Designing an efficient multi-epitope peptide vaccine against Vibrio cholerae via combined immunoinformatics and protein interaction based approaches

Proteome-Wide Mapping and Reverse Vaccinology Approaches to Design a Multi-Epitope Vaccine against Clostridium perfringens

C. perfringens is a highly versatile bacteria of livestock and humans, causing enteritis (a common food-borne illness in humans), enterotoxaemia (in which toxins are formed in the intestine which damage and destroy organs, i.e., the brain), and gangrene (wound infection). There is no particular cure for the toxins of C. perfringens. Supportive care (medical control of pain, intravenous fluids) is the standard treatment. Therefore, a multiple-epitope vaccine (MEV) should be designed to battle against C. perfringens infection. Furthermore, the main objective of this in silico investigation is to design an MEV that targets C. perfringens. For this purpose, we selected the top three proteins that were highly antigenic using immuno-informatics approaches, including molecular docking. B-cells, IFN-gamma, and T cells for target proteins were predicted and the most conserved epitopes were selected for further investigation. For the development of the final MEV, epitopes of LBL5, CTL17, and HTL13 were linked to GPGPG, AAY, and KK linkers. The vaccine N-end was joined to an adjuvant through an EAAK linker to improve immunogenicity. After the attachment of linkers and adjuvants, the final construct was 415 amino acids. B-cell and IFN-gamma epitopes demonstrate that the model structure is enhanced for humoral and cellular immune responses. To validate the immunogenicity and safety of the final construct, various physicochemical properties, and other properties such as antigenicity and non-allergens, were evaluated. Furthermore, molecular docking was carried out for verification of vaccine compatibility with the receptor, evaluated in silico. Also, in silico cloning was employed for the verification of the proper expression and credibility of the construct.

Finding Appropriate Signal Peptides for Secretory Production of Recombinant Glucarpidase: An In SilicoMethod

BACKGROUND

Methotrexate (MTX) is a general chemotherapeutic agent utilized to treat a variety of malignancies, woefully, its high doses can cause nephrotoxicity and subsequent defect in the process of MTX excretion. The recombinant form of glucarpidase is produced by engineered E. coli and is a confirmed choice to overcoming this problem.

OBJECTIVE

In the present study, in silico analyses were performed to select suitable SPs for the secretion of recombinant glucarpidase in E. coli.

METHODS

The signal peptide website and UniProt database were employed to collect the SPs and protein sequences. In the next step, SignalP-5.0 helped us to predict the SPs and the position of cleavage sites. Moreover, physicochemical properties and solubility were evaluated using Prot- Param and Protein-sol online software, and finally, ProtCompB was used to predict the final subcellular localization.

RESULTS

Luckily, all SPs could form soluble fusion proteins. At last, it was found that PPB and TIBA could translocate the glucarpidase into the extracellular compartment.

CONCLUSION

This study showed that there are only 2 applicable SPs for the extracellular translocation of glucarpidase. Although the findings were remarkable with high degrees of accuracy and precision based on the utilization of bioinformatics analyses, additional experimental assessments are required to confirm and validate it. Recent patents revealed several inventions related to the clinical aspects of vaccine peptides against human disorders.

EpitopeVec: Linear Epitope Prediction Using Deep Protein Sequence Embeddings

Motivation

B-cell epitopes (BCEs) play a pivotal role in the development of peptide vaccines, immuno-diagnostic reagents and antibody production, and thus in infectious disease prevention and diagnostics in general. Experimental methods used to determine BCEs are costly and time-consuming. Therefore, it is essential to develop computational methods for the rapid identification of BCEs. Although several computational methods have been developed for this task, generalizability is still a major concern, where cross-testing of the classifiers trained and tested on different datasets has revealed accuracies of 51–53%.

Results

We describe a new method called EpitopeVec, which uses a combination of residue properties, modified antigenicity scales, and protein language model-based representations (protein vectors) as features of peptides for linear BCE predictions. Extensive benchmarking of EpitopeVec and other state-of-the-art methods for linear BCE prediction on several large and small datasets, as well as cross-testing, demonstrated an improvement in the performance of EpitopeVec over other methods in terms of accuracy and area under the curve. As the predictive performance depended on the species origin of the respective antigens (viral, bacterial and eukaryotic), we also trained our method on a large viral dataset to create a dedicated linear viral BCE predictor with improved cross-testing performance.

Availability and implementation

The software is available at https://github.com/hzi-bifo/epitope-prediction.

Supplementary information

Supplementary data are available at Bioinformatics online.

Immunoinformatics and molecular dynamics approaches: Next generation vaccine design against West Nile virus

West Nile Virus (WNV) is a life threatening flavivirus that causes significant morbidity and mortality worldwide. No preventive therapeutics including vaccines against WNV are available for human use. In this study, immunoinformatics approach was performed to design a multi epitope-based subunit vaccine against this deadly pathogen. Human (HLA) and Mice (H-2) allele specific potential T-cell and B-cell epitopes were shortlisted through a stringent procedure. Molecular docking showed selected epitopes that have stronger binding affinity with human TLR-4. Molecular dynamics simulation confirmed the stable nature of the docked complex. Furthermore, in silico cloning analysis ensures efficient expression of desired gene in the microbial system. Interestingly, previous studies showed that two of our selected epitopes have strong immune response against WNV. Therefore, selected epitopes could be strong vaccine candidates to prevent WNV infections in human. However, further in vitro and in vivo investigations could be strengthening the validation of the vaccine candidate against WNV.

Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches

Cervical cancer, caused by human papillomavirus (HPV), is the fourth most common type of cancer among women worldwide. While HPV prophylactic vaccines are available, they have no therapeutic effects and do not clear up existing infections. This study aims to design a therapeutic vaccine against cervical cancer using reverse vaccinology. In this study, the E6 and E7 oncoproteins from HPV16 were chosen as the target antigens for epitope prediction. Cytotoxic T lymphocytes (CTL) and helper T lymphocytes (HTL) epitopes were predicted, and the best epitopes were selected based on antigenicity, allergenicity, and toxicity. The final vaccine construct was composed of the selected epitopes, along with the appropriate adjuvant and linkers. The multi-epitope vaccine was evaluated in terms of physicochemical properties, antigenicity, and allergenicity. The tertiary structure of the vaccine construct was predicted. Furthermore, several analyses were also carried out, including molecular docking, molecular dynamics (MD) simulation, and in silico cloning of the vaccine construct. The results showed that the final proposed vaccine could be considered an effective therapeutic vaccine for HPV; however, in vitro and in vivo experiments are required to validate the efficacy of this vaccine candidate.

