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Conserved HLA binding peptides from five non-structural proteins of SARS-CoV-2-An in silico glance.

ABSTRACT: Coronavirus Disease 2019 (COVID-19) is a dangerous global threat that has no clinically approved treatment yet. Bioinformatics represent an outstanding approach to reveal key immunogenic regions in viral proteins. Here, five severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) non-structural proteins (NSPs) (NSP7, NSP8, NSP9, NSP12, and NSP13) were screened to identify potential human leukocyte antigen (HLA) binding peptides. These peptides showed robust viral antigenicity, immunogenicity, and a marked interaction with HLA alleles. Interestingly, several peptides showed affinity by HLA class I (HLA-I) alleles that commonly activates to natural killer (NK) cells. Notably, HLA biding peptides are conserved among SARS-CoV-2, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle Eastern respiratory syndrome coronavirus (MERS-CoV). Interestingly, HLA-I and HLA class II (HLA-II) binding peptides induced humoral and cell-mediated responses after in silico vaccination. These results may open further in vitro and in vivo investigations to develop novel therapeutic strategies against coronaviral infections.

SUBMITTER: Marchan J

PROVIDER: S-EPMC7425717 | biostudies-literature | 2020 Oct - Nov

REPOSITORIES: biostudies-literature

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Conserved HLA binding peptides from five non-structural proteins of SARS-CoV-2-An in silico glance.

Marchan Jose J

Human immunology 20200813 10-11

Coronavirus Disease 2019 (COVID-19) is a dangerous global threat that has no clinically approved treatment yet. Bioinformatics represent an outstanding approach to reveal key immunogenic regions in viral proteins. Here, five severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) non-structural proteins (NSPs) (NSP7, NSP8, NSP9, NSP12, and NSP13) were screened to identify potential human leukocyte antigen (HLA) binding peptides. These peptides showed robust viral antigenicity, immunogenicit ...[more]

PMID: 32888767

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Project description:The short time between the first cases of COVID-19 and the declaration of a pandemic initiated the search for ways to stop the spread of SARS-CoV-2. There are great expectations regarding the development of effective vaccines that protect against all variants, and in the search for it, we hypothesized the obtention of a predicted rational immunogenic peptide from structural components of SARS-CoV-2 might help the vaccine research direction. In the search for a candidate of an immunogenic peptide of the SARS-CoV-2 envelope (E), membrane (M), nucleocapsid (N), or spike (S) proteins, we access the predicted sequences of each protein after the genome sequenced worldwide. We obtained the consensus amino acid sequences of about 14,441 sequences of each protein of each continent and the worldwide consensus sequence. For epitope identification and characterization from each consensus structural protein related to MHC-I or MHC-II interaction and B-cell receptor recognition, we used the IEDB reaching 68 epitopes to E, 174 to M, 245 to N, and 833 to S proteins. To select an epitope with the highest probability of binding to the MHC or BCR, all epitopes of each consensus sequence were aligned. The curation indicated 1, 4, 8, and 21 selected epitopes for E, M, N, and S proteins, respectively. Those epitopes were tested in silico for antigenicity obtaining 16 antigenic epitopes. Physicochemical properties and allergenicity evaluation of the obtained epitopes were done. Ranking the results, we obtained one epitope of each protein except for the S protein that presented two epitopes after the selection. To check the 3D position of each selected epitope in the protein structure, we used molecular homology modeling. Afterward, each selected epitope was evaluated by molecular docking to reference MHC-I or MHC-II allelic protein sequences. Taken together, the results obtained in this study showed a rational search for a putative immunogenic peptide of SARS-CoV-2 structural proteins that can improve vaccine development using in silico approaches. The epitopes selected represent the most conserved sequence of new coronavirus and may be used in a variety of vaccine development strategies since they are also presented in the described variants of SARS-CoV-2.

Project description:BackgroundThe 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.MethodsIn 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.ResultsThe 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.ConclusionsTaken 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.

Dataset Information

Conserved HLA binding peptides from five non-structural proteins of SARS-CoV-2-An in silico glance.

Publications

Conserved HLA binding peptides from five non-structural proteins of SARS-CoV-2-An in silico glance.

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