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Computational perspectives revealed prospective vaccine candidates from five structural proteins of novel SARS corona virus 2019 (SARS-CoV-2).

ABSTRACT: Background:The present pandemic COVID-19 is caused by SARS-CoV-2, a single-stranded positive-sense RNA virus from the Coronaviridae family. Due to a lack of antiviral drugs, vaccines against the virus are urgently required. Methods:In this study, validated computational approaches were used to identify peptide-based epitopes from six structural proteins having antigenic properties. The Net-CTL 1.2 tool was used for the prediction of CD8+ T-cell epitopes, while the robust tools Bepi-Pred 2 and LBtope was employed for the identification of linear B-cell epitopes. Docking studies of the identified epitopes were performed using HADDOCK 2.4 and the structures were visualized by Discovery Studio and LigPlot+. Antigenicity, immunogenicity, conservancy, population coverage and allergenicity of the predicted epitopes were determined by the bioinformatics tools like VaxiJen v2.0 server, the Immune Epitope Database tools and AllerTOP v.2.0, AllergenFP 1.0 and ElliPro. Results:The predicted T cell and linear B-cell epitopes were considered as prime vaccine targets in case they passed the requisite parameters like antigenicity, immunogenicity, conservancy, non-allergenicity and broad range of population coverage. Among the predicted CD8+ T cell epitopes, potential vaccine targets from surface glycoprotein were; YQPYRVVVL, PYRVVVLSF, GVYFASTEK, QLTPTWRVY, and those from ORF3a protein were LKKRWQLAL, HVTFFIYNK. Similarly, RFLYIIKLI, LTWICLLQF from membrane protein and three epitopes viz; SPRWYFYYL, TWLTYTGAI, KTFPPTEPK from nucleocapsid phosphoprotein were the superior vaccine targets observed in our study. The negative values of HADDOCK and Z scores obtained for the best cluster indicated the potential of the epitopes as suitable vaccine candidates. Analysis of the 3D and 2D interaction diagrams of best cluster produced by HADDOCK 2.4 displayed the binding interaction of leading T cell epitopes within the MHC-1 peptide binding clefts. On the other hand, among linear B cell epitopes the majority of potential vaccine targets were from nucleocapsid protein, viz; 59-HGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLS-105, 227-LNQLE SKMSGKGQQQQGQTVTKKSAAEASKKPRQKRTATK-266, 3-DNGPQNQRNAPRITFGGP-20, 29-GERSGARSKQRRPQGL-45. Two other prime vaccine targets, 370-NSASFSTFKCYGVSPTKLNDLCFTNV-395 and 260-AGAAAYYVGYLQPRT-274 were identified in the spike protein. The potential B-cell conformational epitopes were predicted on the basis of a higher protrusion index indicating greater solvent accessibility. These conformational epitopes were of various lengths and belonged to spike, ORF3a, membrane and nucleocapsid proteins. Conclusions:Taken together, eleven T cell epitopes, seven B cell linear epitopes and ten B cell conformational epitopes were identified from five structural proteins of SARS-CoV-2 using advanced computational tools. These potential vaccine candidates may provide important timely directives for an effective vaccine against SARS-CoV-2.

SUBMITTER: Anand R

PROVIDER: S-EPMC7531350 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Computational perspectives revealed prospective vaccine candidates from five structural proteins of novel SARS corona virus 2019 (SARS-CoV-2).

Anand Rajesh R Biswal Subham S Bhatt Renu R Tiwary Bhupendra N BN

PeerJ 20200929

<h4>Background</h4>The present pandemic COVID-19 is caused by SARS-CoV-2, a single-stranded positive-sense RNA virus from the <i>Coronaviridae</i> family. Due to a lack of antiviral drugs, vaccines against the virus are urgently required.<h4>Methods</h4>In this study, validated computational approaches were used to identify peptide-based epitopes from six structural proteins having antigenic properties. The Net-CTL 1.2 tool was used for the prediction of CD8<sup>+</sup> T-cell epitopes, while th ...[more]

PMID: 33062414

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Project description:Search for an effective and safe vaccine to prevent transmission of current pandemic is an unmet need. This study reviews and compares the available early phase clinical data of vaccine candidates which have reached phase 3 of clinical development. The latest update of "DRAFT landscape of coronavirus (CoV) disease 2019 candidate vaccines (October 2, 2020)" released by the World Health Organization was accessed to identify the potential vaccine candidates. The full text articles (published and/or preprint) of data of early clinical trials of the selected vaccines were accessed from the links provided in the same document, PubMed and/or medRxiv.com. After extraction and synthesis, the data were critically evaluated for the study efficacy and safety outcomes. Of the total 193 candidate vaccines 10 were found to reach phase 3 of the clinical development. Nine of these were included in the evaluation process. In all of the included studies, immunogenicity and serious adverse events/local or systemic adverse events/laboratory parameters abnormality was considered as efficacy and safety outcomes respectively. Immunogenicity response with most of the vaccines was either higher than or similar to the respective controls except one (recombinant adenovirus type 26 COV2 [Ad26.COV2.S]) for which it was less than that in control. Overall adverse events (related and/or unrelated) were more with vaccines than those with respective control(s) in three studies, in other two, these were similar whereas in one study, the events were less in the vaccine group than in control group and in the rest, data described were descriptive only without any mention for the same for the control. In conclusion all studies showed immunogenic response to target protein of severe acute respiratory syndrome CoV-2 and which was higher than the respective control except for Ad26.CoV2.S. Many of the vaccines caused more adverse events than the controls, however most were mild and transient and/or manageable.

Project description:BackgroundReverse vaccinology accelerates the discovery of potential vaccine candidates (PVCs) prior to experimental validation. Current programs typically use one bacterial proteome to identify PVCs through a filtering architecture using feature prediction programs or a machine learning approach. Filtering approaches may eliminate potential antigens based on limitations in the accuracy of prediction tools used. Machine learning approaches are heavily dependent on the selection of training datasets with experimentally validated antigens (positive control) and non-protective-antigens (negative control). The use of one or few bacterial proteomes does not assess PVC conservation among strains, an important feature of vaccine antigens.ResultsWe present ReVac, which implements both a panoply of feature prediction programs without filtering out proteins, and scoring of candidates based on predictions made on curated positive and negative control PVCs datasets. ReVac surveys several genomes assessing protein conservation, as well as DNA and protein repeats, which may result in variable expression of PVCs. ReVac's orthologous clustering of conserved genes, identifies core and dispensable genome components. This is useful for determining the degree of conservation of PVCs among the population of isolates for a given pathogen. Potential vaccine candidates are then prioritized based on conservation and overall feature-based scoring. We present the application of ReVac, applied to 69 Moraxella catarrhalis and 270 non-typeable Haemophilus influenzae genomes, prioritizing 64 and 29 proteins as PVCs, respectively.ConclusionReVac's use of a scoring scheme ranks PVCs for subsequent experimental testing. It employs a redundancy-based approach in its predictions of features using several prediction tools. The protein's features are collated, and each protein is ranked based on the scoring scheme. Multi-genome analyses performed in ReVac allow for a comprehensive overview of PVCs from a pan-genome perspective, as an essential pre-requisite for any bacterial subunit vaccine design. ReVac prioritized PVCs of two human respiratory pathogens, identifying both novel and previously validated PVCs.

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Computational perspectives revealed prospective vaccine candidates from five structural proteins of novel SARS corona virus 2019 (SARS-CoV-2).

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Computational perspectives revealed prospective vaccine candidates from five structural proteins of novel SARS corona virus 2019 (SARS-CoV-2).

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