scholarly journals Designing A Candidate Multi-Epitope Vaccine Against SARS-CoV-2 Using Reverse Vaccinology Approach

2021 ◽  
Author(s):  
Amir Atapour ◽  
Ali Golestan
Keyword(s):  
Molecular Docking ◽  
B Cell ◽  
In Silico ◽  
Md Simulation ◽  
Peptide Sequence ◽  
Reverse Vaccinology ◽  
Effective Vaccine ◽  
Epitope Vaccine ◽  
B Cell Epitopes

Abstract Coronavirus 2019 (COVID-19) infection is a global epidemic that is spreading dramatically from day today. Currently, many efforts have been made against COVID-19 through the designing or developing of specific vaccines or drugs, worldwide. Unfortunately, to date, it has not been successful. Therefore, an effective vaccine against COVID-19 is mandatory. In this study, we used the bioinformatics approach to design an effective multi-epitope vaccine against COVID-19 based on Spike protein. Here, we implemented in silico tools to identify potential T and B cell epitopes that can induce cellular and humoral immunity. Then, the peptide sequence of potential T, B cell epitopes, and flagellin (as an adjuvant molecule) was joined together by suitable linkers to construct of candidate multi-epitope vaccine (MEV). Subsequently, immunological and structural evaluations such as antigenicity, 3D modeling, etc. were performed. In the following, molecular docking of vaccine constructs with Toll-Like Receptors 5 (TLR5), Molecular Dynamics (MD) simulation as well as in silico cloning were carried out. Immunological and structural computational data showed that designed MEV potentially has proper capacity for inducing cellular and humoral immune responses against COVID-19. Based on the preliminary results, in vitro and in vivo experiments are required for validation in the future. Keywords: COVID-19, Vaccine, Reverse Vaccinology, Multi-epitope, Molecular docking, MD Simulation.

scholarly journals Design of a candidate multi-epitope vaccine against SARS-CoV-2 using reverse vaccinology approach

2021 ◽  
Author(s):  
Amir Atapour ◽  
Ali Golestan
Keyword(s):  
B Cell ◽  
In Silico ◽  
Peptide Sequence ◽  
Epitope Vaccine ◽  
B Cell Epitopes ◽  
S Protein ◽  
Global Epidemic ◽  
In Vivo Experiments

Abstract Coronavirus 2019 (COVID-19) infection as a global epidemic that is spreading dramatically day to day. Currently, many efforts have been made against COVID-19 through the designing or developing of specific vaccine or drug, worldwide. In this study, we used the bioinformatics approach to design an effective multi-epitope vaccine against COVID-19 based on Spike (S) protein. Here, we employed in silico tools to identify potential T and B cell epitopes from S protein that have the ability to induce cellular and humoral immunity. Then, the peptide sequence of potential T, B cell epitopes and flagellin (as adjuvant molecule) were joined together by suitable linkers to construct of candidate multi-epitope vaccine (MEV). Subsequently, immunological and structural evaluations such as antigenicity, allergenicity, 3D modeling, molecular docking, fast flexibility simulations as well as in silico cloning were performed. Immunological and structural computational data showed that designed MEV potentially has proper capacity for inducing of cellular and humoral immune responses against COVID-19. Based on the preliminary results, in vitro and in vivo experiments are required for validation in the future.

scholarly journals In silico-guided sequence modifications of K-ras epitopes improve immunological outcome against G12V and G13D mutant KRAS antigens

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5056 ◽  
Author(s):  
Allan Wee Ren Ng ◽  
Pei Jun Tan ◽  
Winfrey Pui Yee Hoo ◽  
Dek Shen Liew ◽  
Michelle Yee Mun Teo ◽  
...  
Keyword(s):  
B Cell ◽  
In Silico ◽  
Secondary Structure Prediction ◽  
Cell Epitope ◽  
Peptide Sequence ◽  
Single Amino Acid ◽  
Peptide Vaccines ◽  
Exon 2 ◽  
B Cell Epitopes

