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Bioinformatics-based prediction of conformational epitopes for human parechovirus.

ABSTRACT: Human parechoviruses (HPeVs) are human pathogens that usually cause diseases ranging from rash to neonatal sepsis in young children. HPeV1 and HPeV3 are the most frequently reported genotypes and their three-dimensional structures have been determined. However, there is a lack of systematic research on the antigenic epitopes of HPeVs, which are useful for understanding virus-receptor interactions, developing antiviral agents or molecular diagnostic tools, and monitoring antigenic evolution. Thus, we systematically predicted and compared the conformational epitopes of HPeV1 and HPeV3 using bioinformatics methods in the study. The results showed that both epitopes clustered into three sites (sites 1, 2 and 3). Site 1 was located on the "northern rim" near the fivefold vertex; site 2 was on the "puff"; and site 3 was divided into two parts, of which one was located on the "knob" and the other was close to the threefold vertex. The predicted epitopes highly overlapped with the reported antigenic epitopes, which indicated that the prediction results were accurate. Although the distribution positions of the epitopes of HPeV1 and HPeV3 were highly consistent, the residues varied largely and determined the genotypes. Three amino acid residues, VP3-91N, -92H and VP0-257S, were the key residues for monoclonal antibody (mAb) AM28 binding to HPeV1 and were also of great significance in distinguishing HPeV1 and HPeV3. We also found that two residues, VP1-85N and -87D, might affect the capability of mAb AT12-015 to bind to HPeV3.

SUBMITTER: Rong H

PROVIDER: S-EPMC8016246 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2.MethodsWe leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8+ T cell responses.ResultsWe first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8+ T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity.ConclusionsUsing our bioinformatics platform, we identify multiple putative epitopes that are potential targets for CD4+ and CD8+ T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8+ T cell responses from multiple SARS-CoV-2 proteins. Protein expression and population HLA allele coverage, combined with the ability to identify T cell epitopes, should be considered in SARS-CoV-2 vaccine design strategies and immune monitoring.

Project description:BACKGROUND: A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow. RESULTS: We prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen. CONCLUSIONS: Our method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at http://cekeg.cs.ntou.edu.tw.

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Bioinformatics-based prediction of conformational epitopes for human parechovirus.

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