Helper T-cell Epitopes Research Articles

MMV-db: vaccinomics and RNA-based therapeutics database for infectious hemorrhagic fever-causing mammarenaviruses.

The recent viral outbreaks and the current pandemic situation urges us to timely address any emerging viral infections by designing therapeutic strategies. Multi-omics and therapeutic data are of great interest to develop early remedial interventions. This work provides a therapeutic data platform (Mammarenavirus (MMV)-db) for pathogenic mammarenaviruses with potential catastrophic effects on human health around the world. The database integrates vaccinomics and RNA-based therapeutics data for seven human pathogenic MMVs associated with severe viral hemorrhagic fever and lethality in humans. Protein-specific cytotoxic T lymphocytes, B lymphocytes, helper T-cell and interferon-inducing epitopes were mapped using a cluster of immune-omics-based algorithms and tools for the seven human pathogenic viral species. Furthermore, the physiochemical and antigenic properties were also explored to guide protein-specific multi-epitope subunit vaccine for each species. Moreover, highly efficacious RNAs (small Interfering RNA (siRNA), microRNA and single guide RNA (sgRNA)) after extensive genome-based analysis with therapeutic relevance were explored. All the therapeutic RNAs were further classified and listed on the basis of predicted higher efficacy. The online platform (http://www.mmvdb.dqweilab-sjtu.com/index.php) contains easily accessible data sets and vaccine designs with potential utility in further computational and experimental work. Conclusively, the current study provides a baseline data platform to secure better future therapeutic interventions against the hemorrhagic fever causing mammarenaviruses.Database URL: http://www.mmvdb.dqweilab-sjtu.com/index.php

Subtractive Proteomics and Immuno-informatics Approaches for Multi-peptide Vaccine Prediction Against Klebsiella oxytoca and Validation Through In Silico Expression.

Klebsiella oxytoca is a gram-negative bacterium. It is opportunistic in nature and causes hospital acquired infections. Subtractive proteomics and reverse vaccinology approaches were employed to screen out the best proteins for vaccine designing. Whole proteome of K. oxytoca strain ATCC 8724, consisting of 5483 proteins, was used for designing the vaccine. Total 1670 cytotoxic T lymphocyte (CTL) epitope were predicted through NetCTL while 1270 helper T lymphocyte (HTL) epitopes were predicted through IEDB server. The epitopes were screened for non-toxicity, allergenicity, antigenicity and water solubility. After epitope screening 300 CTL and 250 HTL epitopes were submitted to IFN-γ epitope server to predict their Interferon-γ induction response. The selected IFN-γ positive epitopes were tested for their binding affinity with MHCI-DRB1 by MHCPred. The 15 CTL and 13 HTL epitopes were joined by linkers AAY and GPGPG respectively in vaccine construct. Chain C of Pam3CSK4 (PDB ID; 2Z7X) was linked to the vaccine construct as an adjuvant. A 450aa long vaccine construct was submitted to I-TASSER server for 3D structure prediction. Thirteen Linear B cells were predicted by ABCPred server and 10 sets of discontinues epitopes for 3D vaccine structure were predicted by DiscoTope server. The modeled 3D vaccine construct was docked with human Toll-like receptor 2 (PDB ID: 6NIG) by PatchDock. The docked complexes were refined by FireDock. The selected docked complex showed five hydrogen bonds and one salt bridge. The vaccine sequence was reverse transcribed to get nucleotide sequence for In silico cloning. The reverse transcribed sequence strand was cloned in pET28a(+) expression vector. A clone containing 6586 bp was constructed including the 450 bp of query gene sequence.

Helicase antigen (HAGE)-derived vaccines induce immunity to HAGE and ImmunoBody®-HAGE DNA vaccine delays the growth and metastasis of HAGE-expressing tumors invivo.