Immunoinformatics based prediction of recombinant multi-epitope vaccine for the control and prevention of SARS-CoV-2

The emergence of SARS-CoV-2 has been reported during December 2019, in the city of Wuhan, China. The transmission of this virus via human to human interaction has already been described. The novel virus has become pandemic and declared as a comprehensive emergency worldwide by World Health Organization due to its exponential spread within and outside China. There is a need of time to create a therapeutic agent and a vaccine to cure and control this lethal SARS-CoV-2. Conventionally, the vaccine development process is time taking, tiresome and requires more economical inputs with manpower. However, bioinformatics offers a key solution to compute the possibilities. The present study focuses on the utilization of bioinformatics platforms to forecast B and T cell epitopes that belong to SARS-CoV-2 spike glycoprotein. The protein is thought to have an involvement in triggering of momentous immune response. NCBI database was explored to collect the surface glycoprotein sequence and was analyzed to determine the immunogenic epitopes. This prediction analysis was carried out using IEDB web based server and the prediction of protein structure was done by homology modeling approach. This study resulted in prediction of 5T cell and 13B cell epitopes. Moreover, GPGPG linker was used to make these predicted epitopes a single peptide prior to further analysis. Afterwards, a 3D model of the final vaccine peptide was constructed, and the structure quality of the final construct was checked by Ramachandran Plot analysis and ProSA-web. Moreover, docking analysis highlighted three interactions of epitope against HLA-B7 including Lys 178, Gol 303 and Thr 31 residues. In conclusion, the predicted multi epitope peptide can be suggested as therapeutic or prophylactic candidate vaccine against SARS-CoV-2 after further confirmation by immunological assays.

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.

Design of a new multi-epitope vaccine against Brucella based on T and B cell epitopes using bioinformatics methods

Brucellosis is one of the most serious and widespread zoonotic diseases, which seriously threatens human health and the national economy. This study was based on the T/B dominant epitopes of Brucella outer membrane protein 22 (Omp22), outer membrane protein 19 (Omp19) and outer membrane protein 28 (Omp28), with bioinformatics methods to design a safe and effective multi-epitope vaccine. The amino acid sequences of the proteins were found in the National Center for Biotechnology Information (NCBI) database, and the signal peptides were predicted by the SignaIP-5.0 server. The surface accessibility and hydrophilic regions of proteins were analysed with the ProtScale software and the tertiary structure model of the proteins predicted by I-TASSER software and labelled with the UCSF Chimera software. The software COBEpro, SVMTriP and BepiPred were used to predict B cell epitopes of the proteins. SYFPEITHI, RANKpep and IEDB were employed to predict T cell epitopes of the proteins. The T/B dominant epitopes of three proteins were combined with HEYGAALEREAG and GGGS linkers, and carriers sequences linked to the N- and C-terminus of the vaccine construct with the help of EAAAK linkers. Finally, the tertiary structure and physical and chemical properties of the multi-epitope vaccine construct were analysed. The allergenicity, antigenicity and solubility of the multi-epitope vaccine construct were 7.37–11.30, 0.788 and 0.866, respectively. The Ramachandran diagram of the mock vaccine construct showed 96.0% residues within the favoured and allowed range. Collectively, our results showed that this multi-epitope vaccine construct has a high-quality structure and suitable characteristics, which may provide a theoretical basis for future laboratory experiments.

Study of lipase producing gene in wheat - an in silico approach

BACKGROUND

Lipases (EC 3.1.1.3) catalyze the hydrolysis of oil into free fatty acids and glycerol forming the 3rd largest group of commercialized enzymes. Plant lipases grab attention recently because of their specificity, less production and purified cost, and easy availability. In silico approach is the first step to identify different genes coding for lipase in a most common indigenous plant, wheat, to explore the possibility of this plant as an alternative source for commercial lipase production. As the hierarchy organization of genes reflects an ancient process of gene duplication and divergence, many of the theoretical and analytical tools of the phylogenetic systematics can be utilized for comparative genomic studies. Also, in addition to experimental identification and characterization of genes, for computational genomic analysis, Arabidopsis has become a popular strategy to identify crop genes which are economically important, as Arabidopsis genes had been well identified and characterized for lipase. A number of articles had been reported in which genes of wheat have shown strong homology with Arabidopsis. The complete genome sequences of rice and Arabidopsis constitute a valuable resource for comparative genome analysis as they are representatives of the two major evolutionary lineages within the angiosperms. Here, in this in silico approach, Arabidopsis and Oryza sativa serve as models for dicotyledonous and monocotyledonous species, respectively, and the genomic sequence data available was used to identify the lipase genes in wheat.

RESULTS

In this present study, Ensembl Plants database was explored for lipase producing gene present in wheat genome and 21 genes were screened down as they contain specific domain and motif for lipase (GXSXG). According to the evolutionary analysis, it was found that the gene TraesCS5B02G157100, located in 5B chromosome, has 58.35% sequence similarity with the reported lipase gene of Arabidopsis thaliana and gene TraesCS3A02G463500 located in the 3A chromosome has 51.74% sequence similarity with the reported lipase gene of Oryza sativa. Homology modeling was performed using protein sequences coded by aforementioned genes and optimized by molecular dynamic simulations. Further with the help of molecular docking of modeled structures with tributyrin, binding efficiency was checked, and the difference in energies (DE) was -9.83 kcal/mol and -6.67 kcal/mol, respectively.

CONCLUSIONS

The present work provides a basic understanding of the gene-encoding lipase in wheat, which could be easily accessible and used as a potent industrial enzyme. The study enlightens another direction which can be used further to explore plant lipases.

An immunoinformatics approach for the design of a multi-epitope vaccine targeting super antigen TSST-1 of Staphylococcus aureus

Background

TSST-1 is a secretory and pyrogenic superantigen that is being responsible for staphylococcal mediated food poisoning and associated clinical manifestations. It is one of the main targets for the construction of vaccine candidates against Staphylococcus aureus. Most of the vaccines have met failure due to adverse reactions and toxicity reported during late clinical studies. To overcome this, an immunoinformatics approach is being used in the present study for the design of a multi-epitope vaccine to circumvent the problems related to toxicity and allergenicity.

Results

In this study, a multi-epitope vaccine against Staphylococcus aureus targeting TSST-1 was designed through an immunoinformatics approach. B cell and T cell epitopes were predicted in silico and mapped with linkers to avoid junctional immunogenicity and to ensure the efficient presentation of exposed epitopes through HLA. β-defensin and PADRE were adjusted at the N-terminal end of the final vaccine as adjuvants. Physiochemical parameters, antigenicity, and allergenicity of the vaccine construct were determined with the help of online servers. The three-dimensional structure of the vaccine protein was predicted and validated with various tools. The affinity of the vaccine with TLR-3 was studied through molecular docking studies and the interactions of two proteins were visualized using LigPlot⁺. The vaccine was successfully cloned in silico into pET-28a (+) for efficient expression in E. coli K12 system. Population coverage analysis had shown that the vaccine construct can cover 83.15% of the global population. Immune simulation studies showed an increase in the antibody levels, IL-2, IFN-γ, TGF-β, B cell, and T cell populations and induced primary, secondary, and tertiary immune responses.

Conclusion

Multi-epitope vaccine designed through a computational approach is a non-allergic and non-toxic antigen. Preliminary in silico reports have shown that this vaccine could elicit both B cell and T cell responses in the host as desired.

