Design of a Novel Multi Epitope-Based Vaccine for Pandemic Coronavirus Disease (COVID-19) by Vaccinomics and Probable Prevention Strategy against Avenging Zoonotics

Development of a novel multi-epitope vaccine for brucellosis prevention

Brucellosis, a zoonotic disease caused by Brucella, presents a significant threat to both animal and human health. In animals, the disease can lead to infertility, miscarriage, and high fever, while in humans, symptoms may include recurrent fever, fatigue, sweating, hepatosplenomegaly, and joint and muscle pain following infection. Treatment often involves long-term antibiotic therapy, placing a substantial psychological and financial burden on patients. While vaccination is crucial for prevention, current animal vaccines have drawbacks such as residual virulence, and a safe and effective human vaccine is lacking. Hence, the development of a vaccine for brucellosis is imperative. In this study, we utilized bioinformatics methods to design a multi-epitope vaccine targeting Brucella. Targeting Heme transporter BhuA and polysaccharide export protein, we identified antigenic epitopes, including six cytotoxic T lymphocyte (CTL) dominant epitopes, six helper T lymphocyte (HTL) dominant epitopes, one conformation B cell dominant epitope, and three linear B cell dominant epitopes. By linking these epitopes with appropriate linkers and incorporating a Toll-like receptor (TLR) agonist (human beta-defensin-2) and an auxiliary peptide (Pan HLA-DR epitopes), we constructed the multi-epitope vaccine (MEV). The MEV demonstrated high antigenicity, non-toxicity, non-allergenicity, non-human homology, stability, and solubility. Molecular docking analysis and molecular dynamics simulations confirmed the interaction and stability of the MEV with receptors (MHCI, MHCII, TLR4). Codon optimization and in silico cloning validated the translation efficiency and successful expression of MEV in Escherichia coli. Immunological simulations further demonstrated the efficacy of MEV in inducing robust immune responses. In conclusion, our findings suggest that the engineered MEVs have the potential to stimulate both humoral and cellular immune responses, offering valuable insights for the future development of safe and efficient Brucella vaccines.

In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches

Infection with the hepatitis C virus (HCV) is one of the causes of liver cancer, which is the world's sixth most prevalent and third most lethal cancer. The current treatments do not prevent reinfection; because they are expensive, their usage is limited to developed nations. Therefore, a prophylactic vaccine is essential to control this virus. Hence, in this study, an immunoinformatics method was applied to design a multi-epitope vaccine against HCV. The best B- and T-cell epitopes from conserved regions of the E2 protein of seven HCV genotypes were joined with the appropriate linkers to design a multi-epitope vaccine. In addition, cholera enterotoxin subunit B (CtxB) was included as an adjuvant in the vaccine construct. This study is the first to present this epitopes-adjuvant combination. The vaccine had acceptable physicochemical characteristics. The vaccine's 3D structure was predicted and validated. The vaccine's binding stability with Toll-like receptor 2 (TLR2) and TLR4 was confirmed using molecular docking and molecular dynamics (MD) simulation. The immune simulation revealed the vaccine's efficacy by increasing the population of B and T cells in response to vaccination. In silico expression in Escherichia coli (E. coli) was also successful.

Computational approach for identifying immunogenic epitopes and optimizing peptide vaccine through in-silico cloning against Mycoplasma genitalium

Mycoplasma genitalium is a pathogenic microorganism linked to a variety of severe health conditions including ovarian cancer, prostate cancer, HIV transmission, and sexually transmitted diseases. A more effective approach to address the challenges posed by this pathogen, given its high antibiotic resistance rates, could be the development of a peptide vaccine. In this study, we used experimentally validated 13 membrane proteins and their immunogenicity to identify suitable vaccine candidates. Thus, based on immunogenic properties and high conservation among other Mycoplasma genitalium sub-strains, the P110 surface protein is considered for further investigation. Later on, we identified T-cell epitopes and B-cell epitopes from the P110 protein to construct a multiepitope-based vaccine. As a result, the 'NIAPISFSFTPFTAA' T-cell epitope and 'KVKYESSGSNNISFDS' B-cell epitope have shown 99.53% and 87.50% population coverage along with 100% conservancy among the subspecies, and both epitopes were found to be non-allergenic. Furthermore, focusing on molecular docking analysis showed the lowest binding energy for MHC-I (-137.5 kcal/mol) and MHC-II (-183.3 kcal/mol), leading to a satisfactory binding strength between the T-cell epitopes and the MHC molecules. However, the constructed multiepitope vaccine (MEV) consisting of 54 amino acids demonstrates favorable characteristics for a vaccine candidate, including a theoretical pI of 4.25 with a scaled solubility of 0.812 and high antigenicity probabilities. Additionally, structural analyses reveal that the MEV displays substantial alpha helices and extended strands, vital for its immunogenicity. Molecular docking with the human Toll-like receptors TLR1/2 heterodimer shows strong binding affinity, reinforcing its potential to elicit an immune response. Our immune simulation analysis demonstrates immune memory development and robust immunity, while codon adaptation suggests optimal expression in E. coli using the pET-28a(+) vector. These findings collectively highlight the MEV's potential as a valuable vaccine candidate against M. genitalium.

Development of a potent recombinant scFv antibody against the SARS-CoV-2 by in-depth bioinformatics study: Paving the way for vaccine/diagnostics development

BACKGROUND

The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches?

METHOD

Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (VH/VL). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the VH-VL. Finally, this scFv was analyzed/optimized using various web servers.

RESULTS

Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs).

CONCLUSIONS

Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen.

Design of a multi-epitope vaccine against goatpox virus using an immunoinformatics approach

Introduction

Goatpox, a severe infectious disease caused by goatpox virus (GTPV), leads to enormous economic losses in the livestock industry. Traditional live attenuated vaccines cause serious side effects and exist a risk of dispersal. Therefore, it is urgent to develop efficient and safer vaccines to prevent and control of GTPV.