The management of patients with triple-negative breast cancer (TNBC) continues to pose a significant clinical challenge. Less than 30% of women with metastatic TNBC survive 5years, despite adjuvant chemotherapy and the initial higher rates of clinical response that can be achieved with neoadjuvant chemotherapy. ImmunoBody is a plasmid DNA designed to encode a human antibody molecule with complementarity-determining regions engineered to express cytotoxic and helper T-cell epitopes derived from the cancer antigen of interest. The helicase antigen (HAGE) is a cancer testis antigen, which is expressed in TNBC. Herein, we have identified a 30-amino-acid-long HAGE-derived sequence containing human leukocyte antigen (HLA)-A2- and HLA-DR1-restricted epitopes and demonstrated that the use of this sequence as a peptide (with CpG/incomplete Freund's adjuvant) or incorporated into an ImmunoBody vaccine can generate specific interferon-γ-secreting splenocytes in HHDII+ DR1+ mice. T-cell responses elicited by the ImmunoBody-HAGE vaccine were superior to peptide immunization. Moreover, splenocytes from ImmunoBody-HAGE-vaccinated mice stimulated invitro could recognize HAGE+ tumor cells and the human TNBC cell line MDA-MB-231. More importantly, the growth of implanted HHDII+ DR1+ HAGE+ Luc+ B16 cells.

In silico analysis and prediction of immunogenic epitopes for pre-erythrocytic proteins of the deadly Plasmodium falciparum.

Malaria is the deadliest parasitic disease in tropical and subtropical areas around the world, with considerable morbidity and mortality, particularly due to the life-threatening Plasmodium falciparum. The present in silico investigation was performed to reveal the biophysical characteristics and immunogenic epitopes of the six pre-erythrocytic proteins of the P. falciparum using comprehensive immunoinformatics approaches. For this aim, different web servers were employed to predict subcellular localization, antigenicity, allergenicity, solubility, physico-chemical properties, post-translational modification sites (PTMs), the presence of signal peptide and transmembrane domains. Moreover, the secondary and tertiary structures of the proteins were revealed followed by refinement and validations. Finally, NetCTL server was used to predict cytotoxic T-lymphocyte (CTL) epitopes, followed by subsequent screening in terms of antigenicity and immunogenicity. Also, IEDB server was utilized to predict helper T-lymphocyte (HTL) epitopes, followed by screening regarding interferon gamma induction and population coverage. These proteins showed appropriate antigenicity, abundant PTMs as well as many CTL and HTL epitopes, which could be directed for future vaccination studies in the context of multi-epitope vaccine design.

Prioritization of potential vaccine candidates and designing a multiepitope-based subunit vaccine against multidrug-resistant Salmonella Typhi str. CT18: A subtractive proteomics and immunoinformatics approach

Salmonella enterica serovar Typhi (S. Typhi), a causative agent of typhoid fever, is a Gram-negative, human-restricted pathogen that causes significant morbidity and mortality, particularly in developing countries. The currently available typhoid vaccines are not recommended to children below six years of age and have poor long-term efficacy. Due to these limitations and the emerging threat of multidrug-resistance (MDR) strains, the development of a new vaccine is urgently needed. The present study aims to design a multiepitope-based subunit vaccine (MESV) against MDR S. Typhi str. CT18 using a computational-based approach comprising subtractive proteomics and immunoinformatics. Firstly, we investigated the proteome of S. Typhi str. CT18 using subtractive proteomics and identified twelve essential, virulent, host non-homologous, and antigenic outer membrane proteins (OMPs) as potential vaccine candidates with low transmembrane helices (≤1) and molecular weight (≤110 kDa). The OMPs were mapped for cytotoxic T lymphocyte(CTL) epitopes, helper T lymphocyte (HTL) epitopes, and linear B lymphocyte (LBL) epitopes using various immunoinformatics tools and servers. A total of 6, 12, and 11 CTL, HTL, and LBL epitopes were shortlisted, respectively, based on their immunogenicity, antigenicity, allergenicity, toxicity, and hydropathicity potential. Four MESV constructs (MESVCs), MESVC-1, MESVC-2, MESVC-3, and MESVC-4, were designed by linking the CTL, HTL, and LBL epitopes with immune-modulating adjuvants, linkers, and PADRE (Pan HLA DR-binding epitope) sequences. The MESVCs were evaluated for their physicochemical properties, allergenicity, antigenicity, toxicity, and solubility potential to ensure their safety and immunogenic behavior. Secondary and tertiary structures of shortlisted MESVCs (MESVC-1, MESVC-3, and MESVC-4) were predicted, modeled, refined, validated, and then docked with various MHC I, MHC II, and TLR4/MD2 complex. Molecular dynamics (MD) simulation of the final selected MESVC-4 with TLR4/MD2 complex confirms its binding affinity and stability. Codon optimization and in silico cloning verified the translation efficiency and successful expression of MESVC-4 in E. coli str. K12. Finally, the efficiency of MESVC-4 to trigger an effective immune response was assessed by an in silico immune simulation. In conclusion, our findings show that the designed MESVC-4 can elicit humoral and cellular immune responses, implying that it may be used for prophylactic or therapeutic purposes. Therefore, it should be subjected to further experimental validations.

