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Background: Since December 2019, a novel coronavirus, SARS-CoV-2, has caused global public health issues after being reported for the first time in Wuhan province of China. So far, there have been approximately 14.8 million confirmed cases and 0.614 million deaths due to the SARS-CoV-2 infection globally, and still, numbers are increasing. Although the virus has caused a global public health concern, no effective treatment has been developed. Objective: One of the strategies to combat the COVID-19 disease caused by SARS-CoV-2 is the development of vaccines that can make humans immune to these infections. Considering this approach, in this study, an attempt has been made to design epitope-based vaccine for combatting COVID-19 disease by analyzing the complete proteome of the virus by using immuno-informatics tools. Methods: The protein sequence of the SARS-CoV-2 was retrieved and the individual proteins were checked for their allergic potential. Then, from non-allergen proteins, antigenic epitopes were identified that could bind with MHCII molecules. The epitopes were modeled and docked to predict the interaction with MHCII molecules. The stability of the epitope-MHCII complex was further analyzed by performing a molecular dynamics simulation study. The selected vaccine candidates were also analyzed for their global population coverage and conservancy among SARS-related coronavirus species. Results: The study has predicted 5 peptide molecules that can act as potential candidates for epitope- based vaccine development. Among the 5 selected epitopes, the peptide LRARSVSPK can be the most potent epitope because of its high geometric shape complementarity score, low ACE and very high response towards it by the world population (81.81% global population coverage). Further, molecular dynamic simulation analysis indicated the formation of a stable epitope-MHCII complex. The epitope LRARSVSPK was also found to be highly conserved among the SARS-CoV- -2 isolated from different countries. Conclusion: The study has predicted T-cell epitopes that can elicit a robust immune response in the global human population and act as potential vaccine candidates. However, the ability of these epitopes to act as vaccine candidate needs to be validated in wet lab studies.

Around the world, emergence and re-emergence of Dengue Virus (DENV) are one of the serious public health concerns. The infection of DENV to human population is mainly caused by the bite of an Aedes...

Whipple’s disease is one of the rare maladies in terms of spread but very fatal one as it is linked with many disorders (like Gastroenteritis, Endocarditis etc.). Also, current regimens include less effective drugs which require long duration follows up. This exploratory study was conducted to commence the investigation for crafting multi target epitope vaccine against its bacterial pathogen Tropheryma whipplei. The modern bioinformatics tools like VaxiJen, NETMHCII PAN 3.2, ALLERGEN-FP, PATCH-DOCK, TOXIC-PRED, MHCPRED and IEDB were deployed, which makes the study more intensive in analyzing proteome of T. whipplei as these methods are based on robust result generating statistical algorithms ANN, HMM, and ML. This Immuno-Informatics approach leads us in the prediction of two epitopes: VLMVSAFPL and IRYLAALHL interacting with 4 and 6 HLA DRB1 alleles of MHC Class II respectively. VLMVSAFPL epitope is a part of DNA-directed RNA polymerase subunit beta, and IRYLAALHL epitope is a part of membranous protein insertase YidC of this bacterium. Molecular-Docking and Molecular-Simulation analysis yields the perfect interaction based on Atomic contact energy, binding scores along with RMSD values (0 to 1.5 Ǻ) in selection zone. The IEDB (Immune epitope database) population coverage analysis exhibits satisfactory relevance with respect to world population.

Orthohantavirus, a zoonotic virus responsible for causing human cardio-pulmonary disease, is proven to be a fatal disease. Due to the paucity of regimens to cure the disease and efficient management to eradicate this deadly virus, there is a constant need to expand in-silico approaches belonging to immunology domain to formulate best feasible peptide-based vaccine against it. In lieu of that, we have predicted and validated an epitope of nine-residue-long sequence "MIGLLSSRI". The predicted epitope has shown best interactions with HLA alleles of MHC Class II proteins, namely HLA DRB1_0101, DRB1_0401, DRB1_0405, DRB1_0701, DRB1_0901, DRB1_1302, and DRB1_1501. The structure of the epitope was modeled by deploying PEPFOLD 3.5 and verified by Ramachandran plot analysis. Molecular docking and simulation studies reveal that this epitope has satisfactory binding scores, ACE value and global energies for docked complexes along with selectable range of RMSD and RMSF values. Also, the predicted epitope "MIGLLSSRI" exhibits population coverage of more than 62% in world population and maximum of 70% in the United States of America. In this intensive study, we have used many tools like AllergenFP, NETMHCII 3.2, VaxiJen, ToxinPred, PEPFOLD 3.5, DINC, IEDB-Population coverage, MHCPred and JCat server. Most of these tools are based on modern innovative statistical algorithms like HMM, ANN, ML, etc. that help in better predictions of putative candidates for vaccine crafting. This innovative methodology is facile, cost-effective and time-efficient, which could facilitate designing of a vaccine against this virus.

