high-throughput screening platforms of linear and mixed peptide libraries, and it could possibly facilitate the rapid identification of conformational immunoreactive regions.

Immune Responses to Dengue and Zika Viruses-Guidance for T Cell Vaccine Development

Despite numerous efforts to identify the molecular and cellular effectors of the adaptive immunity that induce a long-lasting immunity against dengue or Zika virus infection, the specific mechanisms underlying such protective immunity remain largely unknown. One of the major challenges lies in the high level of dengue virus (DENV) seroprevalence in areas where Zika virus (ZIKV) is circulating. In the context of such a pre-existing DENV immunity that can exacerbate ZIKV infection and disease, and given the lack of appropriate treatment for ZIKV infection, there is an urgent need to develop an efficient vaccine against DENV and ZIKV. Notably, whereas several ZIKV vaccine candidates are currently in clinical trials, all these vaccine candidates have been designed to induce neutralizing antibodies as the primary mechanism of immune protection. Given the difficulty to elicit simultaneously high levels of neutralizing antibodies against the different DENV serotypes, and the potential impact of pre-existing subneutralizing antibodies induced upon DENV infection or vaccination on ZIKV infection and disease, additional or alternative strategies to enhance vaccine efficacy, through T cell immunity, are now being considered. In this review, we summarize recent discoveries about cross-reactive B and T cell responses against DENV and ZIKV and propose guidelines for the development of safe and efficient T cell vaccines targeting both viruses.

Identification of putative unique immunogenic ZIKV and DENV1-4 peptides for diagnostic cellular based tests

Since the re-emergence of Zika virus in 2014 and subsequent association with microcephaly, much work has focused on the development of a vaccine to halt its spread throughout the world. The mosquito vector that transmits this virus is widespread and responsible for the spread of other arboviridae including Dengue. Current diagnostic methods rely on serologic testing that are complicated by cross reactivity and therefore unable to distinguish Zika from Dengue infection in the absence of virus isolation. We performed an in silico analysis to identify potential epitopes that may stimulate a unique T-lymphocyte response to distinguish prior infection with Zika or Dengue. From this analysis, we not only identified epitopes unique to Zika and Dengue, but also identified epitopes unique to each Dengue serotype. These peptides contribute to a pool of peptides identified for vaccine development that can be tested in vitro to confirm immunogenicity, absence of homology and global population coverage. The current lack of accurate diagnostic testing hampers our ability to understand the scope of the epidemic, implications for vaccine implementation and complications related to monoinfection and co-infection with these two closely related viruses.

Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins

The recent outbreak of Zika virus (ZIKV) infection in Brazil has developed to a global health concern due to its likely association with birth defects (primary microcephaly) and neurological complications. Consequently, there is an urgent need to develop a vaccine to prevent or a medicine to treat the infection. In this study, immunoinformatics approach was employed to predict antigenic epitopes of Zika viral proteins to aid in development of a peptide vaccine against ZIKV. Both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted for ZIKV Envelope (E), NS3 and NS5 proteins. We further investigated the binding interactions of altogether 15 antigenic CTL epitopes with three class I major histocompatibility complex (MHC I) proteins after docking the peptides to the binding groove of the MHC I proteins. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlight the limits of rigid-body docking methods. Some of the antigenic epitopes predicted and analyzed in this work might present a preliminary set of peptides for future vaccine development against ZIKV.