Poly(hydrophobic amino acid) Conjugates for the Delivery of Multiepitope Vaccine against Group A Streptococcus.

Peptide-based vaccines are composed of small, defined, antigenic peptide epitopes. They are designed to induce well-controlled immune responses. Multiple epitopes are often employed in these vaccines to cover strain variability of a pathogen. However, peptide epitopes cannot stimulate adequate immune responses on their own and require an adjuvant (immune stimulant) and/or delivery system. Here, we designed and synthesized a multiepitope vaccine candidate against Group A Streptococcus (GAS) composed of several B-cell epitopes (J8, PL1, and 88/30) derived from GAS M-protein, universal PADRE T-helper cell epitope, and a polyleucine self-adjuvanting unit. The vaccine components were conjugated together (using mercapto-maleimide and azide-alkyne Huisgen cycloaddition reactions) or delivered as a mixture. The conjugated multiepitope vaccine candidate self-assembled into small nanoparticles and chain-like aggregated nanoparticles (CLANs) that were able to induce the production of J8-, PL1-, and 88/30-specific antibodies in mice. The multiepitope conjugate and the physical mixture of conjugates bearing the individual epitopes produced similar nanoparticles and induced comparable immune responses. Hence, simple physical mixing can replace complex chemical conjugation to produce multiepitope nanoparticles with equivalent morphology and immunological efficacy. This greatly simplifies vaccine production.

In silico design and in vitro expression of novel multiepitope DNA constructs based on HIV-1 proteins and Hsp70 T-cell epitopes.

ObjectivesEpitope-driven vaccines carrying highly conserved and immunodominant epitopes have emerged as promising approaches to overcome human immunodeficiency virus-1 (HIV-1) infection.MethodsTwo multiepitope DNA constructs encoding T cell epitopes from HIV-1 Gag, Pol, Env, Nef and Rev proteins alone and/or linked to the immunogenic epitopes derived from heat shock protein 70 (Hsp70) as an immunostimulatory agent were designed. In silico analyses were applied including MHC-I and MHC-II binding, MHC-I immunogenicity and antigen processing, population coverage, conservancy, allergenicity, toxicity and hemotoxicity. The peptide-MHC-I/MHC-II molecular docking and cytokine production analyses were carried out for predicted epitopes. The selected highly immunogenic T-cell epitopes were then used to design two multiepitope fusion constructs. Next, prediction of the physicochemical and structural properties, B cell epitopes, and constructs-toll-like receptors (TLRs) molecular docking were performed for each construct. Finally, the eukaryotic expression plasmids harboring totally 12 cytotoxic T Lymphocyte (CTL) and 10 helper T lymphocytes (HTL) epitopes from HIV-1 proteins (i.e., pEGFP-N1-gag-pol-env-nef-rev), and linked to 2 CTL and 2 HTL epitopes from Hsp70 (i.e., pEGFP-N1-hsp70-gag-pol-env-nef-rev) were generated and transfected into HEK-293 T cells for evaluating the percentage of multiepitope peptides expression using flow cytometry and western blotting.ResultsThe designed DNA constructs could be successfully expressed in mammalian cells. The expression rates of Gag-Pol-Env-Nef-Rev-GFP and Hsp70-Gag-Pol-Env-Nef-Rev-GFP were about 56–60% as the bands of ~ 63 and ~ 72 kDa confirmed in western blotting, respectively.ConclusionThe combined in silico/in vitro methods indicated two multiepitope constructs can be produced and used as probable effective immunogens for HIV-1 vaccine development.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10529-021-03143-9.