Anticipation of Antigenic Sites for the Goal of Vaccine Designing Against Nipah Virus: An Immunoinformatics Inquisitive Quest

With time, the Nipah virus has been proved as a fatal and dangerous pathogen for humanity. Nipah virus has its origin from bats and severely affects the respiratory as well as neurological organs. Regular outbreaks and unavailability of proper treatment for Nipah virus infection, demands the designing of vaccine for this disease. This prediction study was conducted to explore B cell epitopes from the Nipah virus’s proteome using the immunoinformatics approach. In this curious quest of anticipation of antigenic sites for the Insilico peptide vaccine for the Nipah virus, nine NV-B strain proteins were retrieved for further series of investigations. After sequential refining through immunoinformatics approaches, a total of 26 epitopes was selected to perform molecular modeling and docking. PEPstrMOD and Swiss model, respectively performed 3D modeling of epitopes with their respective alleles. Based on minimum binding energy, four epitopes viz. LHLGNFVRR, LNLSPLIQR, YHNMSPINR and FRRNNAIAF were predicted as promiscuous B cell epitopes. Based on low binding affinity and high population coverage worldwide, epitope LHLGNFVRR was finally selected. Increased Stability of the LHLGNFVRR- HLA DRB_1301 complex during simulation studies exhibit it as the most promising vaccine bidder. So complex of LHLGNFVRR- HLA DRB_1301 has shown most significance result for vaccine and for further validation and confirmation, wet lab and clinical trials can provide the potential of predicted peptides for the subunit vaccine.

Immunoinformatics and molecular modeling approach to design universal multi-epitope vaccine for SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmittable and pathogenic human coronavirus that caused a pandemic situation of acute respiratory syndrome, called COVID-19, which has posed a significant threat to global health security. The aim of the present study is to computationally design an effective peptide-based multi-epitope vaccine (MEV) against SARS-CoV-2. The overall model quality of the vaccine candidate, immunogenicity, allergenicity, and physiochemical analysis have been conducted and validated. Molecular dynamics studies confirmed the stability of the candidate vaccine. The docked complexes during the simulation revealed a strong and stable binding interactions of MEV with human and mice toll-like receptors (TLR), TLR3 and TLR4. Finally, candidate vaccine codons have been optimized for their in silico cloning in E. coli expression system, to confirm increased expression. The proposed MEV can be a potential candidate against SARS-CoV-2, but experimental validation is needed to ensure its safety and immunogenicity status.

Rational design of multimeric based subunit vaccine against Mycoplasma pneumonia: Subtractive proteomics with immunoinformatics framework

Mycoplasma pneumoniae is the prevalent cause of acquired respiratory infections around the globe. A multi-epitope vaccine (MEV) must be developed to combat infections of M. pneumoniae because there is no specific disease-modifying treatment or vaccination is present. The objective of this research is to design a vaccine that targets M. pneumoniae top five highly antigenic proteins using a combination of immunological techniques and molecular docking. T-cell (HTL & CTL), B-cell, and IFN-γ of target proteins were forecasted and highly conservative epitopes were chosen for further study. For designing of final vaccine, 4LBL, 7CTL, and 5HTL epitopes were joined by linkers of KK, AAY, and GPGPG. The N-end of the vaccine was linked to an adjuvant (Cholera enterotoxin subunit B) with a linker named EAAAK to enhance immunogenicity. After the addition of adjuvants and linkers, the size of the construct was 395 amino acids. The epitopes of IFN-γ and B-cells illustrate that the model construct is optimized for cell-mediated immune or humoral responses. To ensure that the final design is safer and immunogenic, properties like non-allergens, antigenicity, and various physicochemical properties were evaluated. Molecular docking of the vaccine with the toll-like receptor 4 (TLR4) was conducted to check the compatibility of the vaccine with the receptor. Besides, in-silico cloning was utilized for validation of the credibility and proper expression of the vaccine. Furthermore, to confirm that the multi-epitope vaccine created is protective and immunogenic, this research requires experimental validation.

A computational study to disclose potential drugs and vaccine ensemble for COVID-19 conundrum

The nucleocapsid (N) protein of SARS-COV-2, a virus responsible for the current COVID-19 pandemic, is considered a potential candidate for the design of new drugs and vaccines. The protein is central to several critical events in virus production, with its highly druggable nature and rich antigenic determinants making it an excellent anti-viral biomolecule. Docking-based virtual screening using the Asinex anti-viral library identified binding of drug molecules at three specific positions: loop 1 region, loop 2 region and β-sheet core pockets, the loop 2 region being the most common binding and stable site for the bulk of the molecules. In parallel, the protein was characterized by vaccine design perspective and harboured three potential B cell-derived T cell epitopes: PINTNSSPD, GVPINTNSS, and DHIGTRNPA. The epitopes are highly antigenic, virulent, non-allergic, non-toxic, bind with good affinity to the highly prevalent DRB*0101 allele and show an average population coverage of 95.04%. A multi-epitope vaccine ensemble which was 83 amino acids long was created. This was highly immunogenic, robust in generating both humoral and cellular immune responses, thermally stable, and had good physicochemical properties that could be easily analyzed in in vivo and in vitro studies. Conformational dynamics of both drug and vaccine ensemble with respect to the receptors are energetically stable, shedding light on favourable conformation and chemical interactions. These facts were validated by subjecting the complexes to relative and absolute binding free energy methods of MMGB/PBSA and WaterSwap. A strong agreement on the system stability was disclosed that supported ligand high affinity potential for the receptors. Collectively, this work sought to provide preliminary experimental data of existing anti-viral drugs as a possible therapy for COVID-19 infections and a new peptide-based vaccine for protection against this pandemic virus.

Computational Design and Preliminary Serological Analysis of a Novel Multi-Epitope Vaccine Candidate against Onchocerciasis and Related Filarial Diseases

: Onchocerciasis is a skin and eye disease that exerts a heavy socio-economic burden, particularly in sub-Saharan Africa, a region which harbours greater than 96% of either infected or at-risk populations. The elimination plan for the disease is currently challenged by many factors including amongst others; the potential emergence of resistance to the main chemotherapeutic agent, ivermectin (IVM). Novel tools, including preventative and therapeutic vaccines, could provide additional impetus to the disease elimination tool portfolio. Several observations in both humans and animals have provided evidence for the development of both natural and artificial acquired immunity. In this study, immuno-informatics tools were applied to design a filarial-conserved multi-epitope subunit vaccine candidate, (designated Ov-DKR-2) consisting of B-and T-lymphocyte epitopes of eight immunogenic antigens previously assessed in pre-clinical studies. The high-percentage conservation of the selected proteins and epitopes predicted in related nematode parasitic species hints that the generated chimera may be instrumental for cross-protection. Bioinformatics analyses were employed for the prediction, refinement, and validation of the 3D structure of the Ov-DKR-2 chimera. In-silico immune simulation projected significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2 responses. Preliminary immunological analyses revealed that the multi-epitope vaccine candidate reacted with antibodies in sera from both onchocerciasis-infected individuals, endemic normals as well as loiasis-infected persons but not with the control sera from European individuals. These results support the premise for further characterisation of the engineered protein as a vaccine candidate for onchocerciasis.

