Prediction of suitable T and B cell epitopes for eliciting immunogenic response against SARS-CoV-2 and its mutant.

IF 2 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Vidhu Agarwal, Akhilesh Tiwari, Pritish Varadwaj
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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.

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预测诱导对SARS-CoV-2及其突变体免疫原性应答的合适T和B细胞表位
SARS-CoV-2的刺突糖蛋白主要负责宿主内的识别和膜融合,该蛋白具有突变能力。因此,T细胞和B细胞表位来源于野生SARS-CoV-2的刺突糖蛋白序列。所提出的T细胞和B细胞表位在SARS-CoV-2突变体序列中具有抗原性和保守性(B.1.1.7)。因此,所提出的表位对SARS-CoV-2及其B.1.1.7突变体有效。利用免疫表位数据库,发现了与所提出的T细胞表位相互作用最好的MHC-I。为了确保所提出的MHC-I和T细胞表位之间的良好结合,进行了分子对接和分子动力学模拟。最后提出的T细胞表位具有抗原性、非致敏性、无毒性和稳定性。此外,最后提出的B细胞表位也被发现具有抗原性。种群保护分析已确保在SARS-CoV-2感染最严重国家的人群中存在mhc - 1分子(相对于最终提出的T细胞)。因此,所提出的T和B细胞表位可以有效地设计基于表位的疫苗,该疫苗对SARS-CoV-2及其B.1.1.7突变体有效。补充信息:在线版本包含补充资料,提供地址:10.1007/s13721-021-00348-w。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.40
自引率
4.30%
发文量
43
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 .
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