Single-component multilayered self-assembling nanoparticles presenting rationally designed glycoprotein trimers as Ebola virus vaccines

Ebola virus (EBOV) glycoprotein (GP) can be recognized by neutralizing antibodies (NAbs) and is the main target for vaccine design. Here, we first investigate the contribution of the stalk and heptad repeat 1-C (HR1C) regions to GP metastability. Specific stalk and HR1C modifications in a mucin-deleted form (GPΔmuc) increase trimer yield, whereas alterations of HR1C exert a more complex effect on thermostability. Crystal structures are determined to validate two rationally designed GPΔmuc trimers in their unliganded state. We then display a modified GPΔmuc trimer on reengineered protein nanoparticles that encapsulate a layer of locking domains (LD) and a cluster of helper T-cell epitopes. In mice and rabbits, GP trimers and nanoparticles elicit cross-ebolavirus NAbs, as well as non-NAbs that enhance pseudovirus infection. Repertoire sequencing reveals quantitative profiles of vaccine-induced B-cell responses. This study demonstrates a promising vaccine strategy for filoviruses, such as EBOV, based on GP stabilization and nanoparticle display.

Exploring SARS-COV-2 structural proteins to design a multi-epitope vaccine using immunoinformatics approach: An in silico study

In December 2019, a new virus called SARS-CoV-2 was reported in China and quickly spread to other parts of the world. The development of SARS-COV-2 vaccines has recently received much attention from numerous researchers. The present study aims to design an effective multi-epitope vaccine against SARS-COV-2 using the reverse vaccinology method. In this regard, structural proteins from SARS-COV-2, including the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, were selected as target antigens for epitope prediction. A total of five helper T lymphocytes (HTL) and five cytotoxic T lymphocytes (CTL) epitopes were selected after screening the predicted epitopes for antigenicity, allergenicity, and toxicity. Subsequently, the selected HTL and CTL epitopes were fused via flexible linkers. Next, the cholera toxin B-subunit (CTxB) as an adjuvant was linked to the N-terminal of the chimeric structure. The proposed vaccine was analyzed for the properties of physicochemical, antigenicity, and allergenicity. The 3D model of the vaccine construct was predicted and docked with the Toll-like receptor 4 (TLR4). The molecular dynamics (MD) simulation was performed to evaluate the stable interactions between the vaccine construct and TLR4. The immune simulation was also conducted to explore the immune responses induced by the vaccine. Finally, in silico cloning of the vaccine construct into the pET-28 (+) vector was conducted. The results obtained from all bioinformatics analysis stages were satisfactory; however, in vitro and in vivo tests are essential to validate these results.

Prophylactic Cancer Vaccines Engineered to Elicit Specific Adaptive Immune Response

Vaccines have been used to prevent and eradicate different diseases for over 200 years, and new vaccine technologies have the potential to prevent many common illnesses. Cancer, despite many advances in therapeutics, is still the second leading causes of death in the United States. Prophylactic, or preventative, cancer vaccines have the potential to reduce cancer prevalence by initiating a specific immune response that will target cancer before it can develop. Cancer vaccines can include many different components, such as peptides and carbohydrates, and be fabricated for delivery using a variety of means including through incorporation of stabilizing chemicals like polyethylene glycol (PEG) and pan-DR helper T-lymphocyte epitope (PADRE), fusion with antigen-presenting cells (APCs), microneedle patches, and liposomal encapsulation. There are currently five cancer vaccines used in the clinic, protecting against either human papillomavirus (HPV) or hepatitis B virus (HBV), and preventing several different types of cancer including cervical and oral cancer. Prophylactic cancer vaccines can promote three different types of adaptive responses: humoral (B cell, or antibody-mediated), cellular (T cell) or a combination of the two types. Each vaccine has its advantages and challenges at eliciting an adaptive immune response, but these prophylactic cancer vaccines in development have the potential to prevent or delay tumor development, and reduce the incidence of many common cancers.