Computational formulation and immune dynamics of a multi-peptide vaccine candidate against Crimean-Congo hemorrhagic fever virus

The sole objective of this research is to devise an epitope-based vaccine candidate as prophylaxis for the Crimean-Congo hemorrhagic fever virus (CCHFV) using the knowledge of immunoinformatics and structural biology. Importantly, CCHFV outbreaks have increased in several countries resulting in increased mortality up to 40% due to the lack of prospective medication and an efficient vaccine. In this study, we have used several immunoinformatic tools and servers to anticipate potent B-cell and T-cell epitopes from the CCHFV glycoprotein with the highest antigenicity. After a comprehensive evaluation, a vaccine candidate was designed using 6 CD8⁺, 3 CD4⁺, and 7 B-cell epitopes with appropriate linkers. To enhance the vaccine's efficiency, we added Mycobacterium tuberculosis lipoprotein LprG (Rv1411c) to the vaccine as an adjuvant. The final construct was composed of a total of 468 amino acid residues. The epitope included in the construct showed 98% worldwide population coverage. Importantly, the construct appeared as antigenic, immunogenic, soluble, and non-allergenic in nature. To explore further, we modelled the three-dimensional (3D) structure of the constructed vaccine. Our chimeric vaccine showed stable and strong interactions for toll-like receptor 2 (TLR2) found on the cell surface. Moreover, the dynamics simulation of immune response showed elevated levels of cellular immune activity and faster clearance of antigen from the body upon repetitive exposure. Finally, the optimized codon (CAI≈1) ensured the marked translation efficiency of the vaccine protein in E. coli strain K12 bacterium followed by the insertion of construct DNA into the cloning vector pET28a (+). We believe that the designed vaccine chimera could be useful in vaccine development to fight CCHFV outbreaks.

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.

Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused COVID-19 disease in China. So far, no vaccine has licensed to protect against infection with COVID-19, therefore an effective COVID-19 vaccine needed. The aim of this study was to predict antigenic peptides of SARS-CoV-2 for designing the COVID-19 vaccine using immunoinformatic analysis. In this study, T and B-cell epitopes of S protein were predicted and screened based on the antigenicity, toxicity, allergenicity, and cross-reactivity with human proteomes. The epitopes were joined by the appropriate linker. LT-IIc as an adjuvant was attached to the end of the structure. The secondary and 3D structure of the vaccine was predicted. The refinement process was performed to improve the quality of the 3D model structure; the validation process is performed using the Ramachandran plot and ProSA z-score. The proposed vaccine's binding affinity to the HLA-A11:01 and HLA-DRB1_01:01 molecule was evaluated by molecular docking. Using molecular dynamics, the stability of vaccine-HLA complexes was also evaluated. Finally, in silico gene cloning was performed in the pET30a (+) vector. The findings suggest that the current vaccine may be a promising vaccine to prevent SARS-CoV-2 infection.

Design of a multi-epitope-based vaccine targeting M-protein of SARS-CoV2: an immunoinformatics approach

In the present study, one of the targets present on the envelopes of coronaviruses, membrane glycoprotein (M) was chosen for the design of a multi-epitope vaccine by Immunoinformatics approach. The B-cell and T-cell epitopes used for the construction of vaccine were antigenic, nonallergic and nontoxic. An adjuvant, β-defensin and PADRE sequence were included at the N-terminal end of the vaccine. All the epitopes were joined by linkers for decreasing the junctional immunogenicity. Various physicochemical parameters of the vaccine were evaluated. Secondary and tertiary structures were predicted for the vaccine construct. The tertiary structure was further refined, and various parameters related to the refinement of the protein structure were validated by using different tools. Humoral immunity induced by B-cells relies upon the identification of antigenic determinants on the surface of the vaccine construct. In this regard, the vaccine construct was found to consist of several B-cell epitopes in its three-dimensional conformation. Molecular docking of the vaccine was carried out with TLR-3 receptor to study their binding and its strength. Further, protein-protein interactions in the docked complex were visualized using LigPlot+. Population coverage analysis had shown that the multi-epitope vaccine covers 94.06% of the global population. The vaccine construct was successfully cloned in silico into pET-28a (+). Immune simulation studies showed the induction of primary, secondary and tertiary immune responses marked by the increased levels of antibodies, INF-γ, IL-2, TGF-β, B- cells, CD4+ and CD8+ cells. Finally, the vaccine construct was able to elicit immune response as desired.Communicated by Ramaswamy H. Sarma.

Designing a novel mRNA vaccine against SARS-CoV-2: An immunoinformatics approach

SARS-CoV-2 is the deadly virus behind COVID-19, the disease that went on to ravage the world and caused the biggest pandemic 21st century has witnessed so far. On the face of ongoing death and destruction, the urgent need for the discovery of a vaccine against the virus is paramount. This study resorted to the emerging discipline of immunoinformatics in order to design a multi-epitope mRNA vaccine against the spike glycoprotein of SARS-CoV-2. Various immunoinformatics tools were utilized to predict T and B lymphocyte epitopes. The epitopes were channeled through a filtering pipeline comprised of antigenicity, toxicity, allergenicity, and cytokine inducibility evaluation with the goal of selecting epitopes capable of generating both T and B cell-mediated immune responses. Molecular docking simulation between the epitopes and their corresponding MHC molecules was carried out. 13 epitopes, a highly immunogenic adjuvant, elements for proper sub-cellular trafficking, a secretion booster, and appropriate linkers were combined for constructing the vaccine. The vaccine was found to be antigenic, almost neutral at physiological pH, non-toxic, non-allergenic, capable of generating a robust immune response and had a decent worldwide population coverage. Based on these parameters, this design can be considered a promising choice for a vaccine against SARS-CoV-2.

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

BACKGROUND

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

METHODS

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

RESULTS

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

Identification of immunogenic T-cell peptides of Mycobacterium tuberculosis PE_PGRS33 protein

The development of a more efficient vaccine is needed to improve tuberculosis control. One of the current approaches is to identify immunogenic T-cell peptides that can elicit a protective and specific immune response. These peptides come from immunogenic proteins of the pathogen. The PE_PGRS33 protein of Mycobacterium tuberculosis has been proved immunogenic. However, little is known about immunogenic T-cell peptides of PE_PGRS33 and their interactions with MHC-II molecules. Therefore, we used the SYFPHEITHI database to determine the immunogenic PE_PGRS33 T-cell peptides. Next, we built homology models by using MOE v2018.1 software in order to obtain information about the specific interactions between the peptides and I-A^k. The AlgPred server was employed to look for allergenic sites in PE_PGRS33. We developed a sequence alignment between PE_PGRS33 and all the human proteins by using BLAST. Three peptides were commercially synthesized, and their activity was evaluated in vitro by the stimulation of PBMC from household contacts of TB patients. Our in silico results showed five immunogenic T-cell peptides. BLAST analysis showed low homology of PE_PGRS33 with human proteins and AlgPred did not reveal allergenic sites in PE_PGRS33. The three peptides triggered the activation of CD4⁺ T cells from the households contacts, showed by the production of IFN-γ. We identified three immunogenic peptides of PE_PGRS33 that demonstrated activity in vitro which allows to deepen into the immune response towards mycobacterial antigens, moving forward to the identification of new vaccine candidates.