Methods

In the present study, we are aimed to design a multi-epitope subunit vaccine against GTPV using an immunoinformatics approach. Various immunodominant cytotoxic T lymphocytes (CTL) epitopes, helper T lymphocytes (HTL) epitopes, and B-cell epitopes from P32, L1R, and 095 proteins of GTPV were screened and liked by the AAY, GPGPG, and KK connectors, respectively. Furthermore, an adjuvant β-defensin was attached to the vaccine's N-terminal using the EAAAK linker to enhance immunogenicity.

Results

The constructed vaccine was soluble, non-allergenic and non-toxic and exhibited high levels of antigenicity and immunogenicity. The vaccine's 3D structure was subsequently predicted, refined and validated, resulting in an optimized model with a Z-value of -3.4. Molecular docking results demonstrated that the vaccine had strong binding affinity with TLR2(-27.25 kcal/mol), TLR3(-39.84 kcal/mol), and TLR4(-59.42 kcal/mol). Molecular dynamics simulation results indicated that docked vaccine-TLR complexes were stable. Immune simulation analysis suggested that the vaccine can induce remarkable increase in antibody titers of IgG and IgM, higher levels of IFN-γ and IL-2.

Conclusion

The designed GTPV multi-epitope vaccine is structurally stable and can induce robust humoral and cellular immune responses, which may be a promising vaccine candidate against GTPV.

An In Silico Multi-epitopes Vaccine Ensemble and Characterization Against Nosocomial Proteus penneri

Proteus penneri (P. penneri) is a bacillus-shaped, gram-negative, facultative anaerobe bacterium that is primarily an invasive pathogen and the etiological agent of several hospital-associated infections. P. penneri strains are naturally resistant to macrolides, amoxicillin, oxacillin, penicillin G, and cephalosporins; in addition, no vaccines are available against these strains. This warrants efforts to propose a theoretical based multi-epitope vaccine construct to prevent pathogen infections. In this research, reverse vaccinology bioinformatics and immunoinformatics approaches were adopted for vaccine target identification and construction of a multi-epitope vaccine. In the first phase, a core proteome dataset of the targeted pathogen was obtained using the NCBI database and subjected to bacterial pan-genome analysis using bacterial pan-genome analysis (BPGA) to predict core protein sequences which were then used to find good vaccine target candidates. This identified two proteins, Hcp family type VI secretion system effector and superoxide dismutase family protein, as promising vaccine targets. Afterward using the IEDB database, different B-cell and T-cell epitopes were predicted. A set of four epitopes "KGSVNVQDRE, NTGKLTGTR, IIHSDSWNER, and KDGKPVPALK" were chosen for the development of a multi-epitope vaccine construct. A 183 amino acid long vaccine design was built along with "EAAAK" and "GPGPG" linkers and a cholera toxin B-subunit adjuvant. The designed vaccine model comprised immunodominant, non-toxic, non-allergenic, and physicochemical stable epitopes. The model vaccine was docked with MHC-I, MHC-II, and TLR-4 immune cell receptors using the Cluspro2.0 web server. The binding energy score of the vaccine was - 654.7 kcal/mol for MHC-I, - 738.4 kcal/mol for MHC-II, and - 695.0 kcal/mol for TLR-4. A molecular dynamic simulation was done using AMBER v20 package for dynamic behavior in nanoseconds. Additionally, MM-PBSA binding free energy analysis was done to test intermolecular binding interactions between docked molecules. The MM-GBSA net binding energy score was - 148.00 kcal/mol, - 118.00 kcal/mol, and - 127.00 kcal/mol for vaccine with TLR-4, MHC-I, and MHC-II, respectively. Overall, these in silico-based predictions indicated that the vaccine is highly promising in terms of developing protective immunity against P. penneri. However, additional experimental validation is required to unveil the real immune response to the designed vaccine.

Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2

In the recent past several vaccines were developed to combat the COVID-19 disease. Unfortunately, the protective efficacy of the current vaccines has been reduced due to the high mutation rate in SARS-CoV-2. Here, we successfully implemented a coevolution based immunoinformatics approach to design an epitope-based peptide vaccine considering variability in spike protein of SARS-CoV-2. The spike glycoprotein was investigated for B- and T-cell epitope prediction. Identified T-cell epitopes were mapped on previously reported coevolving amino acids in the spike protein to introduce mutation. The non-mutated and mutated vaccine components were constructed by selecting epitopes showing overlapping with the predicted B-cell epitopes and highest antigenicity. Selected epitopes were linked with the help of a linker to construct a single vaccine component. Non-mutated and mutated vaccine component sequences were modelled and validated. The in-silico expression level of the vaccine constructs (non-mutated and mutated) in E. coli K12 shows promising results. The molecular docking analysis of vaccine components with toll-like receptor 5 (TLR5) demonstrated strong binding affinity. The time series calculations including root mean square deviation (RMSD), radius of gyration (RGYR), and energy of the system over 100 ns trajectory obtained from all atom molecular dynamics simulation showed stability of the system. The combined coevolutionary and immunoinformatics approach used in this study will certainly help to design an effective peptide vaccine that may work against different strains of SARS-CoV-2. Moreover, the strategy used in this study can be implemented on other pathogens.