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus.

Scouting the receptor-binding Domain of SARS Coronavirus 2: A Comprehensive Immunoinformatics Inquisition

Aim: December 2019 witnessed the emergence of a worldwide outbreak of a novel strain of coronavirus (CoV) termed SARS-CoV-2. Several preventive strategies are being developed, such as vaccines, to stop the spread of infection. Materials & methods: A comprehensive immunoinformatics approach was used to map conserved peptide sequences on the receptor binding domain of SARS-CoV-2 for their B-cell, T-helper & T-cytotoxic cell epitope profiles. Results & conclusion: The antigenic B-cell epitopes were LFRKSN and SYGFQPT. Among T-cell epitopes, CVADYSVLY and FTNVYADSF exhibited affinity for MHC class I, while YRLFRKSNL and VYAWNRKRI exhibited affinity for of MHC class II alleles. The overlapping epitope between B- and T-cells was YRLFRKSNL. The deployment of these epitopes in potential vaccine development against COVID-19 may help in slowing down the SARS-CoV-2 spread.

Applying high throughput and comprehensive immunoinformatics approaches to design a trivalent subunit vaccine for induction of immune response against emerging human coronaviruses SARS-CoV, MERS-CoV and SARS-CoV-2

Coronaviruses (CoVs) cause diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19). Therefore, this study was conducted to combat major CoVs via a trivalent subunit vaccine, which was engineered by implementing sequences of spike (S) protein, nucleocapsid (N), envelope (E), membrane (M) protein, non-structural protein (nsp) 3, and nsp8 antigens. The CTL, HTL, MHC I, and IFN-γ epitopes were predicted via CTLPRED, IEDB, and IFN epitope servers, respectively. Also, to stimulate strong helper T lymphocytes (HTLs) responses, Pan HLA DR-binding epitope (PADRE) was used. Also, for boosting the immune response, β-defensin 2 was added to the construct as an adjuvant. Furthermore, TAT was applied to the vaccine to facilitate the intracellular delivery. Finally, TAT, adjuvant, PADRE, and selected epitopes were appropriately assembled. Based on the predicted epitopes, a trivalent multi-epitope vaccine with a molecular weight of 74.8 kDa was constructed. Further analyses predicted the molecule to be a strong antigen, and a non-allergenic and soluble protein. Secondary and tertiary structures were predicted. Additionally, analyses validated the stability of the proposed vaccine. Molecular docking and molecular dynamics simulation (MDS) showed binding affinity and stability of the vaccine-TLR3 complex was favorable. The predicted epitopes demonstrated a strong potential to stimulate T and B-cell mediated immune responses. Furthermore, codon optimization and in silico cloning guaranteed increased expression. In summary, investigations demonstrated that this next-generation approach might provide a new horizon for the development of a highly immunogenic vaccine against SARS‐CoV, MERS‐CoV, and SARS-CoV-2. Communicated by Ramaswamy H. Sarma

In Silico and in Vivo Analysis of HIV-1 Rev Regulatory Protein for Evaluation of a Multiepitope-based Vaccine Candidate