Nipah virus was first appeared in Malaysian 1998, which further found be outburst in neighbor countries i.e. Bangladesh, Singapore, and India. Currently there is no effective drug or vaccine available only supportive care and prevention are way to manage it. Epitope based vaccine could be the best way to cure Nipah virus infection. In this study, the proteome of Nipah virus was retrieve from UniProt database and were subjected to check for allergenicity using Allergen FP v.1.0 tool. NetMHCII 2.3 server screened epitopes from non-allergen proteins and Vaxijen tool was used to identify the most antigenic epitopes binds with MHC class II molecules. Two potent T cell epitopes LLFVFGPNL and KYKIKSNPL were found, which binds with HLA-DRB1*01:01 and HLA-DRB1*07:01 MHC class II alleles. PepstrMod and Swiss-Model server were used to build 3D structure model of epitopes and alleles, respectively. Further identified epitopes were docked with HLA alleles using AutoDock vina tool to confirm binding ability. The epitopes LLFVFGPNL and KYKIKSNPL had binding affinity of − 8.1 kcal/mol and − 5.7 kcal/mol with HLA-DRB1*01:01 and HLA-DRB1*07:01 alleles, respectively. Solidity of predicted epitope—allele docked complex were evaluating by molecular dynamics simulation.HLA distribution analysis was performed for predicted epitopes using the population coverage tool of Immune Epitope Database (IEDB). These predicted epitopes cover maximum number of populations in India as well as worldwide. Therefore, these epitopes could be potent vaccine candidates to counter Nipah virus by testing in wet lab.

Neural necrosis virus (NNV) of family Nodaviridae affect wide range of fish species with viral encephalopathy and retinopathy causing mass mortality up to 100%. Currently there is no effective treatment and depopulation is only suggested recommendation. New avenues and approach are required to control this harmful malady. In this study we developed an epitope-based vaccine (EBV), against NNV using computation approach. We have selected two conserved proteins RNA-dependent RNA polymerase (RdRP) and capsid proteins. Based on more than ~ 1000 epitopes we selected six antigenic epitopes. These were conjugated to adjuvant and linker peptides to generate a full-length vaccine candidate. Biochemical structural properties were analyzed by Phyre2 server. ProtParam, Molprobity. Ramachandran plot results indicate that 98.7% residues are in a favorable region and 93.4% residues in the favored region. The engineered EBV binds to toll like receptor-5 (TLR5) an important elicitor of immune response. Further molecular docking by PatchDock server reveals the atomic contact energy (i.e. - 267.08) for the best docked model of EBV and TLR5 receptor. The molecular simulation results suggest a stable interaction; the RMSD and RMSF values are 1-4 Ǻ and 1-12Ǻ, respectively. Further we have suggested the best possible codon optimized sequence for its cloning and subsequent purification of the protein. Overall, this is a first report to suggest an in-silico method for generation of an EBV candidate against NNV. We surmise that the method and approach suggested could be used as a promising cure for NNVs.

