scholarly journals Epitope - based peptide vaccine against glycoprotein GPC precursor of Lujo virus using immunoinformatics approaches

2020 ◽  
Author(s):  
Arwa A. Mohammed ◽  
Mayada E. Elkhalifa ◽  
Khadija E. Elamin ◽  
Rawan A. Mohammed ◽  
Musab E. Ibrahim ◽  
...  
Keyword(s):  
B Cell ◽  
Mhc Class Ii ◽  
Peptide Vaccine ◽  
Cell Epitope ◽  
Hemorrhagic Fever ◽  
Population Coverage ◽  
B Cell Epitope ◽  
Cub Domain ◽  
Autodock 4.0

AbstractBackgroundLujo virus (LUJV) is a highly fatal human pathogen belonging to the Arenaviridae family. Lujo virus causes viral hemorrhagic fever (VHF). An In silico molecular docking was performed on the GPC domain of Lujo virus in complex with the first CUB domain of neuropilin-2.The aim of this study is to predict effective epitope-based vaccine against glycoprotein GPC precursor of Lujo virus using immunoinformatics approaches.Methods and Materialsglycoprotein GPC precursor of Lujo virus Sequence was retrieved from NCBI. Different prediction tools were used to analyze the nominee’s epitopes in BepiPred-2.0: Sequential B-Cell Epitope Predictor for B-cell, T-cell MHC class II & I. Then the proposed peptides were docked using Autodock 4.0 software program.Results and ConclusionsThe proposed and promising peptides FWYLNHTKL and YMFSVTLCI shows a very strong binding affinity to MHC class I & II alleles with high population coverage for the world, South Africa and Sudan. This indicates a strong potential to formulate a new vaccine, especially with the peptide YMFSVTLCI which is likely to be the first proposed epitope-based vaccine against glycoprotein GPC of Lujo virus. This study recommends an in-vivo assessment for the most promising peptides especially FWYLNHTKL, YMFSVTLCI and LPCPKPHRLR.

scholarly journals Epitope-Based Peptide Vaccine against Glycoprotein G of Nipah Henipavirus Using Immunoinformatics Approaches

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Arwa A. Mohammed ◽  
Shaza W. Shantier ◽  
Mujahed I. Mustafa ◽  
Hind K. Osman ◽  
Hashim E. Elmansi ◽  
...  
Keyword(s):  
B Cell ◽  
Mhc Class Ii ◽  
Nipah Virus ◽  
Peptide Vaccine ◽  
Cell Epitope ◽  
Asymptomatic Infection ◽  
Healthcare Facilities ◽  
Glycoprotein G ◽  
Autodock 4.0

Background. Nipah belongs to the genus Henipavirus and the Paramyxoviridae family. It is an endemic most commonly found at South Asia and has first emerged in Malaysia in 1998. Bats are found to be the main reservoir for this virus, causing disease in both humans and animals. The last outbreak has occurred in May 2018 in Kerala. It is characterized by high pathogenicity and fatality rates which varies from 40% to 70% depending on the severity of the disease and on the availability of adequate healthcare facilities. Currently, there are no antiviral drugs available for NiV disease and the treatment is just supportive. Clinical presentations for this virus range from asymptomatic infection to fatal encephalitis. Objective. This study is aimed at predicting an effective epitope-based vaccine against glycoprotein G of Nipah henipavirus, using immunoinformatics approaches. Methods and Materials. Glycoprotein G of the Nipah virus sequence was retrieved from NCBI. Different prediction tools were used to analyze the epitopes, namely, BepiPred-2.0: Sequential B Cell Epitope Predictor for B cell and T cell MHC classes II and I. Then, the proposed peptides were docked using Autodock 4.0 software program. Results and Conclusions. The two peptides TVYHCSAVY and FLIDRINWI have showed a very strong binding affinity to MHC class I and MHC class II alleles. Furthermore, considering the conservancy, the affinity, and the population coverage, the peptide FLIDRINWIT is highly suitable to be utilized to formulate a new vaccine against glycoprotein G of Nipah henipavirus. An in vivo study for the proposed peptides is also highly recommended.

scholarly journals An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-COV-2

2020 ◽  
Author(s):  
Renu Jakhar ◽  
S.K Gakhar
Keyword(s):  
B Cell ◽  
Envelope Protein ◽  
Peptide Vaccine ◽  
Cell Epitope ◽  
T Helper ◽  
Ctl Epitopes ◽  
The People ◽  
B Cell Epitope