ABSTRACT In silico-designed multiepitope conserved regions of human immunodeficiency virus 1 (HIV-1) proteins would be a beneficial strategy for antigen design which induces effective anti-HIV-1 T-cell responses. The conserved multiple HLA-DR-binding epitopes of Rev protein were identified using IEDB MHC-I prediction tools and SYFPEITHI webserver to screen potential T-cell epitopes. We analyzed toxicity, allergenicity, immunogenicity, hemolytic activity, cross-reactivity, cell-penetrating peptide (CPP) potency, and molecular docking of the candidate epitopes using several immune-informatics tools. Afterward, we designed a novel multiepitope construct based on non-toxic and non-allergenic Rev, Nef, Gp160 and P24-derived cytotoxic T cell (CTL) and T-helper cell (HTL) epitopes. Next, the designed construct (Nef-Rev-Gp160-P24) was subjected to three B-cell epitope prediction webservers, ProtParam and Protein-Sol to obtain the physicochemical features. Then, the recombinant multiepitope DNA and polypeptide constructs were complexed with different CPPs for nanoparticle formation and pass them via the cell membranes. Finally, the immunogenicity of multiepitope constructs in a variety of modalities was evaluated in mice. The results demonstrated that groups immunized with heterologous DNA+ MPG or HR9 CPP prime/rNef-Rev-Gp160-P24 polypeptide + LDP-NLS CPP boost regimens could significantly produce higher levels of IFN-γ and Granzyme B, and lower amounts of IL-10 than other groups. Moreover, higher levels of IgG2a and IgG2b were observed in all heterologous prime-boost regimens than homologous DNA or polypeptide regimens. Altogether, the present findings indicated that the Nef-Rev-Gp160-P24 polypeptide meets the criteria to be potentially useful as a multiepitope-based vaccine candidate against HIV-1 infection.

Computer-Assisted Multi-Epitopes T-Cell Subunit Covid-19 Vaccine Design

Background: The world is currently facing the coronavirus disease-2019 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers from different parts of the world have employed diverse approaches to create a safe and effective vaccine as it saves millions of lives. Vaccines are created from the viral particle to train the body for a natural defense against invading pathogens. It is important to understand the concept of the vaccine design, especially the multi-epitope T-cells subunit vaccine. Methods: In this regard, we employed bioinformatics and immunoinformatic tools to illustrate the concept of the computer-based vaccine design. The computational methods consist of evaluation and selection of SARS-CoV-2 structural proteins, prediction of cytotoxic T-lymphocyte (CTL) epitopes, prediction of helper T-cell (HTL) epitope, multi-epitope vaccine candidate construct, antigenicity and allergenicity prediction of the designed candidate vaccine, physiochemical properties and solubility evaluation, secondary/tertiary structure prediction, refinement and validation of model vaccine tertiary structure, molecular docking of fusion proteins and Toll-like receptor 9 protein, and in silico cloning of the vaccine. Results: A total of 454 amino acid sequences were generated from CTL and HTL epitopes. The query solubility value (QuerySol) of the vaccine construct was 0.419, including the human β-defensin-2 adjuvant and peptide linkers. A circular clone of vaccine and pEX-C-His plasmid was achieved after in silico ligation using the annealed primer. Conclusion: Here, we provide essential information on computer-assisted multi-epitopes T-cell subunit vaccine design.

A comprehensive screening of the whole proteome of hantavirus and designing a multi-epitope subunit vaccine for cross-protection against hantavirus: Structural vaccinology and immunoinformatics study

Hantaviruses are an emerging zoonotic group of rodent-borne viruses that are having serious implications on global public health due to the increase in outbreaks. Since there is no permanent cure, there is increasing interest in developing a vaccine against the hantavirus. This research aimed to design a robust cross-protective subunit vaccine using a novel immunoinformatics approach. After careful evaluation, the best predicted cytotoxic & helper T-cell and B-cell epitopes from nucleocapsid proteins, glycoproteins, RdRp proteins, and non-structural proteins were considered as potential vaccine candidates. Among the four generated vaccine models with different adjuvant, the model with toll-like receptor-4 (TLR-4) agonist adjuvant was selected because of its high antigenicity, non-allergenicity, and structural quality. The selected model was 654 amino acids long and had a molecular weight of 70.5 kDa, which characterizes the construct as a good antigenic vaccine candidate. The prediction of the conformational B-lymphocyte (CBL) epitope secured its ability to induce the humoral response. Thereafter, disulfide engineering improved vaccine stability. Afterwards, the molecular docking confirmed a good binding affinity of −1292 kj/mol with considered immune receptor TLR-4 and the dynamics simulation showed high stability of the vaccine-receptor complex. Later, the in silico cloning confirmed the better expression of the constructed vaccine protein in E. coli K12. Finally, in in silico immune simulation, significantly high levels of immunoglobulin M (IgM), immunoglobulin G1 (IgG1), cytotoxic & helper T lymphocyte (CTL & HTL) populations, and numerous cytokines such as interferon-γ (IFN-γ), interleukin-2 (IL-2) etc. were found as coherence with actual immune response and also showed faster antigen clearance for repeated exposures. Nonetheless, experimental validation can demonstrate the safety and cross-protective ability of the proposed vaccine to fight against hantavirus infection.

