A Computational Approach for Designing a Universal Epitope-Based Peptide Vaccine Against Nipah Virus

Pangenomic and immunoinformatics based analysis of Nipah virus revealed CD4+ and CD8+ T-Cell epitopes as potential vaccine candidates

Introduction: Nipah (NiV) is the zoonotic deadly bat-borne virus that causes neurological and respiratory infections which ultimately lead to death. There are 706 infected cases reported up till now especially in Asia, out of which 409 patients died. There is no vaccine and effective treatment available for NiV infections and we have to timely design such strategies as world could not bear another pandemic situation. Methods: In this study, we screened viral proteins of NiV strains based on pangenomics analysis, antigenicity, molecular weight, and sub-cellular localization. The immunoproteomics based approach was used to predict T-cell epitopes of MHC class-I and II as potential vaccine candidates. These epitopes are capable to activate CD4+, CD8+, and T-cell dependent B-lymphocytes. Results: The two surface proteins including fusion glycoprotein (F) and attachment glycoprotein (G) are antigenic with molecular weights of 60 kDa and 67 kDa respectively. Three epitopes of F protein (VNYNSEGIA, PNFILVRNT, and IKMIPNVSN) were ranked and selected based on the binding affinity with MHC class-I, and 3 epitopes (VILNKRYYS, ILVRNTLIS, and VKLQETAEK) with MHC-II molecules. Similarly, for G protein, 3 epitopes each for MHC-I (GKYDKVMPY, ILKPKLISY, and KNKIWCISL) and MHC-II (LRNIEKGKY, FLIDRINWI, and FLLKNKIWC) with substantial binding energies were predicted. Based on the physicochemical properties, all these epitopes are non-toxic, hydrophilic, and stable. Conclusion: Our vaccinomics and system-level investigation could help to trigger the host immune system to prevent NiV infection.

Novel "GaEl Antigenic Patches" Identified by a "Reverse Epitomics" Approach to Design Multipatch Vaccines against NIPAH Infection, a Silent Threat to Global Human Health

Nipah virus (NiV) is a zoonotic virus that causes lethal encephalitis and respiratory disease with the symptom of endothelial cell-cell fusion. Several NiV outbreaks have been reported since 1999 with nearly annual occurrences in Bangladesh. The outbreaks had high mortality rates ranging from 40 to 90%. No specific vaccine has yet been reported against NiV. Recently, several vaccine candidates and different designs of vaccines composed of epitopes against NiV were proposed. Most of the vaccines target single protein or protein complex subunits of the pathogen. The multiepitope vaccines proposed also cover a largely limited number of epitopes, and hence, their efficiency is still uncertain. To address the urgent need for a specific and effective vaccine against NiV infection, in the present study, we have utilized the "reverse epitomics" approach ("overlapping-epitope-clusters-to-patches" method) to identify "antigenic patches" (Ag-Patches) and utilize them as immunogenic composition for multipatch vaccine (MPV) design. The designed MPVs were analyzed for immunologically crucial parameters, physiochemical properties, and interaction with Toll-like receptor 3 ectodomain. In total, 30 CTL (cytotoxic T lymphocyte) and 27 HTL (helper T lymphocyte) antigenic patches were identified from the entire NiV proteome based on the clusters of overlapping epitopes. These identified Ag-Patches cover a total of discrete 362 CTL and 414 HTL epitopes from the entire proteome of NiV. The antigenic patches were utilized as immunogenic composition for the design of two CTL and two HTL multipatch vaccines. The 57 antigenic patches utilized here cover 776 overlapping epitopes targeting 52 different HLA class I and II alleles, providing a global ethnically distributed human population coverage of 99.71%. Such large number of epitope coverage resulting in large human population coverage cannot be reached with single-protein/subunit or multiepitope based vaccines. The reported antigenic patches also provide potential immunogenic composition for early detection diagnostic kits for NiV infection. Further, all the MPVs and Toll-like receptor ectodomain complexes show a stable nature of molecular interaction with numerous hydrogen bonds, salt bridges, and nonbounded contact formation and acceptable root mean square deviation and fluctuation. The cDNA analysis shows a favorable large-scale expression of the MPV constructs in a human cell line. By utilizing the novel "reverse epitomics" approach, highly immunogenic novel "GaEl antigenic patches" (GaEl Ag-Patches), a synonym term for "antigenic patches", were identified and utilized as immunogenic composition to design four MPVs against NiV. We conclude that the novel multipatch vaccines are potential candidates to combat NiV, with greater effectiveness, high specificity, and large human population coverage worldwide.