Candida auris is a rapidly emerging human pathogenic fungus with a high mortality rate. Recent report suggests that the new clinical isolates are showing resistance to the major classes of antifungal drugs. Due to the emergence of drug resistance, it becomes imperative to seek novel therapies for the treatment of C. auris. The potent vaccine could be one of the promising strategies for recalcitrant and multidrug-resistant pathogens. Using in silico approach we designed a novel multivalent vaccine against C. auris. We have selected the agglutinin-like sequence-3 (Als3) an adhesion protein, involved in virulence. The Als3p protein of C. auris was targeted to predict T cell and B cell epitopes. Epitopes which were found to be non-toxic, non-allergenic, highly conserved, and antigenic and could induce interferon-γ synthesis were selected for vaccine design. The selected epitopes were linked with suitable adjuvants to construct the final vaccine. The vaccine construct was predicted to be stable, soluble, antigenic, non-allergic with desirable physicochemical properties. We also constructed the 3D model of the vaccine and validated it with the Ramachandran plot. The ability of the vaccine construct to interact with Toll-like receptor (TLR) and major histocompatibility complex (MHC) was determined by molecular docking experiments. The binding energy of the vaccine construct with the TLR and MHC were found to be stable as predicted by molecular dynamics simulation. Further, in-silico cloning analysis showed that the vaccine construct can be successfully cloned and expressed in E. coli. Based on the results, we surmise that our candidate vaccine can be used as an alternative therapy for the treatment of C. auris. However, the efficacy and the safety of the vaccine model need to be determined by performing in vivo studies.

Zika fever is a disease caused by Zika virus which is a mosquito-borne flavivirus and infection is through the bite of mosquito from the Aedes genus. Other mode of transmission of Zika fever is blood transfusion, which is thoroughly investigated. Prominent symptoms of Zika virus fever are mild fever, skin rash, conjunctivitis, muscle and joint pain, malaise or headache. These symptoms normally last for 2–7 days. There is currently no effective and easily affordable vaccine available against Zika virus. Keeping the current status in mind we have devised our objectives to design a peptide-based vaccine against Zika Virus using in-silico approach. The NS3 and NS5 proteins of Zika virus are well known of having important role in replication of the virus that is why we have targeted these proteins for epitope prediction. Prediction and screening of epitopes was performed using IEDB tool. We have also checked the efficacy of these screened epitopes with the help of IC50 value and docking score. “VEMGEAAAI” was found to be the high affinity binding epitope for MHC Class I and MHC Class II molecule. These findings conclude that the predicted epitope could well be used as a potential vaccine candidate for formulations of vaccines against Zika virus.

A new virus termed SARS-COV-2 (causing COVID-19 disease) can exhibit a progressive, fatal impact on individuals. The World Health Organization (WHO) has declared the spread of the virus to be a global pandemic. Currently, there are over 1 million cases and over 100,000 confirmed deaths due to the virus. Hence, prophylactic and therapeutic strategies are promptly needed. In this study we report an epitope, ITLCFTLKR, which is biochemically fit to HLA allelic proteins. We propose that this could be used as a potential vaccine candidate against SARS-COV-2. A selected putative epitope and HLA-allelic complexes show not only better binding scores, but also RMSD values in the range of 0–1 A. This epitope was found to have a 99.8% structural favorability as per Ramachandran-plot analysis. Similarly, a suitable range of IC50 values and population coverage was obtained to represent greater validation of T-cell epitope analysis. Stability analysis using MDWeb and half-life analysis using the ProtParam tool has confirmed that this epitope is well-selected. This new methodology of epitope-based vaccine prediction is fundamental and fast in application, ad can be economically beneficial and viable.

Human Cytomegalovirus (HCMV) is one of the most common causes of congenital infections globally. They infect the fetus and cause severe birth defects such as hearing loss, complications in the eyes, epilepsy, autism, and various other neurological disorders. They are also the leading cause of death in patients with compromised immunity. Due to the prevalence and severity of the virus, the United States National Academy of Medicine has announced the development of the HCMV vaccine as a matter of high priority for public health. Hence, this study is focused on designing a novel potent multi-epitope HCMV vaccine. The envelope glycoproteins B, H, L and M of HCMV were targeted to predict T cell (both Helper and Cytotoxic) epitopes that were non-toxic, highly antigenic, and non-allergenic and could induce interferon-γ production. Further, the selected epitopes were subjected to molecular docking and population coverage analysis. Eventually, using the two best epitopes and adjuvants like RS09, flagellin protein and PADRE sequence a 283 amino acid long vaccine was designed. The vaccine is antigenic, non-allergenic, stable, and soluble. The 3D model of the vaccine was developed and validated using the Ramachandran plot. Docking with Toll-like receptor 5 (TLR5) molecule and molecular dynamics simulation confirmed the stability of the vaccine. In silico cloning simulation using pET28a (+) vector showed that the vaccine can be successfully cloned and expressed in E. coli K12 strain. However, the efficacy and the safety of the vaccine need to be further validated by in vivo studies.