Multiple epitope-based vaccine prediction against SARS-CoV-2 spike glycoprotein

The global emergence of novel coronavirus disease and its rapid global expansion over a short span of time require effective countermeasures to combat it. Development of a specific vaccine can induce an optimal antibody response, thus providing immunity against it. Our study proposes a detailed and comprehensive immunoinformatic approach that can be applied to the currently available coronavirus protein data in the online server for vaccine candidate development. We have identified the receptor binding domain (RBD) of structural spike protein (S1) as a potential target for immunity against COVID- 19 infection. Epitope prediction illustrated cytotoxic T-cell epitopes, helper T-cell epitopes, and B-cell epitopes associated with the target protein. These were joined through specific linkers along with adjuvant beta-defensin located at the N-terminal to create a multi epitope subunit vaccine (MESV). The specificity in the binding of the devised vaccine candidate to the TLR-3 immune cell receptor was evaluated via molecular docking interaction studies. Good docking score combined with robust interactions in the binding cavity certified the stringency of the engineered vaccine. Molecular dynamics simulation data showed minimal variation of the root-mean square deviations (RMSDs) and root-mean-square fluctuations (RMSFs) which confirmed the interaction stability. These results obtained from various in-silico experiments indicate the potency of this vaccine candidate as a probable therapeutic agent against COVID-19. Vaccination strategies targeting conserved epitope-based immune response would be beneficial in providing cross protection across beta-coronaviruses, and such vaccines would be resistant to the ever-evolving viruses. Communicated by Ramaswamy H. Sarma

Design of a multi-epitope peptide vaccine candidate against chandipura virus: an immuno-informatics study

Chandipura virus (CHPV) is an emerging pathogen responsible for acute encephalitic syndrome (AES) in pediatric population in India. Several outbreaks of CHPV have been reported from different states of India since the year 2003. At present there is no vaccine or therapeutic measures available to curtail the disease. In this study, we have identified both T-cell and B-cell epitopes of different antigenic proteins of CHPV like Nucleoprotein (N), Phosphoprotein (P) and Matrix protein (M) along with the immuno-dominant glycoprotein (G) and conducted in silico characterization for the same. The idea is to design a multi-epitope peptide construct using the epitopes, which were found to be non-toxic, non-allergenic and possessing high immunogenicity. The final multi-epitope construct named as: MEC-CHPV, comprised of β-defensin adjuvant at N-terminal for enhancement of immunogenicity followed by fourteen B-cell epitopes, four Helper T-cell epitopes and six Cytotoxic T-cell epitopes. The characterization of designed construct was carried out in terms of physicochemical parameters, antigenicity and allergenicity. The 3D structure prediction was performed. Molecular docking and molecular-dynamics simulation of MEC-CHPV with Toll like receptors (TLR-3 and TLR-8) showed stable interactions. In silico cloning of MEC-CHPV in pET30a(+) expression vector was also conducted using codon optimization. The in silico immune-simulation indicated a typical immune response against MEC-CHPV when used as a potential vaccine. This study provides a cost-effective and time-saving way to design a peptide vaccine candidate against CHPV using immuno-informatics approach. Development of the MEC-CHPV construct may pave the way for future laboratory experiments. Communicated by Ramaswamy H. Sarma

In Silico Approach in Designing a Novel Multi-Epitope Vaccine Candidate against Non-Small Cell Lung Cancer with Overexpressed G Protein-Coupled Receptor 56.