Core Proteomics and Immunoinformatic Approaches to Design a Multiepitope Reverse Vaccine Candidate against Chagas Disease

Chagas disease is a tropical ailment indigenous to South America and caused by the protozoan parasite Trypanosoma cruzi, which has serious health consequences globally. Insect vectors transmit the parasite and, due to the lack of vaccine availability and limited treatment options, we implemented an integrated core proteomics analysis to design a reverse vaccine candidate based on immune epitopes for disease control. Firstly, T. cruzi core proteomics was used to identify immunodominant epitopes. Therefore, we designed the vaccine sequence to be non-allergic, antigenic, immunogenic, and to have better solubility. After predicting the tertiary structure, docking and molecular dynamics simulation (MDS) were performed with TLR4, MHC-I, and MHC-II receptors to discover the binding affinities. The final vaccine design demonstrated significant hydrogen bond interactions upon docking with TLR4, MHC-I, and MHC-II receptors. This indicated the efficacy of the vaccine candidate. A server-based immune simulation approach was generated to predict the efficacy. Significant structural compactness and binding stability were found based on MDS. Finally, by optimizing codons on Escherichia coli K12, a high GC content and CAI value were obtained, which were then incorporated into the cloning vector pET2+ (a). Thus, the developed vaccine sequence may be a viable therapy option for Chagas disease.

Application of reverse vaccinology to design a multi-epitope subunit vaccine against a new strain of Aeromonas veronii

BACKGROUND

Aeromonas veronii is one of the most common pathogens of freshwater fishes that cause sepsis and ulcers. There are increasing numbers of cases showing that it is a significant zoonotic and aquatic agent. Epidemiological studies have shown that A. veronii virulence and drug tolerance have both increased over the last few years as a result of epidemiological investigations. Cadaverine reverse transporter (CadB) and maltoporin (LamB protein) contribute to the virulence of A. veronii TH0426. TH0426 strain is currently showing severe cases on fish species, and its resistance against therapeutic has been increasing. Despite these devastating complications, there is still no effective cure or vaccine for this strain of A.veronii.

RESULTS

In this regard, an immunoinformatic method was used to generate an epitope-based vaccine against this pathogen. The immunodominant epitopes were identified using the CadB and LamB protein of A. veronii. The final constructed vaccine sequence was developed to be immunogenic, non-allergenic as well as have better solubility. Molecular dynamic simulation revealed significant binding stability and structural compactness. Finally, using Escherichia coli K12 as a model, codon optimization yielded ideal GC content and a higher CAI value, which was then included in the cloning vector pET2+ (a).

CONCLUSION

Altogether, our outcomes imply that the proposed peptide vaccine might be a good option for A. veronii TH0426 prophylaxis.

Designing a novel mRNA vaccine against Vibrio harveyi infection in fish: an immunoinformatics approach

Vibrio harveyi belongs to the family Vibrionaceae of class Gammaproteobacteria. Around 12 Vibrio species can cause gastroenteritis (gastrointestinal illness) in humans. A large number of bacterial particles can be found in the infected cells, which may cause death. Despite these devastating complications, there is still no cure or vaccine for the bacteria. As a result, we used an immunoinformatics approach to develop a multi-epitope vaccine against the most pathogenic hemolysin gene of V. harveyi. The immunodominant T- and B-cell epitopes were identified using the hemolysin protein. We developed a vaccine employing three possible epitopes: cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocyte epitopes, after thorough testing. The vaccine was developed to be antigenic, immunogenic, and non-allergenic, as well as have a better solubility. Molecular dynamics simulation revealed significant structural stiffness and binding stability. In addition, the immunological simulation generated by computers revealed that the vaccination might elicit immune reactions Escherichia coli K12 as a model, codon optimization yielded ideal GC content and a higher codon adaptation index value, which was then included in the cloning vector pET2+ (a). Altogether, our experiment implies that the proposed peptide vaccine might be a good option for vibriosis prophylaxis.

Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus

BACKGROUND

The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity.

METHODS

Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes.

RESULTS

Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability.

CONCLUSION

This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.

Epitope-Based Peptide Vaccine against Glycoprotein G of Nipah Henipavirus Using Immunoinformatics Approaches

Background

Nipah belongs to the genus Henipavirus and the Paramyxoviridae family. It is an endemic most commonly found at South Asia and has first emerged in Malaysia in 1998. Bats are found to be the main reservoir for this virus, causing disease in both humans and animals. The last outbreak has occurred in May 2018 in Kerala. It is characterized by high pathogenicity and fatality rates which varies from 40% to 70% depending on the severity of the disease and on the availability of adequate healthcare facilities. Currently, there are no antiviral drugs available for NiV disease and the treatment is just supportive. Clinical presentations for this virus range from asymptomatic infection to fatal encephalitis.

Objective

This study is aimed at predicting an effective epitope-based vaccine against glycoprotein G of Nipah henipavirus, using immunoinformatics approaches.

Methods and Materials

Glycoprotein G of the Nipah virus sequence was retrieved from NCBI. Different prediction tools were used to analyze the epitopes, namely, BepiPred-2.0: Sequential B Cell Epitope Predictor for B cell and T cell MHC classes II and I. Then, the proposed peptides were docked using Autodock 4.0 software program. Results and Conclusions. The two peptides TVYHCSAVY and FLIDRINWI have showed a very strong binding affinity to MHC class I and MHC class II alleles. Furthermore, considering the conservancy, the affinity, and the population coverage, the peptide FLIDRINWIT is highly suitable to be utilized to formulate a new vaccine against glycoprotein G of Nipah henipavirus. An in vivo study for the proposed peptides is also highly recommended.

Overlapping CD8+ and CD4+ T-cell epitopes identification for the progression of epitope-based peptide vaccine from nucleocapsid and glycoprotein of emerging Rift Valley fever virus using immunoinformatics approach

Rift Valley fever virus (RVFV) is an emergent arthropod-borne zoonotic infectious viral pathogen which causes fatal diseases in the humans and ruminants. Currently, no effective and licensed vaccine is available for the prevention of RVFV infection in endemic as well as in non-endemic regions. So, an immunoinformatics-driven genome-wide screening approach was performed for the identification of overlapping CD8+ and CD4+ T-cell epitopes and also linear B-cell epitopes from the conserved sequences of the nucleocapsid (N) and glycoprotein (G) of RVFV. We identified overlapping 99.39% conserved 1 CD8+ T-cell epitope (MMHPSFAGM) from N protein and 100% conserved 7 epitopes (AVFALAPVV, LAVFALAPV, FALAPVVFA, VFALAPVVF, IAMTVLPAL, FFDWFSGLM, and FLLIYLGRT) from G protein and also identified IL-4 and IFN-γ induced (99.39% conserved) 1 N protein CD4+ T-cell epitope (HMMHPSFAGMVDPSL) and 100% conserved 5 G protein CD4+ T-cell epitopes (LPALAVFALAPVVFA, PALAVFALAPVVFAE, GIAMTVLPALAVFAL, GSWNFFDWFSGLMSW, and FFLLIYLGRTGLSKM). The overlapping CD8+ and CD4+ T-cell epitopes were bound with most conserved HLA-C*12:03 and HLA-DRB1*01:01, respectively with the high binding affinity (kcal/mol). The combined population coverage analysis revealed that the allele frequencies of these epitopes are high in endemic and non-endemic regions. Besides, we found 100% conserved and non-allergenic 2 decamer B-cell epitopes, GVCEVGVQAL and RVFNCIDWVH of G protein had the sequence similarity with the nonamer CD8+ T-cell epitopes, VCEVGVQAL and RVFNCIDWV, respectively. Consequently, these epitopes may be used for the development of epitope-based peptide vaccine against emerging RVFV. However, in vivo and in vitro experiments are required for their efficient use as a vaccine.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.