Multiepitope-Based Subunit Vaccine Design and Evaluation against Respiratory Syncytial Virus Using Reverse Vaccinology Approach

Respiratory syncytial virus (RSV) is primarily associated with respiratory disorders globally. Despite the availability of information, there is still no competitive vaccine available for RSV. Therefore, the present study has been designed to develop a multiepitope-based subunit vaccine (MEV) using a reverse vaccinology approach to curb RSV infections. Briefly, two highly antigenic and conserved proteins of RSV (glycoprotein and fusion protein) were selected and potential epitopes of different categories (B-cell and T-cell) were identified from them. Eminently antigenic and overlapping epitopes, which demonstrated strong associations with their respective human leukocyte antigen (HLA) alleles and depicted collective ~70% coverage of the world's populace, were shortlisted. Finally, 282 amino acids long MEV construct was established by connecting 13 major histocompatibility complex (MHC) class-I with two MHC class-II epitopes with appropriate adjuvant and linkers. Adjuvant and linkers were added to increase the immunogenic stimulation of the MEV. Developed MEV was stable, soluble, non-allergenic, non-toxic, flexible and highly antigenic. Furthermore, molecular docking and molecular dynamics (MD) simulations analyses were carried out. Results have shown a firm and robust binding affinity of MEV with human pathogenic toll-like receptor three (TLR3). The computationally mediated immune response of MEV demonstrated increased interferon-γ production, a significant abundance of immunoglobulin and activation of macrophages which are essential for immune-response against RSV. Moreover, MEV codons were optimized and in silico cloning was performed, to ensure its increased expression. These outcomes proposed that the MEV developed in this study will be a significant candidate against RSV to control and prevent RSV-related disorders if further investigated experimentally.

Chimeric Vaccines Designed by Immunoinformatics-Activated Polyfunctional and Memory T Cells That Trigger Protection against Experimental Visceral Leishmaniasis

Many vaccine candidates against visceral leishmaniasis (VL) have been proposed; however, to date, none of them have been efficacious for the human or canine disease. On this basis, the design of leishmaniasis vaccines has been constantly changing, and the use of approaches to select specific epitopes seems to be crucial in this scenario. The ability to predict T cell-specific epitopes makes immunoinformatics an even more necessary approach, as in VL an efficient immune response against the parasite is triggered by T lymphocytes in response to Leishmania spp. immunogenic antigens. Moreover, the success of vaccines depends on the capacity to generate long-lasting memory and polyfunctional cells that are able to eliminate the parasite. In this sense, our study used a combination of different approaches to develop potential chimera candidate vaccines against VL. The first point was to identify the most immunogenic epitopes of Leishmania infantum proteins and construct chimeras composed of Major histocompatibility complex (MHC) class I and II epitopes. For this, we used immunoinformatics features. Following this, we validated these chimeras in a murine model in a thorough memory study and multifunctionality of T cells that contribute to a better elucidation of the immunological protective mechanisms of polyepitope vaccines (chimera A and B) using multicolor flow cytometry. Our results showed that in silico-designed chimeras can elicit polyfunctional T cells producing T helper (Th)1 cytokines, a strong immune response against Leishmania antigen, and the generation of central and effector memory T cells in the spleen cells of vaccinated animals that was able to reduce the parasite burden in this organ. These findings contribute two potential candidate vaccines against VL that can be used in further studies, and help in this complex field of vaccine development against this challenging parasite.

3CL hydrolase-based multiepitope peptide vaccine against SARS-CoV-2 using immunoinformatics

The present study provides the first multiepitope vaccine construct using the 3CL hydrolase protein of SARS-CoV-2. The coronavirus 3CL hydrolase (Mpro) enzyme is essential for proteolytic maturation of the virus. This study was based on immunoinformatics and structural vaccinology strategies. The design of the multiepitope vaccine was built using helper T-cell and cytotoxic T-cell epitopes from the 3CL hydrolase protein along with an adjuvant to enhance immune response; these are joined to each other by short peptide linkers. The vaccine also carries potential B-cell linear epitope regions, B-cell discontinuous epitopes, and interferon-γ-inducing epitopes. Epitopes of the constructed multiepitope vaccine were found to be antigenic, nonallergic, nontoxic, and covering large human populations worldwide. The vaccine construct was modeled, validated, and refined by different programs to achieve a high-quality three-dimensional structure. The resulting high-quality model was applied for conformational B-cell epitope selection and docking analyses with toll-like receptor-3 for understanding the capability of the vaccine to elicit an immune response. In silico cloning and codon adaptation were also performed with the pET-19b plasmid vector. The designed multiepitope peptide vaccine may prompt the development of a vaccine to control SARS-CoV-2 infection.

Immunoinformatics guided rational design of a next generation multi epitope based peptide (MEBP) vaccine by exploring Zika virus proteome

Zika virus (ZIKV) is an RNA virus that has spread through mosquito sting. Currently, no vaccine and antiviral medication available so far against ZIKV. Therefore, it has fostered a study to design MEBP vaccine enabling effective prevention against the ZIKV infection. In this study combination of immuno-informatics and molecular docking approach was used to constitute a MEBP vaccine. The ZIKV proteome was used for prediction of B-cell, T-cell (HTL & CTL) and IFN-γ epitopes. After prediction, highly antigenic and overlapping epitopes have been shortlisted which includes 14 CTL and 11 HTL epitopes that have been linked to the final peptide through AAY and GPGPG linkers respectively. An adjuvant at the N-end of the vaccine was added to improve the immunogenicity of the vaccine through the EAAAK linker. The final construct constitutes 435 amino acids after the addition of linkers and adjuvant. The existence of B-cell and IFN-γ epitopes affirms the humoral and cell-mediated immune responses acquired by the construct. Allergenicity, antigenicity and different physiochemical attributes of the vaccine were evaluated to assure its safety and immunogenicity profile. In fact, the construct was antigenic and non-allergenic. Docking was performed among vaccine and TLR-3 to evaluate the binding affinity and the molecular interaction. Finally, the construct was subjected to In silico cloning to confers the authenticity of its expression efficiency. However, the proposed construct need to be validate experimentally to ensure its safety and immunogenic profile.

Novel Chimeric Multiepitope Vaccine for Streptococcosis Disease in Nile Tilapia (Oreochromis niloticus Linn.)

Streptococcus agalactiae is a causative agent of streptococcosis disease in various fish species, including Nile tilapia (Oreochromis niloticus Linn.). Vaccination is an effective disease prevention and control method, but limitations remain for protecting against catastrophic mortality of fish infected with different strains of streptococci. Immunoproteomics analysis of S. agalactiae was used to identify antigenic proteins and construct a chimeric multiepitope vaccine. Epitopes from five antigenic proteins were shuffled in five helices of a flavodoxin backbone, and in silico analysis predicted a suitable RNA and protein structure for protein expression. 45F2 and 42E2 were identified as the best candidates for a chimeric multiepitope vaccine. Recombinant plasmids were constructed to produce a recombinant protein vaccine and DNA vaccine system. Overexpressed proteins were determined to be 30 kDa and 25 kDa in the E. coli and TK1 systems, respectively. The efficacy of the chimeric multiepitope construct as a recombinant protein vaccine and DNA vaccine was evaluated in Nile tilapia, followed by S. agalactiae challenge at 1 × 107 CFU/mL. Relative percentage survival (RPS) and cumulative mortality were recorded at approximately 57-76% and 17-30%, respectively. These chimeric multiepitope vaccines should be applied in streptococcosis disease control and developed into a multivalent vaccine to control multiple diseases.