Background Somatic point substitution mutations in the KRAS proto-oncogene primarily affect codons 12/13 where glycine is converted into other amino acids, and are highly prevalent in pancreatic, colorectal, and non-small cell lung cancers. These cohorts are non-responsive to anti-EGFR treatments, and are left with non-specific chemotherapy regimens as their sole treatment options. In the past, the development of peptide vaccines for cancer treatment was reported to have poor AT properties when inducing immune responses. Utilization of bioinformatics tools have since become an interesting approach in improving the design of peptide vaccines based on T- and B-cell epitope predictions. Methods In this study, the region spanning exon 2 from the 4th to 18th codon within the peptide sequence of wtKRAS was chosen for sequence manipulation. Mutated G12V and G13D K-ras controls were generated in silico, along with additional single amino acid substitutions flanking the original codon 12/13 mutations. IEDB was used for assessing human and mouse MHC class I/II epitope predictions, as well as linear B-cell epitopes predictions, while RNA secondary structure prediction was performed via CENTROIDFOLD. A scoring and ranking system was established in order to shortlist top mimotopes whereby normalized and reducing weighted scores were assigned to peptide sequences based on seven immunological parameters. Among the top 20 ranked peptide sequences, peptides of three mimotopes were synthesized and subjected to in vitro and in vivo immunoassays. Mice PBMCs were treated in vitro and subjected to cytokine assessment using CBA assay. Thereafter, mice were immunized and sera were subjected to IgG-based ELISA. Results In silico immunogenicity prediction using IEDB tools shortlisted one G12V mimotope (68-V) and two G13D mimotopes (164-D, 224-D) from a total of 1,680 candidates. Shortlisted mimotopes were predicted to promote high MHC-II and -I affinities with optimized B-cell epitopes. CBA assay indicated that: 224-D induced secretions of IL-4, IL-5, IL-10, IL-12p70, and IL-21; 164-D triggered IL-10 and TNF-α; while 68-V showed no immunological responses. Specific-IgG sera titers against mutated K-ras antigens from 164-D immunized Balb/c mice were also elevated post first and second boosters compared to wild-type and G12/G13 controls. Discussion In silico-guided predictions of mutated K-ras T- and B-cell epitopes were successful in identifying two immunogens with high predictive scores, Th-bias cytokine induction and IgG-specific stimulation. Developments of such immunogens are potentially useful for future immunotherapeutic and diagnostic applications against KRAS(+) malignancies, monoclonal antibody production, and various other research and development initiatives.

scholarly journals Design of a Novel Multi-Epitope Vaccine Against Echinococcus granulosus in Immunoinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingkai Yu ◽  
Yuejie Zhu ◽  
Yujiao Li ◽  
Zhiqiang Chen ◽  
Tong Sha ◽  
...  
Keyword(s):  
Molecular Docking ◽  
T Cell ◽  
B Cell ◽  
Echinococcus Granulosus ◽  
The Body ◽  
T Cell Epitopes ◽  
Epitope Vaccine ◽  
B Cell Epitopes

All the time, echinococcosis is a global zoonotic disease which seriously endangers public health all over the world. In order to speed up the development process of anti-Echinococcus granulosus vaccine, at the same time, it can also save economic cost. In this study, immunoinformatics tools and molecular docking methods were used to predict and screen the antigen epitopes of Echinococcus granulosus, to design a multi-epitope vaccine containing B- and T-cell epitopes. The multi-epitope vaccine could activate B lymphocytes to produce specific antibodies theoretically, which could protect the human body against Echinococcus granulosus infection. It also could activate T lymphocytes and clear the infected parasites in the body. In this study, four CD8+ T-cell epitopes, three CD4+ T-cell epitopes and four B-cell epitopes of Protein EgTeg were identified by immunoinformatics methods. Meanwhile, three CD8+ T-cell epitopes, two CD4+ T-cell epitopes and four B-cell epitopes of Protein EgFABP1 were identified. We constructed the multi-epitope vaccine using linker proteins. The study based on the traditional methods of antigen epitope prediction, further optimized the prediction results combined with molecular docking technology and improved the precision and accuracy of the results. Finally, in vivo and in vitro experiments had verified that the vaccine designed in this study had good antigenicity and immunogenicity.

scholarly journals Designing a new multi epitope-based vaccine against COVID-19 disease: an immunoinformatic study based on reverse vaccinology approach

2021 ◽  
Author(s):  
Afshin Samimi Nemati ◽  
Majid Tafrihi ◽  
Fatemeh Sheikhi ◽  
Abolfazl Rostamian Tabari ◽  
Amirhossein Haditabar
Keyword(s):  
Molecular Docking ◽  
Docking Studies ◽  
High Rate ◽  
Effective Vaccine ◽  
Epitope Vaccine ◽  
Mhc I ◽  
Mhc Ii ◽  
Refinement Process

