scholarly journals In silico characterization of Echinococcus granulosus paramyosin nucleotide sequence for the development of epitope vaccine against cystic echinococcosis

2017 ◽  
Vol 54 (4) ◽  
pp. 275-283 ◽  
Author(s):  
Y.-J. Lu ◽  
D.-Sh. Chen ◽  
W.-T. Hao ◽  
H.-W. Xu ◽  
Y.-W. Zhang ◽  
...  
Keyword(s):  
Secondary Structure ◽  
B Cell ◽  
Echinococcus Granulosus ◽  
Cystic Echinococcosis ◽  
In Silico ◽  
Cytotoxic T Lymphocyte ◽  
Epitope Vaccine ◽  
B Cell Epitopes ◽  
Larval Stages ◽  
Linear B

Summary The paramyosin (Pmy) protein has been presented as a potential vaccine candidate against Schistosoma spp. However, it remains elusive whether it works in controlling cystic echinococcosis (CE), which is caused by the larval stages of Echinococcus granulosus (E. granulosus). This study investigated the characteristics of E. granulosus Pmy (EgPmy) using in silico analysis and evaluated its potential as an epitope vaccine. The secondary structure was predicted by SOPMA software and linear B-cell epitopes were screened by the Kolaskar and Tongaonkar’s method on IEBD while conformational B-cell epitopes were predicted by the Ellipro. Additionally, the epitopes of cytotoxic T lymphocyte (CTL) were analyzed by the NetCTL-1.2 server. The results showed that α-helices, extended strands, random coils and β-turns accounted for 84.82 %, 6.60 %, 5.56 % and 3.01 % in EgPmy’s secondary structure, respectively. A total of 29 linear B-cell epitopes and 6 conformational epitopes were identified together with 25 CTL epitopes. The CTL epitope 709KLEEAEAFA717 showed a high potential to elicit CTL response. These results suggested that EgPmy has a strong immunogenicity, which could serve as a reference for the development of EgPmy-based epitope vaccine against CE.

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.

A novel in silico minigene vaccine based on CD4 + T-helper and B-cell epitopes of EG95 isolates for vaccination against cystic echinococcosis

2018 ◽  
Vol 72 ◽  
pp. 150-163 ◽  
Author(s):  
Mohammad M. Pourseif ◽  
Gholamali Moghaddam ◽  
Behrouz Naghili ◽  
Nazli Saeedi ◽  
Sepideh Parvizpour ◽  
...  
Keyword(s):  
B Cell ◽  
Cystic Echinococcosis ◽  
In Silico ◽  
T Helper ◽  
B Cell Epitopes

scholarly journals Prediction of Multi peptides Vaccination from VP10,VP21, VP51 against Reverse Transcriptase Human immunodeficiency Viruses Using Immuno-informatics Approach

2019 ◽  
Author(s):  
Safa Ahmad Almostafa ◽  
Ienas Ibrahim mohmed ◽  
Habab Abd elmoneim Siddig ◽  
Sahar obi Abd albagi ◽  
Nadir Musa Khalil Abuzeid
Keyword(s):  
Reverse Transcriptase ◽  
B Cell ◽  
Mhc Class I ◽  
In Silico ◽  
Class Ii ◽  
Class I ◽  
Population Coverage ◽  
B Cell Epitopes ◽  
Surface Accessibility ◽  
Linear B

AbstractThe human immunodeficiency virus-(HIV) is group of the genus Lentivirus within the family of Retroviridae, subfamily Ortho retrovirinae. Based on genetic characteristics and differences in the viral antigens, HIV is classified into the types 1 and 2 (HIV-1, HIV-2). HIV is identical single – stranded RNA molecule that are enclosed within the core of the virus particle proteins, the genome of the HIV Provirus, also known as DNA, is generated by the Protease against reverse transcriptase RNA genome into DNA, degradation of the RNA and integration of the double – stranded HIV DNA into the human genome. The aim of this study is to determine antigenic peptides from p10, p21, and p51 proteins that can be used for multiple peptide vaccine design using In-Silico study. A total of 73 sequences of three proteins were obtained from NCBI and subjected to multiple sequence alignments using CLUSTALW tool to determine conserved regions.Immune Epitope Data Base tools were used to determine B cell epitopes, these tools are Bepipred Linear B cell epitopes prediction, surface accessibility and antigenicity prediction. Epitope binding to MHC class I and class II and their population coverage were also determined using IEDB software. The analysis results are as follow, for B cell binding from p10 (708QGYSP712), from p21 (704QGYSP708, 73CVPTDPNPQ81) and (346“FKNL349) from p51. All these peptides have high score in Linear B cell epitopes prediction, surface accessibility and antigenicity prediction. On another hand peptides that reacted to MHC class I were (47EANTTLFCA55, 53FCASDAKAY61, 55,ASDAKAYET63) form p10,(38YYGVPVWKE46, 10PQEVFLVNV18 and 29AAGSTMGAA37) from p21 and (63“EWEFVNTPP71, 70PPLVKLWYQ78 and 79EKEPIVGA87) from p51 protein respectively. It worth noting that the peptides (119IISLWDQSL127,108CVKLTPLCV116) from p10, (38YYGVPVWKE46, 20LLQYWSQEL34, 16FNMWKNNMV30) from p21 and (7WKGSPAIFQ21, 11WEFVNTPPL25 and 58 FLWMGYELH72) protein is also binds to MHC class II with high affinity. All T cell peptides had highest population coverage, and the combined coverage for all peptides in this study was found to be 100%. Using In-Silico studies will ensure less risk of virulence and side effects. Evaluation of antibodies response in animal models is needed to confirm efficacy of these epitopes in inducing protective immune response.

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.

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

2021 ◽  
Vol 11 (1) ◽  
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

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

scholarly journals Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2

2020 ◽  
Author(s):  
Souvik Banerjee ◽  
Kaustav Majumder ◽  
Gerardo Jose Gutierrez ◽  
Debkishore Gupta ◽  
Bharti Mittal
Keyword(s):  
Immune Response ◽  
T Cell ◽  
B Cell ◽  
In Silico ◽  
Dynamics Simulation ◽  
Cytotoxic T Cell ◽  
Epitope Vaccine ◽  
B Cell Epitopes ◽  
Corona Virus ◽  
The World

AbstractThe novel Corona Virus Disease 2019 (COVID-19) pandemic has set the fatality rates ablaze across the world. So, to combat this disease, we have designed a multi-epitope vaccine from various proteins of Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) with an immuno-informatics approach, validated in silico to be stable, non-allergic and antigenic. Cytotoxic T-cell, helper T-cell, and B-cell epitopes were computationally predicted from six conserved protein sequences among four viral strains isolated across the world. The T-cell epitopes, overlapping with the B-cell epitopes, were included in the vaccine construct to assure the humoral and cell-mediated immune response. The beta-subunit of cholera toxin was added as an adjuvant at the N-terminal of the construct to increase immunogenicity. Interferon-gamma inducing epitopes were even predicted in the vaccine. Molecular docking and binding energetics studies revealed strong interactions of the vaccine with immune-stimulatory toll-like receptors (TLR) −2, 3, 4. Molecular dynamics simulation of the vaccine ensured in vivo stability in the biological system. The immune simulation of vaccine evinced elevated immune response. The efficient translation of the vaccine in an expression vector was assured utilizing in silico cloning approach. Certainly, such a vaccine construct could reliably be effective against COVID-19.

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 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.

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