Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design

Kazi, Ada; Chuah, Candy; Majeed, Abu Bakar Abdul; Leow, Chiuan Herng; Lim, Boon Huat

doi:10.1080/20477724.2018.1446773

Cited by 84 publications

(60 citation statements)

References 79 publications

(79 reference statements)

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“…The most immunogenic epitope for the T and B cells involved in cell-mediated immunity was previously analyzed [18]. The main focus of the analysis was the MHC class I potential peptides using in silico analysis techniques [19,20]. In this study, the same techniques were applied to keep MHC classes I and II along with the world population coverage as our main focus.…”

Section: Introductionmentioning

confidence: 99%

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

Mohammed

Shantier

Mustafa

et al. 2020

Journal of Immunology Research

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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.

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Section: Introductionmentioning

confidence: 99%

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

Mohammed

Shantier

Mustafa

et al. 2020

Journal of Immunology Research

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“…The main focus of the analysis was the MHC class-I potential peptides using in silico analysis techniques [19,20] . In this study, the same techniques were applied to keep MHC class I and II along with the world population coverage as our main focus.…”

Section: Introductionmentioning

confidence: 99%

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

Mohammed

Shantier

Mustafa

et al. 2019

Preprint

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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.

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“…For example, our results indicated that nine potential antigen epitopes were predicted in GRA12 by BCPREDS. Also, Bcepred's prediction accuracy for models based on different properties ranges from 52.92% to 57.53% and this server permits users to predict epitopes of B-cell using any of the physico-chemical properties (accessibility, hydrophilicity, polarity, flexibility/mobility, exposed surface, and turns) [39]. In addition, the online server of ABCpred predicts B-cell epitopes in an antigen sequence using ANN.…”

Section: Discussionmentioning

confidence: 99%

Antigenic properties of dense granule antigen 12 protein using bioinformatics tools in order to improve vaccine design againstToxoplasma gondii

Ghaffari

Dalimi

Ghaffarifar

et al. 2020

Clin Exp Vaccine Res

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Purpose: Toxoplasma gondii is an opportunistic parasite infecting all warm-blooded animals including humans. The dense granule antigens (GRAs) play an important role in parasite survival and virulence and in forming the parasitophorous vacuole. Identification of protein characteristics increases our knowledge about them and leads to develop the vaccine and diagnostic studies. Materials and Methods: This paper gave a comprehensive definition of the important aspects of GRA12 protein, including physico-chemical features, a transmembrane domain, subcellular position, secondary and tertiary structure, potential epitopes of B-cells and T-cells, and other important features of this protein using different and reliable bioinformatics methods to determine potential epitopes for designing of a high-efficient vaccine. Results: The findings showed that GRA12 protein had 53 potential post-translational modification sites. Also, only one transmembrane domain was recognized for this protein. The secondary structure of GRA12 protein comprises 35.55% alpha-helix, 19.50% extended strand, and 44.95% random coil. Moreover, several potential Band T-cell epitopes were identified for GRA12. Based on the results of the Ramachandran plot, 79.26% of amino acid residues were located in favored, 11.85% in allowed and 8.89% in outlier regions. Furthermore, the results of the antigenicity and allergenicity assessment noted that GRA12 is immunogenic and nonallergenic. Conclusion: This research provided important basic and conceptual data on GRA12 to develop an effective vaccine against acute and chronic toxoplasmosis for further in vivo investigations. More studies are required on vaccine development using the GRA12 alone or combined with other antigens in the future.

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Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design

Cited by 84 publications

References 79 publications

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

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

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

Antigenic properties of dense granule antigen 12 protein using bioinformatics tools in order to improve vaccine design againstToxoplasma gondii

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