AbstractCOVID-19 is a new viral emergent human disease caused by a novel strain of Coronavirus. This virus has caused a huge problem in the world as millions of the people are affected with this disease in the entire world. We aimed to design a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system and can be used later to create a new peptide vaccine that could replace conventional vaccines. A total of available 370 sequences of SARS-CoV-2 were retrieved from NCBI for bioinformatics analysis using Immune Epitope Data Base (IEDB) to predict B and T cells epitopes. Then we docked the best predicted CTL epitopes with HLA alleles. CTL cell epitopes namely interacted with MHC class I alleles and we suggested them to become universal peptides based vaccine against COVID-19. Potentially continuous B cell epitopes were predicted using tools from IEDB. The Allergenicity of predicted epitopes was analyzed by AllerTOP tool and the coverage was determined throughout the worlds. We found these CTL epitopes to be T helper epitopes also. The B cell epitope, SRVKNL and T cell epitope, FLAFVVFLL were suggested to become a universal candidate for peptide-based vaccine against COVID-19. We hope to confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted candidate.

scholarly journals Epitope - based peptide vaccine against glycoprotein G of Nipah henipavirus using immunoinformatics approaches

2019 ◽  
Author(s):  
Arwa A. Mohammed ◽  
Shaza W. Shantier ◽  
Mujahed I. Mustafa ◽  
Hind K. Osman ◽  
Hashim E. Elmansi ◽  
...  
Keyword(s):  
B Cell ◽  
Binding Affinity ◽  
Mhc Class Ii ◽  
Peptide Vaccine ◽  
Cell Epitope ◽  
Case Fatality ◽  
Mhc I ◽  
Strong Binding ◽  
Glycoprotein G

AbstractBackgroundNipah virus (NiV) is a member of the genus Henipavirus of the family Paramyxoviridae, characterized by high pathogenicity and endemic in South Asia, first emerged in Malaysia in 1998. The case-fatality varies from 40% to 70% depending on the severity of the disease and on the availability of adequate healthcare facilities. At present no antiviral drugs are available for NiV disease and the treatment is just supportive. Clinical presentation ranges from asymptomatic infection to fatal encephalitis. Bats are the main reservoir for this virus, which can cause disease in humans and animals. The last investigated NiV outbreak has occurred in May 2018 in Kerala.ObjectiveThis study aims to predict effective epitope-based vaccine against glycoprotein G of Nipah henipavirus using immunoinformatics approaches.Methods and MaterialsGlycoprotein G of Nipah henipavirus sequence was retrieved from NCBI. Different prediction tools were used to analyze the nominee’s epitopes in BepiPred-2.0: Sequential B-Cell Epitope Predictor for B-cell, T-cell MHC class II & I. Then the proposed peptides were docked using Autodock 4.0 software program.Results and ConclusionsPeptide TVYHCSAVY shows a very strong binding affinity to MHC I alleles while FLIDRINWI shows a very strong binding affinity to MHC II and MHC I alleles. This indicates a strong potential to formulate a new vaccine, especially with the peptide FLIDRINWI that is likely to be the first proposed epitope-based vaccine against glycoprotein G of Nipah henipavirus. This study recommends an in-vivo assessment for the most promising peptides especially FLIDRINWI.

scholarly journals IgG-Mediated Anaphylaxis to a Synthetic Long Peptide Vaccine Containing a B Cell Epitope Can Be Avoided by Slow-Release Formulation

2014 ◽  
Vol 192 (12) ◽  
pp. 5813-5820 ◽  
Author(s):  
Esther D. Quakkelaar ◽  
Marieke F. Fransen ◽  
Wendy W. C. van Maren ◽  
Joost Vaneman ◽  
Nikki M. Loof ◽  
...  
Keyword(s):  
B Cell ◽  
Slow Release ◽  
Peptide Vaccine ◽  
Cell Epitope ◽  
Release Formulation ◽  
Slow Release Formulation ◽  
B Cell Epitope

scholarly journals Epitope - based peptide vaccine againstFructose-bisphosphate aldolase (FBA)ofMadurella mycetomatisusing immunoinformatics approaches