Background:Majority of cancer-related deaths worldwide is attributed to non-small cell lung cancer (NSCLC). G protein-coupled receptor 56 (GPR56) is overexpressed and associated in the progression of NSCLC. The aim of this study is to use immunoinformatics approach in designing a multi-epitope vaccine to target overexpressed GPR56 which can potentially activate antibody-mediated cell death mechanisms and inhibit pathways involved in the proliferation, migration and survival of NSCLC. Methods:Herein, the reported overexpression of GPR56 was further investigated by conducting a differential gene expression analysis of NSCLC samples from GEO DataSets (GSE29249). Results confirmed significant overexpression of GPR56 in NSCLC compared to adjacent normal samples. A multi-epitope vaccine (Fvax) was constructed in silico by adjoining B lymphocytes (BL) and helper T lymphocytes (HTL) epitopes from the extracellular sequence of GPR56. Population coverage (PC) of HTL epitopes was also estimated. To enhance its immunogenicity, sequences of flagellin domains were fused as adjuvant. Fvax was evaluated in silico for antigenicity, allergenicity, peptide toxicity, physicochemical properties and cross-reactivity. Its tertiary structure was predicted, refined, and validated followed by structural epitope prediction. Lastly, Fvax DNA was optimized and cloned in silico. Results:This is the first work to design a potential vaccine against GPR56-overexpressing NSCLC. Fvax has 3 BL and 7 HTL immunogenic epitopes on GPR56. In silico evaluations suggest that Fvax is antigenic, non-toxic, non-allergenic, stable, and has accessible BL epitopes with high PC worldwide for HTL epitopes. Conclusion:Overall, results showed that Fvax is a potential vaccine against NSCLC. The approach of this study efficiently minimized the number of tests, cost and time required to select the best epitopes and to design a vaccine for the treatment of NSCLC.

Contriving Multi-Epitope Subunit of Vaccine for COVID-19: Immunoinformatics Approaches.

COVID-19 has recently become the most serious threat to public health, and its prevalence has been increasing at an alarming rate. The incubation period for the virus is ~1–14 days and all age groups may be susceptible to a fatality rate of about 5.9%. COVID-19 is caused by a novel single-stranded, positive (+) sense RNA beta coronavirus. The development of a vaccine for SARS-CoV-2 is an urgent need worldwide. Immunoinformatics approaches are both cost-effective and convenient, as in silico predictions can reduce the number of experiments needed. In this study, with the aid of immunoinformatics tools, we tried to design a multi-epitope vaccine that can be used for the prevention and treatment of COVID-19. The epitopes were computed by using B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL) base on the proteins of SARS-CoV-2. A vaccine was devised by fusing together the B cell, HTL, and CTL epitopes with linkers. To enhance the immunogenicity, the β-defensin (45 mer) amino acid sequence, and pan-HLA DR binding epitopes (13aa) were adjoined to the N-terminal of the vaccine with the help of the EAAAK linker. To enable the intracellular delivery of the modeled vaccine, a TAT sequence (11aa) was appended to C-terminal. Linkers play vital roles in producing an extended conformation (flexibility), protein folding, and separation of functional domains, and therefore, make the protein structure more stable. The secondary and three-dimensional (3D) structure of the final vaccine was then predicted. Furthermore, the complex between the final vaccine and immune receptors (toll-like receptor-3 (TLR-3), major histocompatibility complex (MHC-I), and MHC-II) were evaluated by molecular docking. Lastly, to confirm the expression of the designed vaccine, the mRNA of the vaccine was enhanced with the aid of the Java Codon Adaptation Tool, and the secondary structure was generated from Mfold. Then we performed in silico cloning. The final vaccine requires experimental validation to determine its safety and efficacy in controlling SARS-CoV-2 infections.