Identification of a linear B-cell epitope on the African swine fever virus CD2v protein

African swine fever virus (ASFV) poses a serious threat to domestic pigs and wild boars, which is responsible for substantial production and economic losses. A dominant ASFV specific linear B cell epitope that reacted with the convalescent serum was explored and identified with the help of immune informatics techniques. It is essential in understanding the host immunity and in developing diagnostic technical guidelines and vaccine design. The confirmation of dominant epitopes with a positive serological matrix is feasible. To improve the immunogenicity of the epitope, we designed the dominant epitope of CD2v in the form of 2 branch Multiple-Antigen peptide (MAPs-2), CD2v-MAPs-2. Notably, CD2v peptide can be taken up by dendritic cells (DCs) to activate T lymphocytes and induce highly effective valence antibodies in BALB/c mice. The specific CD8+ T cell response were observed. The dominant epitope peptide identified in this study was able to effectively activate humoral and cellular immunity in mice model.

Bioinformatics and immunoinformatics approach to develop potent multi-peptide vaccine for coxsackievirus B3 capable of eliciting cellular and humoral immune response

Coxsackievirus B3 (CVB3) is a viral pathogen of various human disorders with no effective preventative interventions. Herein, we aimed to design a chimeric vaccine construct for CVB3 using reverse vaccinology and immunoinformatics approaches by screening the whole viral polyprotein sequence. Firstly, screening and mapping of viral polyprotein to predict 21 immunodominant epitopes (B-cell, CD8+ and CD4+ T-cell epitopes), fused with an adjuvant (Resuscitation-promoting factor), appropriate linkers, HIV-TAT peptide, Pan DR epitope, and 6His-tag to assemble a multi-epitope vaccine construct. The chimeric construct is predicted as probable antigen, non-allergen, stable, possess encouraging physicochemical features, and indicates a broader population coverage (98 %). The tertiary structure of the constructed vaccine was predicted and refined, and its interaction with the Toll-like receptor 4 (TLR4) was investigated through molecular docking and dynamics simulation. Computational cloning of the construct was carried out in pET28a (+) plasmid to guarantee the higher expression of the vaccine protein. Lastly, in silico immune simulation foreseen that humoral and cellular immune responses would be elicited in response to the administration of such a potent chimeric construct. Thus, the design constructed could vaccinate against CVB3 infection and various CVB serotypes. However, further in vitro/in vivo research must assess its safety and effectiveness.

Immunoinformatics design of multivalent epitope vaccine against monkeypox virus and its variants using membrane-bound, enveloped, and extracellular proteins as targets

Introduction

The current monkeypox (MPX) outbreak, caused by the monkeypox virus (MPXV), has turned into a global concern, with over 59,000 infection cases and 23 deaths worldwide.

Objectives

Herein, we aimed to exploit robust immunoinformatics approach, targeting membrane-bound, enveloped, and extracellular proteins of MPXV to formulate a chimeric antigen. Such a strategy could similarly be applied for identifying immunodominant epitopes and designing multi-epitope vaccine ensembles in other pathogens responsible for chronic pathologies that are difficult to intervene against.

Methods

A reverse vaccinology pipeline was used to select 11 potential vaccine candidates, which were screened and mapped to predict immunodominant B-cell and T-cell epitopes. The finalized epitopes were merged with the aid of suitable linkers, an adjuvant (Resuscitation-promoting factor), a PADRE sequence (13 aa), and an HIV TAT sequence (11 aa) to formulate a multivalent epitope vaccine. Bioinformatics tools were employed to carry out codon adaptation and computational cloning. The tertiary structure of the chimeric vaccine construct was modeled via I-TASSER, and its interaction with Toll-like receptor 4 (TLR4) was evaluated using molecular docking and molecular dynamics simulation. C-ImmSim server was implemented to examine the immune response against the designed multi-epitope antigen.

Results and discussion

The designed chimeric vaccine construct included 21 immunodominant epitopes (six B-cell, eight cytotoxic T lymphocyte, and seven helper T-lymphocyte) and is predicted non-allergen, antigenic, soluble, with suitable physicochemical features, that can promote cross-protection among the MPXV strains. The selected epitopes indicated a wide global population coverage (93.62%). Most finalized epitopes have 70%-100% sequence similarity with the experimentally validated immune epitopes of the vaccinia virus, which can be helpful in the speedy progression of vaccine design. Lastly, molecular docking and molecular dynamics simulation computed stable and energetically favourable interaction between the putative antigen and TLR4.

Conclusion

Our results show that the multi-epitope vaccine might elicit cellular and humoral immune responses and could be a potential vaccine candidate against the MPXV infection. Further experimental testing of the proposed vaccine is warranted to validate its safety and efficacy profile.

Proteome-Wide Screening of Potential Vaccine Targets against Brucella melitensis

The ongoing antibiotic-resistance crisis is becoming a global problem affecting public health. Urgent efforts are required to design novel therapeutics against pathogenic bacterial species. Brucella melitensis is an etiological agent of brucellosis, which mostly affects sheep and goats but several cases have also been reported in cattle, water buffalo, yaks and dogs. Infected animals also represent the major source of infection for humans. Development of safer and effective vaccines for brucellosis remains a priority to support disease control and eradication in animals and to prevent infection to humans. In this research study, we designed an in-silico multi-epitopes vaccine for B. melitensis using computational approaches. The pathogen core proteome was screened for good vaccine candidates using subtractive proteomics, reverse vaccinology and immunoinformatic tools. In total, 10 proteins: catalase; siderophore ABC transporter substrate-binding protein; pyridoxamine 5'-phosphate oxidase; superoxide dismutase; peptidylprolyl isomerase; superoxide dismutase family protein; septation protein A; hypothetical protein; binding-protein-dependent transport systems inner membrane component; and 4-hydroxy-2-oxoheptanedioate aldolase were selected for epitopes prediction. To induce cellular and antibody base immune responses, the vaccine must comprise both B and T-cells epitopes. The epitopes were next screened for antigenicity, allergic nature and water solubility and the probable antigenic, non-allergic, water-soluble and non-toxic nine epitopes were shortlisted for multi-epitopes vaccine construction. The designed vaccine construct comprises 274 amino acid long sequences having a molecular weight of 28.14 kDa and instability index of 27.62. The vaccine construct was further assessed for binding efficacy with immune cell receptors. Docking results revealed that the designed vaccine had good binding potency with selected immune cell receptors. Furthermore, vaccine-MHC-I, vaccine-MHC-II and vaccine-TLR-4 complexes were opted based on a least-binding energy score of -5.48 kcal/mol, 0.64 kcal/mol and -2.69 kcal/mol. Those selected were then energy refined and subjected to simulation studies to understand dynamic movements of the docked complexes. The docking results were further validated through MMPBSA and MMGBSA analyses. The MMPBSA calculated -235.18 kcal/mol, -206.79 kcal/mol, and -215.73 kcal/mol net binding free energy, while MMGBSA estimated -259.48 kcal/mol, -206.79 kcal/mol and -215.73 kcal/mol for TLR-4, MHC-I and MHC-II complexes, respectively. These findings were validated by water-swap and entropy calculations. Overall, the designed vaccine construct can evoke proper immune responses and the construct could be helpful for experimental researchers in formulation of a protective vaccine against the targeted pathogen for both animal and human use.