Multi-epitope based vaccine against yellow fever virus applying immunoinformatics approaches

Yellow fever disease is considered a re-emerging major health issue which has caused recent outbreaks with a high number of deaths. Tropical countries, mainly African and South American, are the most affected by Yellow fever outbreaks. Despite the availability of an attenuated vaccine, its use is limited for some groups such as pregnant and nursing women, immunocompromised and immunosuppressed patients, elderly people >65 years, infants <6 months and patients with biological disorders like thymus disorders. In order to achieve new preventive measures, we applied immunoinformatics approaches to develop a multi-epitope-based subunit vaccine for Yellow fever virus. Different epitopes, related to humoral and cell-mediated immunity, were predicted for complete polyproteins of two Yellow fever strains (Asibi and 17 D vaccine). Those epitopes common for both strains were mapped into a set of 137 sequences of Yellow fever virus, including 77 sequences from a recent outbreak at the state of Minas Gerais, southeast Brazil. Therefore, the present work uses robust bioinformatics approaches for the identification of a multi-epitope vaccine against the Yellow fever virus. Our results indicate that the identified multi-epitope vaccine might stimulate humoral and cellular immune responses and could be a potential vaccine candidate against Yellow fever virus infection. Hence, it should be subjected to further experimental validations. Communicated by Ramaswamy H. Sarma.

Proteome-wide screening for designing a multi-epitope vaccine against emerging pathogen Elizabethkingia anophelis using immunoinformatic approaches

Elizabethkingia anophelis is an emerging human pathogen causing neonatal meningitis, catheter-associated infections and nosocomial outbreaks with high mortality rates. Besides, they are resistant to most antibiotics used in empirical therapy. In this study, therefore, we used immunoinformatic approaches to design a prophylactic peptide vaccine against E. anophelis as an alternative preventive measure. Initially, cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes were predicted from the highest antigenic protein. The CTL and HTL epitopes together had a population coverage of 99.97% around the world. Eventually, six CTL, seven HTL, and two LBL epitopes were selected and used to construct a multi-epitope vaccine. The vaccine protein was found to be highly immunogenic, non-allergenic, and non-toxic. Codon adaptation and in silico cloning were performed to ensure better expression within E. coli K12 host system. The stability of the vaccine structure was also improved by disulphide bridging. In addition, molecular docking and dynamics simulation revealed strong and stable binding affinity between the vaccine and toll-like receptor 4 (TLR4) molecule. The immune simulation showed higher levels of T-cell and B-cell activities which was in coherence with actual immune response. Repeated exposure simulation resulted in higher clonal selection and faster antigen clearance. Nevertheless, experimental validation is required to ensure the immunogenic potency and safety of this vaccine to control E. anophelis infection in the future.Communicated by Ramaswamy H. Sarma.

Designing self-assembled peptide nanovaccine against Streptococcus pneumoniae: An in silico strategy

Streptococcus pneumoniae is the main cause of diseases such as meningitis, pneumoniae and sepsis, especially in children and old people. Due to costly antibiotic treatment, and increasing resistance of pneumococcus, developing high-efficient protective vaccine against this pathogen is an urgent need. Although the pneumoniae polysaccharide vaccine (PPV) and pneumonia conjugate vaccines (PCV) are the efficient pneumococcal vaccine in children and adult groups, but the serotype replacement of S. pneumoniae strains causes the reduction in efficacy of such vaccines. For overcoming the aforesaid drawbacks epitope-based vaccines are introduced as the relevant alternative. In our previous research, the epitope vaccine was designed based on immunodominant epitopes from PspA, CbpA antigens as cellular stimulants and PhtD, PiuA as humoral stimulants. Because the low immunogenicity is the main disadvantage of epitope vaccine, in the current study, we applied coiled-coil self-assembled structures for developing our vaccine. Recently, self-assembled peptide nanoparticles (SAPNs) have gained much attention in the field of vaccine development due to their multivalency, self-adjuvanticity, biocompatibility, and size similarity to pathogen. In this regard, the final designed vaccine is comprised of cytotoxic T lymphocytes (CTL) epitopes from PspA and CbpA, helper T lymphocytes (HTL) epitopes from PhtD and PiuA, the pentamer and trimmer oligomeric domains form 5-stranded and 3-stranded coiled-coils as self-assembled scaffold, Diphtheria toxoids (DTD) as a universal T-helper, which fused to each other with appropriate linkers. The four different arrangements based on the order of above-mentioned compartments were constructed, and each of them were modeled, and validated to find the 3D structure. The structural, physicochemical, and immunoinformatics analyses of final vaccine construct represented that our vaccine could stimulate potent immune response against S. pneumoniae; however, the potency of that should be approved via various in vivo and in vitro immunological tests.

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Stimulating cellular and especially humoral immunities are essential for protection against Streptococcus Pneumoniae.

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Immunodominant epitopes were selected from highly protective antigens of S. pneumoniae: PspA, CbpA, PiuA, PhtD.

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In order to bypass the low immunogenicity of epitope-based peptide vaccine the self-assembled motifs, coiled-coil structure, was applied as the vaccine scaffold.

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The structural, physicochemical, and immunoinformatics results indicate that the designed vaccine can incite strong immune response against S. pneumoniae.

An immunoinformatics approach for design and validation of multi-subunit vaccine against Cryptosporidium parvum

An immunoinformatics-based approach is explored for potential multi-subunit vaccine candidates against Cryptosporidium parvum. We performed protein structure based systematic methodology for the development of a proficient multi-subunit vaccine candidate against C. parvum based on their probability of antigenicity, allergenicity and transmembrane helices as the screening criteria. The best-screened epitopes like B-cell epitopes (BCL), Helper T-lymphocytes (HTL) and cytotoxic T- lymphocytes (CTL) were joined by using the appropriate linkers to intensify and develop the presentation and processing of the antigenic molecules. Modeller software was used to generate the best 3D model of the subunit protein. RAMPAGE and other web servers were employed for the validation of the modeled protein. Furthermore, the predicted modeled structure was docked with the two known receptors like TLR2 and TLR4 through ClusPro web server. Based on the docking score, the multi-subunit vaccine docked with TLR2 was subjected to energy minimization by molecular dynamics (MD) simulation to examine their stability within a solvent system. From the simulation study, we found that the residue Glu-107 of subunit vaccine formed a hydrogen bond interaction with Arg-299 of the TLR2 receptor throughout the time frame of the MD simulation. The overall results showed that the multi-subunit vaccine could be an efficient vaccine candidate against C. parvum.