2018 ◽  
Author(s):  
Arwa A. Mohammed ◽  
Ayman M. H. ALnaby ◽  
Solima M. Sabeel ◽  
Fagr M. AbdElmarouf ◽  
Amina I. Dirar ◽  
...  
Keyword(s):  
B Cell ◽  
Binding Affinity ◽  
Mhc Class Ii ◽  
Bacterial Infections ◽  
Peptide Vaccine ◽  
Fructose Bisphosphate ◽  
Strong Binding ◽  
Fructose Bisphosphate Aldolase ◽  
Madurella Mycetomatis

AbstractBackgroundMycetoma is a distinct flesh eating and destructive neglected tropical disease. It is endemic in many tropical and subtropical countries. Mycetoma is caused by bacterial infections (actinomycetoma) such as Streptomyces somaliensis and Nocardiae or true fungi (eumycetoma) such as Madurella mycetomatis. Until date, treatments fail to cure the infection and the available marketed drugs are expensive and toxic upon prolonged usage. Moreover, no vaccine was prepared yet against mycetoma.The aimof this study is to predict effective epitope-based vaccine against fructose-bisphosphate aldolase enzymes of M. mycetomatis using immunoinformatics approaches.Methods and MaterialsFructose-bisphosphate aldolase ofMadurella mycetomatisSequence was retrieved from NCBI. Different prediction tools were used to analyze the nominee’s epitopes in Immune Epitope Database for B-cell, T-cell MHC class II & I. Then the proposed peptides were docked using Autodock 4.0 software program.Results and ConclusionsThe proposed and promising peptides KYLQ shows a potent binding affinity to B-cell, FEYARKHAF with a very strong binding affinity to MHC1 alleles and FFKEHGVPL that show a very strong binding affinity to MHC11and MHC1 alleles. This indicates a strong potential to formulate a new vaccine, especially with the peptide FFKEHGVPL which is likely to be the first proposed epitope-based vaccine against Fructose-bisphosphate aldolase of Madurella mycetomatis. This study recommends an in-vivo assessment for the most promising peptides especially FFKEHGVPL.

scholarly journals Conserved B-cell epitope identification of envelope glycoprotein (GP120) HIV-1 to develop multi-strain vaccine candidate through bioinformatics approach

2021 ◽  
Vol 10 (1) ◽  
pp. 06-13
Author(s):  
Viol Dhea Kharisma ◽  
Arif Nur Muhammad Ansori ◽  
Gabrielle Ann Villar Posa ◽  
Wahyu Choirur Rizky ◽  
Sofy Permana ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Activation ◽  
Immune Cell ◽  
Vaccine Candidate ◽  
Peptide Vaccine ◽  
Cell Epitope ◽  
Vaccine Design ◽  
B Cell Epitope ◽  
Conserved Region ◽  
Hiv 1

Acquired immune deficiency syndrome (AIDS) has been identified from US patients since 1981. AIDS is caused by infection with the human immunodeficiency virus type 1 (HIV-1) which is a retrovirus. HIV-1 gp120 can be recognized by the immune system because it is located outside the virion. The conserved region is identified in gp120, and it is recognized by an immune cell which then initiates specific immune responses, viral mutation escape, and increase vaccine protection coverage, a benefit derived from the conserved region-based vaccine design. However, previous researchers have little knowledge on this conserved region as a target for vaccine design. This paper explains how the conserved region of gp120 HIV-1 is a major target for vaccine design through a bioinformatics approach. The conserved region from gp120 was explored as a vaccine design target with a bioinformatics tool that consists of B-cell epitope mapping, vaccine properties, molecular docking, and dynamic simulation. The peptide vaccine candidate of B5 with the gp120 HIV-1 conserved region was found to provoke B-cell activation through a direct pathway, produce specific antibody, and increase protection from multi-strain viral infection.

scholarly journals Potential immune epitope map for structural proteins of SARS-CoV-2

2020 ◽  
Author(s):  
Yengkhom Damayanti Devi ◽  
Himanshu Ballav Goswami ◽  
Sushmita Konwar ◽  
Chandrima Doley ◽  
Anutee Dolley ◽  
...  
Keyword(s):  
T Cell ◽  
B Cell ◽  
Mhc Class I ◽  
Mhc Class Ii ◽  
Cell Epitope ◽  
Class I ◽  
Structural Proteins ◽  
S Protein ◽  
N Protein ◽  
B Cell Epitope

