Yasmin Hisham1, Yaqoub Ashhab2, Sang-Hyun Hwang3, Dong-Eun Kim1. 1. Department of Bioscience and Biotechnology, Konkuk University, Seoul 05029, Korea. 2. Palestine-Korea Biotechnology Center, Palestine Polytechnic University, Hebron 90100, Palestine. 3. Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.
Abstract
One of the most effective strategies for eliminating new and emerging infectious diseases is effective immunization. The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) warrants the need for a maximum coverage vaccine. Moreover, mutations that arise within the virus have a significant impact on the vaccination strategy. Here, we built a comprehensive in silico workflow pipeline to identify B-cell- and T-cell-stimulating antigens of SARS-CoV-2 viral proteins. Our in silico reverse vaccinology (RV) approach consisted of two parts: (1) analysis of the selected viral proteins based on annotated cellular location, antigenicity, allele coverage, epitope density, and mutation density and (2) analysis of the various aspects of the epitopes, including antigenicity, allele coverage, IFN-γ induction, toxicity, host homology, and site mutational density. After performing a mutation analysis based on the contemporary mutational amino acid substitutions observed in the viral variants, 13 potential epitopes were selected as subunit vaccine candidates. Despite mutational amino acid substitutions, most epitope sequences were predicted to retain immunogenicity without toxicity and host homology. Our RV approach using an in silico pipeline may potentially reduce the time required for effective vaccine development and can be applicable for vaccine development for other pathogenic diseases as well.
One of the most effective strategies for elin class="Gene">minating new and emerging infectious diseases is effective immunization. The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) warrants the need for a maximumcoverage vaccine. Moreover, mutations that arise within the virus have a significant impact on the vaccination strategy. Here, we built a comprehensive in silico workflow pipeline to identify B-cell- and T-cell-stimulating antigens of SARS-CoV-2 viral proteins. Our in silico reverse vaccinology (RV) approach consisted of two parts: (1) analysis of the selected viral proteins based on annotated cellular location, antigenicity, allele coverage, epitope density, and mutation density and (2) analysis of the various aspects of the epitopes, including antigenicity, allele coverage, IFN-γ induction, toxicity, host homology, and site mutational density. After performing a mutation analysis based on the contemporary mutational amino acid substitutions observed in the viral variants, 13 potential epitopes were selected as subunit vaccine candidates. Despite mutational amino acid substitutions, most epitope sequences were predicted to retain immunogenicity without toxicity and host homology. Our RV approach using an in silico pipeline may potentially reduce the time required for effective vaccine development and can be applicable for vaccine development for other pathogenic diseases as well.
Authors: Leonardo Pereira de Araújo; Maria Eduarda Carvalho Dias; Gislaine Cristina Scodeler; Ana de Souza Santos; Letícia Martins Soares; Patrícia Paiva Corsetti; Ana Carolina Barbosa Padovan; Nelson José de Freitas Silveira; Leonardo Augusto de Almeida Journal: Immunoinformatics (Amst) Date: 2022-06-11