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has currently caused a significant pandemic among worldwide populations. The transmission speed and the high rate of mortality caused by the disease necessitate studies for the rapid designing and effective vaccine production. The purpose of this study is to predict and design a novel multi-epitope vaccine against the SARS-CoV-2 virus using bioinformatics approaches. Coronavirus envelope proteins, ORF7b, ORF8, ORF10, and NSP9 were selected as targets for epitope mapping using IEDB and BepiPred 2.0 Servers. Also, molecular docking studies were performed to determine the candidate vaccine's affinity to TLR3, TLR4, MHC I, and MHC II molecules. Thirteen epitopes were selected to construct the multi-epitope vaccine. We found that the constructed peptide has valuable antigenicity, stability, and appropriate half-life. The Ramachandran plot approved the quality of the predicted model after the refinement process. Molecular docking investigations revealed that antibody-mode in the Cluspro 2.0 server showed the lowest binding energy for MHCI, MHCII, TLR3, and TLR4. This study confirmed that the designed vaccine has a good antigenicity and stability and could be a proper vaccine candidate against the COVID-19 infectious disease though, in vitro and in vivo experiments are necessary to complete and confirm our results.

scholarly journals In silico Design of novel Multi-epitope recombinant Vaccine based on Coronavirus surface glycoprotein

2020 ◽  
Author(s):  
Mandana Behbahani
Keyword(s):  
T Cell ◽  
Immune Responses ◽  
B Cell ◽  
Ribosomal Protein ◽  
In Silico ◽  
Three Dimensional ◽  
Surface Glycoprotein ◽  
Dimensional Structure ◽  
Effective Vaccine ◽  
Epitope Vaccine

AbstractIt is of special significance to find a safe and effective vaccine against coronavirus disease 2019 (COVID-19) that can induce T cell and B cell -mediated immune responses. There is currently no vaccine to prevent COVID-19. In this project, a novel multi-epitope vaccine for COVID-19 virus based on surface glycoprotein was designed through application of bioinformatics methods. At the first, seventeen potent linear B-cell and T-cell binding epitopes from surface glycoprotein were predicted in silico, then the epitopes were joined together via different linkers. The ability of the selected epitopes to induce interferon-gamma was evaluate using IFNepitope web server. One final vaccine was constructed which composed of 398 amino acids and attached to 50S ribosomal protein L7/L12 as adjuvant. Physicochemical properties, as well as antigenicity in the proposed vaccines, were checked for defining the vaccine stability and its ability to induce cell-mediated immune responses. Three-dimensional structure of the mentioned vaccine was subjected to the molecular docking studies with MHC-I and MHC-II molecules. The results proposed that the multi-epitope vaccine with 50S ribosomal protein L7/L12 was a stable construct with high aliphatic content and high antigenicity.

Immunoinformatics approaches to design: A novel multi-epitope vaccine candidate against SARS-CoV-2 and it’s in silico expression (Preprint)

2021 ◽  
Author(s):  
Ravi Deval ◽  
Ayushi Saxena ◽  
Zeba Mueed ◽  
Dibyabhaba Pradhan ◽  
Pankaj Kumar Rai
Keyword(s):  
B Cell ◽  
In Silico ◽  
Vaccine Candidate ◽  
Rna Virus ◽  
Consensus Sequence ◽  
Structure Model ◽  
Epitope Vaccine ◽  
B Cell Epitopes ◽  
Epitope Vaccines ◽  
Further Development

BACKGROUND SARS-CoV-2, belonging to the Coronaviridae family, is a novel RNA virus, known for causing fatal disease in humans called COVID-19. Researchers all around the world are keen on developing a precise treatment or vaccine against this deadly disease. OBJECTIVE The main objective of this paper is to design a novel multi-epitope vaccine candidate against SARS-CoV-2 using immunoinformatics tools. METHODS A consensus sequence was generated from various genomes of SARS-Cov-2 available from various countries of the outbreak at the ViPR database using JalView software. T cell and B cell epitopes were predicted by restricting them to certain HLA alleles using various servers (nHLApred, NetMHCIIpan v.3.1, ABCpred) and were validated using IEDB tools. Using these epitopes and adjuvant, a multi-epitope vaccine was constructed in-silicoand was later subjected to allergenicity, antigenicity and physicochemical properties profiling along with identification of conformational B-cell epitopes. The designed vaccine was evaluated via codon optimization by the Jcat server and finally, it’s in-silicoexpression was done in pET-28a(+) vector using snap-gene software. RESULTS A total of 18 epitopes (both T and B cell) were predicted that constituted vaccine construct along with adjuvant and end tag. Vaccine construct was validated and its best structure model was successfully docked with human Toll-like receptors. In-silico expression of the designed vector was also seen in pET-28a(+) plasmid. CONCLUSIONS The designed novel vaccine candidate has been validated in-silico to elicit robust immune responses hence; it can be used as a potential model for further development of multi-epitope vaccines in the laboratory.