Designing of cytotoxic T lymphocyte-based multi-epitope vaccine against SARS-CoV2: a reverse vaccinology approach

SARS-CoV2 is a single-stranded RNA virus, gaining much attention after it out broke in China in December 2019. The virus rapidly spread to several countries around the world and caused severe respiratory illness to humans. Since the outbreak, researchers around the world have devoted maximum resources and effort to develop a potent vaccine that would offer protection to uninfected individuals against SARS-CoV2. Reverse vaccinology is a relatively new approach that thrives faster in vaccine research. In this study, we constructed Cytotoxic T Lymphocytes (CTL)-based multi-epitope vaccine using hybrid epitope prediction methods. A total of 121 immunogenic CTL epitopes were screened by various sequence-based prediction methods and docked with their respective HLA alleles using the AutoDock Vina v1.1.2. In all, 17 epitopes were selected based on their binding affinity, followed by the construction of multi-epitope vaccine by placing the appropriate linkers between the epitopes and tuberculosis heparin-binding hemagglutinin (HBHA) adjuvant. The final vaccine construct was modeled by the I-TASSER server and the best model was further validated by ERRAT, ProSA, and PROCHECK servers. Furthermore, the molecular interaction of the constructed vaccine with TLR4 was assessed by ClusPro 2.0 and PROtein binDIng enerGY prediction (PRODIGY) server. The immune simulation analysis confirms that the constructed vaccine was capable of inducing long-lasting memory T helper (Th) and CTL responses. Finally, the nucleotide sequence was codon-optimized by the JCAT tool and cloned into the pET21a (+) vector. The current results reveal that the candidate vaccine is capable of provoking robust CTL response against the SARS-CoV2.Communicated by Ramaswamy H. Sarma.

Designing multi-epitope based peptide vaccine targeting spike protein SARS-CoV-2 B1.1.529 (Omicron) variant using computational approaches

Millions of lives have been infected since the SARS-CoV-2 outbreak in 2019. The high human-to-human transmission rate has warranted a need for a vaccine to protect people. Although some vaccines are in use, due to the high mutation rate in the SARS-CoV-2 multiple variants, the current vaccines may not be sufficient to immunize people against new variant threats. One of the emerging concern variants is B1.1.529 (Omicron), which carries ~ 30 mutations in the Spike protein (S) of SARS-CoV-2 and is predicted to evade antibody recognition even from vaccinated people. We used a structure-based approach and an epitope prediction server to develop a Multi-Epitope based Subunit Vaccine (MESV) involving SARS-CoV-2 B1.1.529 variant spike glycoprotein. The predicted epitope with better antigenicity and non-toxicity was used for designing and predicting vaccine construct features and structure models. In addition, the MESV construct In silico cloning in the pET28a expression vector predicted the construct to be highly translational. The proposed MESV vaccine construct was also subjected to immune simulation prediction and was found to be highly antigenic and elicit a cell-mediated immune response. Therefore, the proposed MESV in the present study has the potential to be evaluated further for vaccine production against the newly identified B1.1.529 (Omicron) variant of concern.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02027-6.

Immunology to Immunotherapeutics of SARS-CoV-2: Identification of Immunogenic Epitopes for Vaccine Development

The emergence of COVID19 pandemic caused by SARS-CoV-2 virus has created a global public health and socio-economic crisis. Immunoinformatics-based approaches to investigate the potential antigens is the fastest way to move towards a multiepitope-based vaccine development. This review encompasses the underlying mechanisms of pathogenesis, innate and adaptive immune signaling along with evasion pathways of SARS-CoV-2. Furthermore, it compiles the promiscuous peptides from in silico studies which are subjected to prediction of cytokine milieu using web-based servers. Out of the 434 peptides retrieved from all studies, we have identified 33 most promising T cell vaccine candidates. This review presents a list of the most potential epitopes from several proteins of the virus based on their immunogenicity, homology, conservancy and population coverage studies. These epitopes can form a basis of second generation of vaccine development as the first generation vaccines in various stages of trials mostly focus only on Spike protein. We therefore, propose them as most potential candidates which can be taken up immediately for confirmation by experimental studies.

mRNA vaccines in the prevention and treatment of diseases

Messenger ribonucleic acid (mRNA) vaccines made their successful public debut in the effort against the COVID-19 outbreak starting in late 2019, although the history of mRNA vaccines can be traced back decades. This review provides an overview to discuss the historical course and present situation of mRNA vaccine development in addition to some basic concepts that underly mRNA vaccines. We discuss the general preparation and manufacturing of mRNA vaccines and also discuss the scientific advances in the in vivo delivery system and evaluate popular approaches (i.e., lipid nanoparticle and protamine) in detail. Next, we highlight the clinical value of mRNA vaccines as potent candidates for therapeutic treatment and discuss clinical progress in the treatment of cancer and coronavirus disease 2019. Data suggest that mRNA vaccines, with several prominent advantages, have achieved encouraging results and increasing attention due to tremendous potential in disease management. Finally, we suggest some potential directions worthy of further investigation and optimization. In addition to basic research, studies that help to facilitate storage and transportation will be indispensable for practical applications.