Introduction of vaccines has been one of the most vital offering of immunology to public health till date. Present influenza vaccines basically induce antibody responses to the hemagglutinin protein of the viral strains selected for the vaccine. Peptide based vaccine are formulated by combining conserved B and T cell epitopes. Recent progress has been made in the discovery of vaccines based on use of peptides. This study evaluates the capacity of four epitopes to induce specific immune response and protect human against viral infection. Predicted YEELREQLSSVSS epitope could further be taken by scientist for development of vaccine against swine flu.

Epitopes play a vital role in the development of peptide based vaccine and diagnosis of diseases including HCV treatment based on immunotherapy and, antigenic drift is well established fact within the epitopic region. However silver-lining in this problem is that the epitopes are distributed among a handful of proteins only and slight changes or mutations within sequence of this group of proteins make alteration of their epitope structures which finally lead to ineffectiveness of any drug or vaccine meant to contain this organism through interacting with these proteins. Since experimental methods for identifying epitope is costly and time-consuming, computational and statistical approaches are quite often considered to accelerate prediction of epitopes. Apart from the fact that more than ninety percent of epitopes are conformational in nature, prediction of b-cell based linear epitope remains a challenge for computational biology. This piece of work dealt with the identification step of B-cell based linear epitopes recruiting Elman backpropagation of neural network inputted by features extracted through statistics of apparently unrelated physicochemical and some other judiciously selected properties of amino acids. As a result, a significantly high accuracy was achieved for B-cell based linear epitope identification.

Hepatitis C is a severe disease caused by Hepatitis C virus which leads to human fatality and affected 180 million people across the globe. Its chronic infection leads to liver damage and malignant hepatoma. Till now there is no vaccine in the market for this virus. The objective of the study was to predict the best epitope using Bioinformatics tools for designing a vaccine against HCV. Here T-cell epitope was considered since it can recognize only antigen that processes to generate peptide by antigen presenting cell. For selecting the best T cell epitope, the binding energy with the MHC molecule must be high, must have a protease cleavage site, conserved site, motif, good binder with hydrophobic binding pocket and half-life of dissociation must be high. By considering above criteria suitable bioinformatics tools were used to predict the epitopes from NS3, NS5A and NS5B of 3a and 3b genotype. A total of 600 epitopes from different tools for each protein were predicted and from there only 11 efficient epitopes was virtually screened out using protein-protein interaction between MHC-I and MHC-II molecules and their energy. IMYAPTIWV peptide of NS5A protein was found to be the best epitope. The selected epitope for T-cell can further be used for future work in a wet laboratory for the development of vaccine against HCV.

Hepatitis C virus (HCV) being an infectious disease is prevalent in most parts of the world. Till date, no vaccine is being developed in the market for HCV. This paper focuses mainly on developing a peptide-based vaccine against HCV. The purpose for this study is taken to determine the suitable epitope with the help of Bioinformatics tools developed for designing a vaccine against infectious diseases such as HCV. In present work, T-cell epitope is taken into consideration, as it recognises the antigen that helps to generate peptide with the help of antigen presenting cell. With respect to T-cell epitope selection, high binding energy must be required for binding major histocompatibility complex molecule. Moreover, T-cell epitope were considered on the basis of conserved site, protease cleavage site, motif, as well as an excellent hydrophobic binding pocket with a high half-life of dissociation. In consideration to the mentioned criteria, the required bioinformatics tools are used which are designed to predict the epitopes from different envelope and non-structural proteins of HCV virus. On an average, 1,000 epitopes from various databases and tools were extracted, from which 11 adept epitopes were withdrawn virtually with a base of binding energy using MHC I and II molecule protein interaction. The best epitope predicted during study was IMYAPTIWV peptide of NS5A protein. The T-cell predicted epitope can be further used for later chore in vaccine discovery for HCV.