Development of a conserved chimeric vaccine based on helper T-cell and CTL epitopes for induction of strong immune response against Schistosoma mansoni using immunoinformatics approaches

Currently, three recombinant antigens based vaccines are under clinical trials against Schistosomiasis, but there is no vaccine available for prophylaxis or therapeutic. This study was conducted to construct a multi-epitope based vaccine against Schistosoma mansoni via utilizing Sm14, Sm21.7, Sm23, Sm29, Smp80, Sm-CB and SM-TSP-2 antigens. Helper T lymphocyte (HTL), cytotoxic T lymphocyte (CTL) and IFN-γ epitopes were predicted. Furthermore, Pan HLA DR-binding epitope was added to the vaccine. Moreover, 50S ribosomal protein L7/L12 of Mycobacterium tuberculosis as a novel TLR4 agonist was applied. The TAT peptide was added to the vaccine to augment intracellular delivery. The selected epitopes were linked together through appropriate linkers and chimeric vaccine was constructed with 617 amino acids with molecular weight of 65.43 kDa. Physico-chemical properties revealed a soluble protein with antigenic and non-allergic properties. Further analyses validated the stability of the construct that was able to interact with TLR4. Immunoinformatics analysis demonstrated the strong potential of constructed vaccine to stimulate T and B-cell mediated immune responses. In summary, obtained data indicated that the proposed vaccine can properly induce both T and B cells immune responses and could possibly be utilized for prophylactic or therapeutic aims in response to infection caused by S. mansoni.

Epitope Prediction by Novel Immunoinformatics Approach: A State-of-the-art Review

Immunoinformatics is a science that helps to create significant immunological information using bioinformatics softwares and applications. One of the most important applications of immunoinformatics is the prediction of a variety of specific epitopes for B cell recognition and T cell through MHC class I and II molecules. This method reduces costs and time compared to laboratory tests. In this state-of-the-art review, we review about 50 papers to find the latest and most used immunoinformatic tools as well as their applications for predicting the viral, bacterial and tumoral structural and linear epitopes of B and T cells. In the clinic, the main application of prediction of epitopes is for designing peptide-based vaccines. Peptide-based vaccines are a considerably potential alternative to low-cost vaccines that may reduce the risks related to the production of common vaccines.

Development a multi-epitope driven subunit vaccine for immune response reinforcement against Serogroup B of Neisseria meningitidis using comprehensive immunoinformatics approaches

Serogroup B of Neisseria meningitidis is the main cause of mortality due to meningococcal meningitis. Despite of many investigations, there is still no effective vaccine to prevent this serious infection. Therefore, this study was conducted to design a multi-epitope based vaccine through immunoinformatics approaches. The T CD4⁺ and TCD8⁺ cells along with IFN-γ inducing epitopes were selected from TspA, FHbp, NspA, TbpB, PilQ and NspA antigens form serogroup B of Neisseria meningitidis. Furthermore, to induce strong helper T lymphocytes (HTLs) responses, Pan HLA DR-binding epitope (PADRE) was used. In addition, loop 5 and 7 of the PorB as a TLR2 agonist were added to the vaccine construct. Physico-chemical properties, secondary and tertiary structures of the proposed construct were assessed. Finally, homology modeling, refinement and molecular docking were carried out to evaluated the construct tertiary structure and protein-protein interaction, respectively. By fusing the CTL, HTL and IFN-γ predicted epitopes along with suitable adjuvant and linkers, a multi-epitope vaccine was constructed with a TAT sequence of HIV at the N-terminal. Immunoinformatics analyses confirmed a soluble and non-allergic protein with a molecular weight of 62.5 kDa and high antigenicity. Furthermore, the stability of the multi-epitope construct was established and showed strong potential to generate humoral and cell-mediated immune responses. In addition, through molecular docking and dynamic simulation, the microscopic interaction between the vaccine construct and TLR-2 were verified. In summary, immunoinformatics analysis demonstrated that the constructed multi-epitope vaccine had a strong potential of T and B-cell stimulation and it could possibly be used for prophylactic or therapeutic aims to protect against serogroup B of N. meningitidis.

Decreasing the immunogenicity of Erwinia chrysanthemi asparaginase via protein engineering: computational approach

Immunogenicity of therapeutic proteins is one of the main challenges in disease treatment. L-Asparaginase is an important enzyme in cancer treatment which sometimes leads to undesirable side effects such as immunogenic or allergic responses. Here, to decrease Erwinase (Erwinia chrysanthemiL-Asparaginase) immunogenicity, which is the main drawback of the enzyme, firstly conformational B cell epitopes of Erwinase were predicted from three-dimensional structure by three different computational methods. A few residues were defined as candidates for reducing immunogenicity of the protein by point mutation. In addition to immunogenicity and hydrophobicity, stability and binding energy of mutants were also analyzed computationally. In order to evaluate the stability of the best mutant, molecular dynamics simulation was performed. Among mutants, H240A and Q239A presented significant reduction in immunogenicity. In contrast, the immunogenicity scores of D235A slightly decreased according to two servers. Binding affinity of substrate to the active site reduced significantly in K265A and E268A. The final results of molecular dynamics simulation indicated that H240A mutation has not changed the stability, flexibility, and the total structure of desired protein. Overall, point mutation can be used for reducing immunogenicity of therapeutic proteins, in this context, in silico approaches can be used to screen suitable mutants.

FeptideDB: A web application for new bioactive peptides from food protein

BACKGROUND

Bioactive peptides derived from food are important sources for alternative medicine and possess therapeutic activity. Several biochemical methods have been achieved to isolate bioactive peptides from food, which are tedious and time consuming. In silico methods are an alternative process to reduce cost and time with respect to bioactive peptide production. In this paper, FeptideDB was used to collect bioactive peptide (BP) data from both published research articles and available bioactive peptide databases. FeptideDB was developed to assist in forecasting bioactive peptides from food by combining peptide cleavage tools and database matching. Furthermore, this application was able to predict the potential of cleaved peptides from 'enzyme digestion module' to identify new ACE (angiotensin converting enzyme) inhibitors using an automatic molecular docking approach.

RESULTS

The FeptideDB web application contains tools for generating all possible peptides cleaved from input protein by various available enzymes. This database was also used for analysis and visualization to assist in bioactive peptide discovery. One module of FeptideDB has the ability to create 3-dimensional peptide structures to further predict inhibitors for the target protein, ACE (angiotensin converting enzyme).

CONCLUSIONS

FeptideDB is freely available to researchers who are interested in exploring bioactive peptides. The FeptideDB interface is easy to use, allowing users to rapidly retrieve data based on desired search criteria. FeptideDB is freely available at http://www4g.biotec.or.th/FeptideDB/. Ultimately, FeptideDB is a computational aid for assessing peptide bioactivities.