Non-structure protein ORF1ab (NSP8) in SARS-CoV-2 contains potential γδT cell epitopes

Upon activation by the pathogen through T-cell receptors (TCRs), γδT cells suppress the pathogenic replication and thus play important roles against viral infections. Targeting SARS-CoV-2 via γδT cells provides alternative therapeutic strategies. However, little is known about the recognition of SARS-CoV-2 antigens by γδT cells. We discovered a specific Vγ9/δ2 CDR3 by analyzing γδT cells derived from the patients infected by SARS-CoV-2. Using a cell model exogenously expressing γδ-TCR established, we further screened the structural motifs within the CDR3 responsible for binding to γδ-TCR. Importantly, these sequences were mapped to NSP8, a non-structural protein in SARS-CoV-2. Our results suggest that NSP8 mediates the recognition by γδT cells and thus could serve as a potential target for vaccines.

Senna makki and other active phytochemicals: Myths and realities behind covid19 therapeutic interventions

This study aims to investigate the binding potential of chemical compounds of Senna in comparison with the experimentally tested active phytochemicals against SARS-CoV-2 protein targets to assist in prevention of infection by exploring multiple treatment options. The entire set of phytochemicals from both the groups were subjected to advanced computational analysis that explored functional molecular descriptors from a set of known medicinal-based active therapeutics followed by MD simulations on multiple SARS-CoV-2 target proteins. Our findings manifest the importance of hydrophobic substituents in chemical structures of potential inhibitors through cross-validation with the FDA-approved anti-3CL^pro drugs. Noteworthy improvement in end-point binding free energies and pharmacokinetic profiles of the proposed compounds was perceived in comparison to the control drug, vizimpro. Moreover, the identification of common drug targets namely; AKT1, PTGS1, TNF, and DPP4 between proposed active phytochemicals and Covid19 using network pharmacological analysis further substantiate the importance of medicinal scaffolds. The structural dynamics and binding affinities of phytochemical compounds xanthoangelol_E, hesperetin, and beta-sitosterol reported as highly potential against 3CL^pro in cell-based and cell-free assays are consistent with the computational analysis. Whereas, the secondary metabolites such as sennosides A, B, C, D present in higher amount in Senna exhibited weak binding affinity and instability against the spike protein, helicase nsp13, RdRp nsp12, and 3CL^pro. In conclusion, the results contravene fallacious efficacy claims of Senna tea interventions circulating on electronic/social media as Covid19 cure; thus emphasizing the importance of well-examined standardized data of the natural products in hand; thereby preventing unnecessary deaths under pandemic hit situations worldwide.

Designing of a Recombinant Multi-Epitopes Based Vaccine against Enterococcus mundtii Using Bioinformatics and Immunoinformatics Approaches

Enterococcus species are an emerging group of bacterial pathogens that have a significant role in hospital-associated infections and are associated with higher mortality and morbidity rates. Among these pathogens, Enterococcus mundtii is one of the causative agents of multiple hospital associated infections. Currently, no commercially available licensed vaccine is present, and multi-drug resistant strains of the pathogen are prominent. Due to several limitations of experimental vaccinology, computational vaccine designing proved to be helpful in vaccine designing against several bacterial pathogens. Herein, we designed a multi-epitope-based vaccine against E. mundtii using in silico approaches. After an in-depth analysis of the core genome, three probable antigenic proteins (lytic polysaccharide monooxygenase, siderophore ABC transporter substrate-binding protein, and lytic polysaccharide monooxygenase) were shortlisted for epitope prediction. Among predicted epitopes, ten epitopes-GPADGRIAS, TTINHGGAQA, SERTALSVTT, GDGGNGGGEV, GIKEPDLEK, KQADDRIEA, QAIGGDTSN, EPLDEQTASR, AQWEPQSIEA, QPLKFSDFEL-were selected for multi-epitope vaccine construct designing. The screened B- and T-cell epitopes were joined with each other via specific linkers and linked to the cholera toxin B subunit as an adjuvant to enhance vaccine immune protection efficacy. The designed vaccine construct induced cellular and humoral immune responses. Blind docking with immune cell receptors, followed by molecular dynamic simulation results confirms the good binding potency and stability of the vaccine in providing protection against the pathogen.

Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach

Antibiotic resistance (AR) is the resistance mechanism pattern in bacteria that evolves over some time, thus protecting the bacteria against antibiotics. AR is due to bacterial evolution to make itself fit to changing environmental conditions in a quest for survival of the fittest. AR has emerged due to the misuse and overuse of antimicrobial drugs, and few antibiotics are now left to deal with these superbug infections. To combat AR, vaccination is an effective method, used either therapeutically or prophylactically. In the current study, an in silico approach was applied for the design of multi-epitope-based vaccines against Providencia rettgeri, a major cause of traveler’s diarrhea. A total of six proteins: fimbrial protein, flagellar hook protein (FlgE), flagellar basal body L-ring protein (FlgH), flagellar hook-basal body complex protein (FliE), flagellar basal body P-ring formation protein (FlgA), and Gram-negative pili assembly chaperone domain proteins, were considered as vaccine targets and were utilized for B- and T-cell epitope prediction. The predicted epitopes were assessed for allergenicity, antigenicity, virulence, toxicity, and solubility. Moreover, filtered epitopes were utilized in multi-epitope vaccine construction. The predicted epitopes were joined with each other through specific GPGPG linkers and were joined with cholera toxin B subunit adjuvant via another EAAAK linker in order to enhance the efficacy of the designed vaccine. Docking studies of the designed vaccine construct were performed with MHC-I (PDB ID: 1I1Y), MHC-II (1KG0), and TLR-4 (4G8A). Findings of the docking study were validated through molecular dynamic simulations, which confirmed that the designed vaccine showed strong interactions with the immune receptors, and that the epitopes were exposed to the host immune system for proper recognition and processing. Additionally, binding free energies were estimated, which highlighted both electrostatic energy and van der Waals forces to make the complexes stable. Briefly, findings of the current study are promising and may help experimental vaccinologists to formulate a novel multi-epitope vaccine against P. rettgeri.

