{"title":"Prediction of suitable T and B cell epitopes for eliciting immunogenic response against SARS-CoV-2 and its mutant.","authors":"Vidhu Agarwal, Akhilesh Tiwari, Pritish Varadwaj","doi":"10.1007/s13721-021-00348-w","DOIUrl":null,"url":null,"abstract":"<p><p>Spike glycoprotein of SARS-CoV-2 is mainly responsible for the recognition and membrane fusion within the host and this protein has an ability to mutate. Hence, T cell and B cell epitopes were derived from the spike glycoprotein sequence of wild SARS-CoV-2. The proposed T cell and B cell epitopes were found to be antigenic and conserved in the sequence of SARS-CoV-2 mutant (B.1.1.7). Thus, the proposed epitopes are effective against SARS-CoV-2 and its B.1.1.7 mutant. MHC-I that best interacts with the proposed T cell epitopes were found, using immune epitope database. Molecular docking and molecular dynamic simulations were done for ensuring a good binding between the proposed MHC-I and T cell epitopes. The finally proposed T cell epitope was found to be antigenic, non-allergenic, non-toxic and stable. Further, the finally proposed B cell epitopes were also found to be antigenic. The population conservation analysis has ensured the presence of MHC-I molecule (respective to the finally proposed T cell) in human population of most affected countries with SARS-CoV-2. Thus the proposed T and B cell epitope could be effective in designing an epitope-based vaccine, which is effective on SARS-CoV-2 and its B.1.1.7mutant.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13721-021-00348-w.</p>","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":" ","pages":"1"},"PeriodicalIF":2.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8619655/pdf/","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Network Modeling and Analysis in Health Informatics and Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13721-021-00348-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/11/26 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 7
Abstract
Spike glycoprotein of SARS-CoV-2 is mainly responsible for the recognition and membrane fusion within the host and this protein has an ability to mutate. Hence, T cell and B cell epitopes were derived from the spike glycoprotein sequence of wild SARS-CoV-2. The proposed T cell and B cell epitopes were found to be antigenic and conserved in the sequence of SARS-CoV-2 mutant (B.1.1.7). Thus, the proposed epitopes are effective against SARS-CoV-2 and its B.1.1.7 mutant. MHC-I that best interacts with the proposed T cell epitopes were found, using immune epitope database. Molecular docking and molecular dynamic simulations were done for ensuring a good binding between the proposed MHC-I and T cell epitopes. The finally proposed T cell epitope was found to be antigenic, non-allergenic, non-toxic and stable. Further, the finally proposed B cell epitopes were also found to be antigenic. The population conservation analysis has ensured the presence of MHC-I molecule (respective to the finally proposed T cell) in human population of most affected countries with SARS-CoV-2. Thus the proposed T and B cell epitope could be effective in designing an epitope-based vaccine, which is effective on SARS-CoV-2 and its B.1.1.7mutant.
Supplementary information: The online version contains supplementary material available at 10.1007/s13721-021-00348-w.
期刊介绍:
NetMAHIB publishes original research articles and reviews reporting how graph theory, statistics, linear algebra and machine learning techniques can be effectively used for modelling and analysis in health informatics and bioinformatics. It aims at creating a synergy between these disciplines by providing a forum for disseminating the latest developments and research findings; hence, results can be shared with readers across institutions, governments, researchers, students, and the industry. The journal emphasizes fundamental contributions on new methodologies, discoveries and techniques that have general applicability and which form the basis for network based modelling, knowledge discovery, knowledge sharing and decision support to the benefit of patients, healthcare professionals and society in traditional and advanced emerging settings, including eHealth and mHealth .