In silico rational design of a novel tetra-epitope tetanus vaccine with complete population coverage using developed immunoinformatics and surface epitope mapping approaches

Presentation of many unwanted epitopes within tetanus toxoid vaccine to lymphocyte clones may lead to production of many unwanted antibodies. Moreover an ideal vaccine must cover all individuals in a population that is dependent to the kinds of human leukocyte antigen alleles. Concerning these issues, our study was aimed to in silico design of a multi-epitope tetanus vaccine (METV) in order to improve population coverage and protectivity of tetanus vaccine as well as reduction of complications. Concerning these issues, a novel rational filtration was implemented to design a novel METV using immunoinformatics and surface epitope mapping approaches. Prediction of epitopes for tetanus toxin was performed in the candidate country in which the frequency had been gathered from almost all geographical distributions. The most strong binder epitopes for major histocompatibility complex class II were selected and among them the surface epitopes of native toxin were selected. The population coverage of the selected epitopes was estimated. The final candidate epitopes had highly population coverage. Molecular docking was performed to prediction of binding affinity of our candidate epitopes to the HLA-DRB1 alleles. At first, 680 strong binder epitopes were predicted. Among them 11 epitopes were selected. Finally, 4 epitopes had the most population coverage and suggested as a tetra-epitope tetanus vaccine. 99.41% of inessential strong binders were deleted using our tree steps filtration. HLA-DP had the most roles in epitope presentation. Molecular docking analysis proved the strong binding affinity of candidate epitopes to the HLA-DRB1 alleles. In conclusion, we theoretically reduced 99.41% of unwanted antibodies using our novel filtration strategies. Our tetra-epitope tetanus vaccine showed 100% population coverage in the candidate country. Furthermore, it was demonstrated that HLA-DP and HLA-DQ had more potential in epitope presentation in comparison to HLA-DRB1.

Towards the first multi-epitope recombinant vaccine against Crimean-Congo hemorrhagic fever virus: A computer-aided vaccine design approach

Crimean-Congo hemorrhagic fever (CCHF) is considered one of the major public health concerns with case fatality rates of up to 80%. Currently, there is no effective approved vaccine for CCHF. In this study, we used a computer-aided vaccine design approach to develop the first multi-epitope recombinant vaccine for CCHF. For this purpose, linear B-cell and T-cell binding epitopes from two structural glycoproteins of CCHF virus including Gc and Gn were predicted. The epitopes were further studied regarding their antigenicity, allergenicity, hydrophobicity, stability, toxicity and population coverage. A total number of seven epitopes including five T-cell and two B-cell epitopes were screened for the final vaccine construct. Final vaccine construct composed of 382 amino acid residues which were organized in four domains including linear B-cell, T-cell epitopes and cholera toxin B-subunit (CTxB) along with heat labile enterotoxin IIc B subunit (LT-IIc) as adjuvants. All the segments were joined using appropriate linkers. The physicochemical properties as well as the presence of IFN-γ inducing epitopes in the proposed vaccine, was also checked to determining the vaccine stability, solubility and its ability to induce cell-mediated immune responses. The 3D structure of proposed vaccine was subjected to the prediction of computational B-cell epitopes and molecular docking studies with MHC-I and II molecules. Furthermore, molecular dynamics stimulations were performed to study the vaccine-MHCs complexes stability during stimulation time. The results suggest that our proposed vaccine was stable, well soluble in water and potentially antigenic. Results also demonstrated that the vaccine can induce both humoral and cell-mediated immune responses and could serve as a promising anti-CCHF vaccine candidate.

In-silico design of a multi-epitope vaccine candidate against onchocerciasis and related filarial diseases

Onchocerciasis is a parasitic disease with high socio-economic burden particularly in sub-Saharan Africa. The elimination plan for this disease has faced numerous challenges. A multi-epitope prophylactic/therapeutic vaccine targeting the infective L3 and microfilaria stages of the parasite's life cycle would be invaluable to achieve the current elimination goal. There are several observations that make the possibility of developing a vaccine against this disease likely. For example, despite being exposed to high transmission rates of infection, 1 to 5% of people have no clinical manifestations of the disease and are thus considered as putatively immune individuals. An immuno-informatics approach was applied to design a filarial multi-epitope subunit vaccine peptide consisting of linear B-cell and T-cell epitopes of proteins reported to be potential novel vaccine candidates. Conservation of the selected proteins and predicted epitopes in other parasitic nematode species suggests that the generated chimera could be helpful for cross-protection. The 3D structure was predicted, refined, and validated using bioinformatics tools. Protein-protein docking of the chimeric vaccine peptide with the TLR4 protein predicted efficient binding. Immune simulation predicted significantly high levels of IgG₁, T-helper, T-cytotoxic cells, INF-γ, and IL-2. Overall, the constructed recombinant putative peptide demonstrated antigenicity superior to current vaccine candidates.

Application of built-in adjuvants for epitope-based vaccines

Several studies have shown that epitope vaccines exhibit substantial advantages over conventional vaccines. However, epitope vaccines are associated with limited immunity, which can be overcome by conjugating antigenic epitopes with built-in adjuvants (e.g., some carrier proteins or new biomaterials) with special properties, including immunologic specificity, good biosecurity and biocompatibility, and the ability to vastly improve the immune response of epitope vaccines. When designing epitope vaccines, the following types of built-in adjuvants are typically considered: (1) pattern recognition receptor ligands (i.e., toll-like receptors); (2) virus-like particle carrier platforms; (3) bacterial toxin proteins; and (4) novel potential delivery systems (e.g., self-assembled peptide nanoparticles, lipid core peptides, and polymeric or inorganic nanoparticles). This review primarily discusses the current and prospective applications of these built-in adjuvants (i.e., biological carriers) to provide some references for the future design of epitope-based vaccines.

Evaluating the effect of BDNF Val66Met polymorphism on complex formation with HAP1 and Sortilin1 via structural modeling

Brain derived neurotrophic factor (BDNF) has a critical role in the neurogenesis, differentiation, survival of the neurons, regulation of the appetite, and energy homeostasis. Two key proteins, Huntingtin associated protein-1 (HAP1) and sortilin1, regulate the intracellular trafficking and stabilization of the precursor proBDNF through interaction with its prodomain region and mark it for secretion. Evidence suggests that the most frequent single nucleotide polymorphism (SNP) of BDNF gene (rs6265) has been associated with different psychiatric, neurodegenerative and eating disorders. In this study, structural bioinformatics and molecular dynamics (MD) simulations were applied, in order to get precise insights into the impact of Val66Met polymorphism on the proBDNF structure and its interaction with HAP1 and Sortilin1. Homology modeling, structure validation, refinement and also protein-protein docking were performed using appropriate servers. The stability, the fluctuations and the compactness of protein complexes were measured by MD simulation parameters including root mean square deviation (RMSD), root mean square fluctuation (RMSF) and Radius of gyration (Rg), respectively. The mutant proBDNF complexes with HAP1 and Sortilin1 revealed higher RMSD and RMSF values and also variable R_g over time compared with wild-type proBDNF. These computational results indicated that, wild-type proBDNF possessed more stable structure in binding with HAP1 and Sortilin1 compared with its mutant form. Therefore, Val66Met SNP could be deleterious due to making structural changes. It may cause a decrease in proBDNF secretion, which could possibly lead to different psychiatric, neurodegenerative and eating disorders. Further experimental lab studies are required for a more accurate conclusion.