An integrated computational framework to design a multi-epitopes vaccine against Mycobacterium tuberculosis

Tuberculosis (TB) is a highly contagious disease that mostly affects the lungs and is caused by a bacterial pathogen, Mycobacterium tuberculosis. The associated mortality rate of TB is much higher compared to any other disease and the situation is more worrisome by the rapid emergence of drug resistant strains. Bacillus Calmette-Guerin (BCG) is the only licensed attenuated vaccine available for use in humans however, many countries have stopped its use as it fails to confer protective immunity. Therefore, urgent efforts are required to identify new and safe vaccine candidates that are not only provide high immune protection but also have broad spectrum applicability. Considering this, herein, I performed an extensive computational vaccine analysis to investigate 200 complete sequenced genomes of M. tuberculosis to identify core vaccine candidates that harbor safe, antigenic, non-toxic, and non-allergic epitopes. To overcome literature reported limitations of epitope-based vaccines, I carried out additional analysis by designing a multi-epitopes vaccine to achieve maximum protective immunity as well as to make experimental follow up studies easy by selecting a vaccine that can be easily analyzed because of its favorable physiochemical profile. Based on these analyses, I identified two potential vaccine proteins that fulfill all required vaccine properties. These two vaccine proteins are diacylglycerol acyltransferase and ESAT-6-like protein. Epitopes: DSGGYNANS from diacylglycerol acyltransferase and AGVQYSRAD, ADEEQQQAL, and VSRADEEQQ from ESAT-6-like protein were found to cover all necessary parameters and thus used in a multi-epitope vaccine construct. The designed vaccine is depicting a high binding affinity for different immune receptors and shows stable dynamics and rigorous van der Waals and electrostatic binding energies. The vaccine also simulates profound primary, secondary, tertiary immunoglobulin production as well as high interleukins and interferons count. In summary, the designed vaccine is ideal to be evaluated experimentally to decipher its real biological efficacy in controlling drug resistant infections of M. tuberculosis.

Vaccinomics: a future avenue for vaccine development against emerging pathogens

INTRODUCTION

Vaccines are a major achievement in medical sciences, but the development of more effective vaccines against infectious diseases is essential for prevention and control of emerging pathogens worldwide. The application of omics technologies has advanced vaccinology through the characterization of host-vector-pathogen molecular interactions and the identification of candidate protective antigens. However, major challenges such as host immunity, pathogen and environmental factors, vaccine efficacy and safety need to be addressed. Vaccinomics provides a platform to address these challenges and improve vaccine efficacy and safety.

AREAS COVERED

In this review, we summarize current information on vaccinomics and propose quantum vaccinomics approaches to further advance vaccine development through the identification and combination of antigen protective epitopes, the immunological quantum. The COVID-19 pandemic caused by SARS-CoV-2 is an example of emerging infectious diseases with global impact on human health.

EXPERT OPINION

Vaccines are required for the effective and environmentally sustainable intervention for the control of emerging infectious diseases worldwide. Recent advances in vaccinomics provide a platform to address challenges in improving vaccine efficacy and implementation. As proposed here, quantum vaccinomics will contribute to vaccine development, efficacy, and safety by facilitating antigen combinations to target pathogen infection and transmission in emerging infectious diseases.

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

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

A Comprehensive Computer Aided Vaccine Design Approach to Propose a Multi-Epitopes Subunit Vaccine against Genus Klebsiella Using Pan-Genomics, Reverse Vaccinology, and Biophysical Techniques

Klebsiella is a genus of nosocomial bacterial pathogens and is placed in the most critical list of World Health Organization (WHO) for development of novel therapeutics. The pathogens of the genus are associated with high mortality and morbidity. Owing to their strong resistance profile against different classes of antibiotics and nonavailability of a licensed vaccine, urgent efforts are required to develop a novel vaccine candidate that can tackle all pathogenic species of the Klebsiella genus. The present study aims to design a broad-spectrum vaccine against all species of the Klebsiella genus with objectives to identify the core proteome of pathogen species, prioritize potential core vaccine proteins, analyze immunoinformatics of the vaccine proteins, construct a multi-epitopes vaccine, and provide its biophysical analysis. Herein, we investigated all reference species of the genus to reveal their core proteome. The core proteins were then subjected to multiple reverse vaccinology checks that are mandatory for the prioritization of potential vaccine candidates. Two proteins (TonB-dependent siderophore receptor and siderophore enterobactin receptor FepA) were found to fulfill all vaccine parameters. Both these proteins harbor several potent B-cell-derived T-cell epitopes that are antigenic, nonallergic, nontoxic, virulent, water soluble, IFN-γ producer, and efficient binder of DRB*0101 allele. The selected epitopes were modeled into a multi-epitope peptide comprising linkers and Cholera Toxin B adjuvant. For docking with innate immune and MHC receptors and afterward molecular dynamics simulations and binding free energy analysis, the vaccine structure was modeled for tertiary structure and refined for structural errors. To assess the binding affinity and presentation of the designed vaccine construct, binding mode and interactions analysis were performed using molecular docking and molecular dynamics simulation techniques. These biophysical approaches illustrated the vaccine as a good binder to the immune receptors and revealed robust interactions energies. The vaccine sequence was further translated to nucleotide sequence and cloned into an appropriate vector for expressing it at high rate in Escherichia coli K12 strain. In addition, the vaccine was illustrated to generate a good level of primary, secondary, and tertiary immune responses, proving good immunogenicity of the vaccine. Based on the reported results, the vaccine can be a good candidate to be evaluated for effectiveness in wet laboratory validation studies.