scholarly journals In Silico screening of T-cell and B-cell Epitopes of Rotavirus VP7 and VP4 proteins for Effective Vaccine Design

2019 ◽  
Vol 35 (1) ◽  
pp. 45-55
Author(s):  
Md Sadikur Rahman Shuvo ◽  
Sanjoy Kumar Mukharjee ◽  
Firoz Ahmed
Keyword(s):  
Developing Countries ◽  
T Cell ◽  
B Cell ◽  
In Silico ◽  
Child Death ◽  
Vaccine Design ◽  
Effective Vaccine ◽  
World Population ◽  
Promising Alternative ◽  
B Cell Epitopes

Rotavirus is one of the deadliest causative agents of childhood diarrhea which causes half a million child death across the globe, mostly in developing countries. However, effective vaccine strategies against rotavirus are yet to be established to prevent these unwanted premature deaths. In this regard, in silico vaccine design for rotavirus could be a promising alternative for developing countries due to its efficiency in shortening valuable time and cost. The present study described an epitope-based peptide vaccine design against rotavirus, using a combination of T-cell and B-cell epitope predictions and molecular docking approach. To perform this, sequences of rotavirus VP7 and VP4 proteins were retrieved from the NCBI database and subjected to different bioinformatics tools to predict most immunogenic T-cell and B-cell epitopes. From the identified epitopes, the sequence VMSKRSRSL of VP7 and TQFTDFVSL of VP4 was identified as the most potential epitopes based on their antigenicity, conservancy and interaction with major histocompatability complex I (MHC-I) alleles. Moreover, the peptide VMSKRSRSL interacted with human leukocyte antigen, HLA-B*08:01 and TQFTDFVSL interacted with HLA-A*02:06 with considerable binding energy and affinity score. Combined population coverage for our identified epitopes was found 70.53% and 45.64% for world population and South Asian population respectively. All these results suggest that, the epitopes identified in this study could be a very good vaccine candidate for the strains of rotavirus circulating in Bangladesh. However, as this study is completely dependent on computational prediction algorithms, further in vivo screening is required to come up in a precise conclusion about these epitopes for effective rotavirus vaccination. Bangladesh J Microbiol, Volume 35 Number 1 June 2018, pp 45-55

In silico prediction and in vitro identification of bluetongue virus 4 VP5 protein B-cell epitopes

2013 ◽  
Vol 98 (7) ◽  
pp. 3033-3047 ◽  
Author(s):  
L. Sun ◽  
E. C. Sun ◽  
T. Yang ◽  
Q. Y. Xu ◽  
Y. F. Feng ◽  
...  
Keyword(s):  
B Cell ◽  
In Silico ◽  
Bluetongue Virus ◽  
In Silico Prediction ◽  
B Cell Epitopes ◽  
Protein B

scholarly journals In silico prediction of b-cell epitopes of dengue virus – A reverse vaccinology approach

2020 ◽  
Author(s):  
Mohan Manikandan ◽  
Shanmugaraja Prabu ◽  
Krishnan Rajeswari ◽  
Rajagopalan Kamaraj ◽  
Sundar Krishnan
Keyword(s):  
Dengue Virus ◽  
B Cell ◽  
In Silico ◽  
Reverse Vaccinology ◽  
In Silico Prediction ◽  
B Cell Epitopes

scholarly journals An In-silico Deep Learning Approach to Multi-epitope Vaccine Design: A SARS-CoV-2 Case Study

2020 ◽  
Author(s):  
Zikun Yang ◽  
Paul Bogdan ◽  
Shahin Nazarian
Keyword(s):  
Deep Learning ◽  
T Lymphocytes ◽  
B Cell ◽  
In Silico ◽  
Vaccine Design ◽  
Learning Approach ◽  
Epitope Vaccine ◽  
B Cell Epitopes ◽  
Ctl Epitopes ◽  
The World

Abstract The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over 6.5 million cases and more than 380,000 deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in-silico deep learning approach for prediction and design of a multi-epitope vaccine (Deep-Vac-Pred). By combining the in-silico immunotherapeutic and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV- 2 spike protein sequence. We further use in-silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in-silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi- epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the CoV and make sure our designed vaccine can tackle the recent RNA mutations of the virus.

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