Abstract Researchers around the world are developing more than 145 vaccines (DNA/mRNA/whole-virus/viral-vector/protein-based/repurposed vaccine) against the SARS-CoV-2 and 21 vaccines are in human trials. However, a limited information is available about which SARS-CoV-2 proteins are recognized by human B- and T-cell immune responses. Using a comprehensive computational prediction algorithm and stringent selection criteria, we have predicted and identified potent B- and T-cell epitopes in the structural proteins of SARS-CoV and SARS-CoV-2. The amino acid residues spanning the predicted linear B-cell epitope in the RBD of S protein (370-NSASFSTFKCYGVSPTKLNDLCFTNV-395) have recently been identified for interaction with the CR3022, a previously described neutralizing antibody known to neutralize SARS-CoV-2 through binding to the RBD of the S protein. Intriguingly, most of the amino acid residues spanning the predicted B-cell epitope (aa 331-NITNLCPFGEVFNATRFASVYAWNRK-356, 403-RGDEVRQIAPGQTGKIADYNYKLPD-427 and aa 437- NSNNLDSKVGGNYNYLYRLFRKSNL-461) of the S protein have been experimentally verified to interact with the cross-neutralizing mAbs (S309 and CB6) in an ACE2 receptor-S protein interaction independent-manner. In addition, we found that computationally predicted epitope of S protein (370-395) is likely to function as both linear B-cell and MHC class II epitope. Similarly, 403-27 and 437-461 peptides of S protein were predicted as linear B cell and MHC class I epitope while, 177-196 and 1253-1273 peptides of S protein were predicted as linear and conformational B cell epitope. We found MHC class I epitope 316-GMSRIGMEV-324 predicted as high affinity epitope (HLA-A*02:03, HLA-A*02:01, HLA-A*02:06) common to N protein of both SARS-CoV-2 and SARS-CoV (N317-325) was previously shown to induce interferon-gamma (IFN-γ) in PBMCs of SARS-recovered patients. Interestingly, two MHC class I epitopes, 1041-GVVFLHVTY-1049 (HLA-A*11:01, HLA-A*68:01, HLA-A*03:01) and 1202-FIAGLIAIV-1210 (HLA-A*02:06, HLA-A*68:02) derived from SARS-CoV S protein with epitope conservancy between 85 to 100% with S protein of SARS-CoV-2 was experimentally verified using PBMCs derived from SARS-CoV patients. We observed that HLA-A*02:01, HLA-A*02:03, HLA-A*02:06, HLA-A*11:01, HLA-A*30:01, HLA-A*68:01, HLA-A*68:02, HLA-B*15:01 and HLA-B*35:01 have been predicted to bind to the maximum number of MHC class I epitope (based on the criterion of allele predicted to bind more than 30 epitopes) of S protein of SARS-CoV-2. Similarly, we observed that HLA-A*02:06, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*68:01, HLA-A*68:02, HLA-B*15:01 and HLA-B*35:01 are predicted to bind to the maximum number of MHC class I epitope of N protein of SARS-CoV-2. We found that HLA-DRB1*04:01, HLA-DRB1*04:05, HLA-DRB1*13:02, HLA-DRB1*15:01, HLA-DRB3*01:01, HLA-DRB3*02:02, HLA-DRB4*01:01, HLA-DRB5*01:01, HLA-DQA1*04:01, DQB1*04:02, HLA-DPA1*02:01, DPB1*01:01, HLA-DPA1*01:03, DPB1*02:01, HLA-DPA1*01:03, DPB1*04:01, HLA-DPA1*03:01, DPB1*04:02, HLA-DPA1*02:01, DPB1*05:01, HLA-DPA1*02:01, and DPB1*14:01 are predicted to bind to the maximum number of MHC class II epitope of S protein of SARS-CoV-2. Alleles such as HLA-DRB1*04:01, HLA-DRB1*07:01, HLA-DRB1*08:02, HLA-DRB1*09:01, HLA-DRB1*11:01, HLA-DRB1*13:02, HLA-DRB3*02:02, HLA-DRB5*01:01, HLA-DQA1*01:02, DQB1*06:02, DPB1*05:01 and HLA-DPA1*02:01 are found to interact with the maximum number of MHC class II epitope of N protein of SARS-CoV-2. Using the IEDB tool we found the occurrence of HLA alleles with population coverage of around 99% throughout the world. The findings of computational predictions of mega-pool of B- and T-cell epitopes identified in the four main structural proteins of SARS-CoV-2 provides a platform for future experimental validations and the results of present works support the use of RBD or the full-length S and N proteins in an effort towards designing of recombinant protein-based vaccine and a serological diagnostic assay for SARS-CoV-2.

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