A reverse vaccinology and immunoinformatics approach for designing a multiepitope vaccine against SARS-CoV-2

Since 2019, the world was involved with SARS-CoV-2 and consequently, with the announcement by the World Health Organization that COVID-19 was a pandemic, scientific were an effort to obtain the best approach to combat this global dilemma. The best way to prevent the pandemic from spreading further is to use a vaccine against COVID-19. Here, we report the design of a recombinant multi-epitope vaccine against the four proteins spike or crown (S), membrane (M), nucleocapsid (N), and envelope (E) of SARS-CoV-2 using immunoinformatics tools. We evaluated the most antigenic epitopes that bind to HLA class 1 subtypes, along with HLA class 2, as well as B cell epitopes. Beta-defensin 3 and PADRE sequence were used as adjuvants in the structure of the vaccine. KK, GPGPG, and AAY linkers were used to fuse the selected epitopes. The nucleotide sequence was cloned into pET26b(+) vector using restriction enzymes XhoI and NdeI, and HisTag sequence was considered in the C-terminal of the construct. The results showed that the proposed candidate vaccine is a 70.87 kDa protein with high antigenicity and immunogenicity as well as non-allergenic and non-toxic. A total of 95% of the selected epitopes have conservancy with similar sequences. Molecular docking showed a strong binding between the vaccine structure and tool-like receptor (TLR) 7/8. The docking, molecular dynamics, and MM/PBSA analysis showed that the vaccine established a stable interaction with both structures of TLR7 and TLR8. Simulation of immune stimulation by this vaccine showed that it evokes immune responses related to humoral and cellular immunity.

Genome-Wide Analysis of Codon Usage Patterns of SARS-CoV-2 Virus Reveals Global Heterogeneity of COVID-19

The ongoing outbreak of coronavirus disease COVID-19 is significantly implicated by global heterogeneity in the genome organization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The causative agents of global heterogeneity in the whole genome of SARS-CoV-2 are not well characterized due to the lack of comparative study of a large enough sample size from around the globe to reduce the standard deviation to the acceptable margin of error. To better understand the SARS-CoV-2 genome architecture, we have performed a comprehensive analysis of codon usage bias of sixty (60) strains to get a snapshot of its global heterogeneity. Our study shows a relatively low codon usage bias in the SARS-CoV-2 viral genome globally, with nearly all the over-preferred codons' A.U. ended. We concluded that the SARS-CoV-2 genome is primarily shaped by mutation pressure; however, marginal selection pressure cannot be overlooked. Within the A/U rich virus genomes of SARS-CoV-2, the standard deviation in G.C. (42.91% ± 5.84%) and the GC3 value (30.14% ± 6.93%) points towards global heterogeneity of the virus. Several SARS-CoV-2 viral strains were originated from different viral lineages at the exact geographic location also supports this fact. Taking all together, these findings suggest that the general root ancestry of the global genomes are different with different genome's level adaptation to host. This research may provide new insights into the codon patterns, host adaptation, and global heterogeneity of SARS-CoV-2.

Engineering a multi epitope vaccine against SARS-CoV-2 by exploiting its non structural and structural proteins

SARS-CoV-2, the causative agent behind the ongoing pandemic exhibits an enhanced potential for infection when compared to its related family members- the SARS-CoV and MERS-CoV; which have caused similar disease outbreaks in the past. The severity of the global health burden, increasing mortality rate and the emergent economic crisis urgently demands the development of next generation vaccines. Amongst such emergent next generation vaccines are the multi-epitope subunit vaccines, which hold promise in combating deadly pathogens. In this study we have exploited immunoinformatics applications to delineate a vaccine candidate possessing multiple B and T cells epitopes by utilizing the SARS-CoV-2 non structural and structural proteins. The antigenicity potential, safety, structural stability and the production feasibility of the designed construct was evaluated computationally. Furthermore, due to the known role of human TLR-3 immune receptor in viral sensing, which facilitates host cells activation for an immune response, the vaccine construct was examined for its binding efficiency using molecular docking and molecular dynamics simulation studies, which resulted in strong and stable interactions. Finally, the immune simulation studies suggested an effective immune response on vaccine administration. Overall, the immunoinformatics analysis advocates that the proposed vaccine candidate is safe and immunogenic and therefore can be pushed as a lead for in vitro and in vivo investigations.Communicated by Ramaswamy H. Sarma.

Immunoinformatics-guided design of a multi-epitope vaccine based on the structural proteins of severe acute respiratory syndrome coronavirus 2

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in a contagious respiratory tract infection that has become a global burden since the end of 2019. Notably, fewer patients infected with SARS-CoV-2 progress from acute disease onset to death compared with the progression rate associated with two other coronaviruses, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). Several research organizations and pharmaceutical industries have attempted to develop successful vaccine candidates for the prevention of COVID-19. However, increasing evidence indicates that the SARS-CoV-2 genome undergoes frequent mutation; thus, an adequate analysis of the viral strain remains necessary to construct effective vaccines. The current study attempted to design a multi-epitope vaccine by utilizing an approach based on the SARS-CoV-2 structural proteins. We predicted the antigenic T- and B-lymphocyte responses to four structural proteins after screening all structural proteins according to specific characteristics. The predicted epitopes were combined using suitable adjuvants and linkers, and a secondary structure profile indicated that the vaccine shared similar properties with the native protein. Importantly, the molecular docking analysis and molecular dynamics simulations revealed that the constructed vaccine possessed a high affinity for toll-like receptor 4 (TLR4). In addition, multiple descriptors were obtained from the simulation trajectories, including the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (R g), demonstrating the rigid nature and inflexibility of the vaccine and receptor molecules. In addition, codon optimization, based on Escherichia coli K12, was used to determine the GC content and the codon adaptation index (CAI) value, which further followed for the incorporation into the cloning vector pET28+(a). Collectively, these findings suggested that the constructed vaccine could be used to modulate the immune reaction against SARS-CoV-2.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

Designing a next generation multi-epitope based peptide vaccine candidate against SARS-CoV-2 using computational approaches

COVID-19 caused by SARS-CoV-2 was declared a global pandemic by WHO (World Health Organization) in March, 2020. Within 6 months, nearly 750,000 deaths are claimed by COVID-19 across the globe. This called for immediate social, scientific, technological, public and community interventions. Considering the severity of infection and the associated mortalities, global efforts are underway to develop preventive measures against SARS-CoV-2. Among the SARS-CoV-2 target proteins, Spike (S) glycoprotein (a.k.a S Protein) is the most studied target known to trigger strong host immune response. A detailed analysis of S protein-based epitopes enabled us to design a novel B-cell-derived T-cell Multi-epitope-based peptide (MEBP) vaccine candidate. This involved a systematic and comprehensive computational protocol consisting of prediction of dual-purpose epitopes and designing an MEBP vaccine construct. This was followed by 3D structure validation, MEBP complex interaction studies, in silico cloning and vaccine dose-based immune response simulation to evaluate the immunogenic potency of the vaccine construct. The dual-purpose epitope prediction protocol was designed such that the same epitope elicits both humoral and cellular immune response unlike the earlier designs. Further, the epitopes predicted were screened against stringent criteria to ensure selection of a potent candidate with maximum antigen coverage and best immune response. The vaccine dose-based immune response simulation studies revealed a rapid antigen clearance through antibody generation and elevated levels of cell-mediated immunity during repeated exposure of the vaccine. The favourable results of the analysis strongly indicate that the vaccine construct is indeed a potent vaccine candidate and ready to proceed to the next steps of experimental validation and efficacy studies.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-020-02574-x.

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

Designing of Nucleocapsid Protein Based Novel Multi-epitope Vaccine Against SARS-COV-2 Using Immunoinformatics Approach

The COVID-19 disease is caused by SARS-CoV-2 and spreading rapidly worldwide with extremely high infection rate. Since effective and specific vaccine is not available to combat the deadly COVID-19, the objective of our study was to design a multi-epitope vaccine using immunoinformatics approach with translational implications. Nucleocapsid (N) protein of SARS-CoV-2 is stable, conserved and highly immunogenic along with being less prone to mutations during infection, which makes it a suitable candidate for designing vaccine. In our study, B- and T-cells epitopes were identified from N protein and screened based on crucial parameters to design the multi-epitope vaccine construct. Additionally, human beta-defensin-2 was incorporated into the vaccine construct as an adjuvant along with suitable linkers followed by its further evaluation based on crucial parameters including allergenicity, antigenicity, stability etc. Combined major histocompatibility complexes (MHC-I and MHC-II) binding epitopes presented broader population coverage of the vaccine throughout the world. The three-dimensional structure of vaccine candidate implied strong interaction with toll-like receptor 3 (TLR3) using molecular docking. The vaccine-TLR3 complex was observed to be highly stable during simulation and electrostatic free energy was foremost contributor for stabilization of the structure. Subsequently, in silico cloning of vaccine candidate was carried out to generate the construct into pET-28a(+) expression vector succeeded by its virtual confirmation. Altogether, our results advocate that the designed vaccine candidate could be an effective and promising weapon to fight with COVID-19 infection worldwide.

Multi-epitope vaccine against SARS-CoV-2 applying immunoinformatics and molecular dynamics simulation approaches

COVID-19, caused by SARS-CoV-2, is severe respiratory illnesses leading to millions of deaths worldwide in very short span. The high case fatality rate and the lack of medical counter measures emphasize for an urgent quest to develop safe and effective vaccine. Receptor-binding domain (RBD) of spike protein of SARS-CoV-2 binds to the ACE2 receptor on human host cell for the viral attachment and entry, hence considered as a key target to develop vaccines, antibodies and therapeutics. In this study, immunoinformatics approach was employed to design a novel multi-epitope vaccine using RBD of SARS-CoV-2 spike protein. The potential B- and T-cell epitopes were selected from RBD sequence using various bioinformatics tools to design the vaccine construct. The in silico designed multi-epitope vaccine encompasses 146 amino acids with an adjuvant (human beta-defensin-2), which was further computationally evaluated for several parameters including antigenicity, allergenicity and stability. Subsequently, three-dimensional structure of vaccine construct was modelled and then docked with various toll-like receptors. Molecular dynamics (MD) study of docked TLR3-vaccine complex delineated it to be highly stable during simulation time and the stabilization of interaction was majorly contributed by electrostatic energy. The docked complex also showed low deformation and increased rigidity in motion of residues during dynamics. Furthermore, in silico cloning of the multi-epitope vaccine was carried out to generate the plasmid construct for expression in a bacterial system. Altogether, our study suggests that the designed vaccine candidate containing RBD region could provide the specific humoral and cell-mediated immune responses against SARS-CoV-2.

Communicated by Ramaswamy H. Sarma