SARS-CoV-2抗原表位预测的免疫信息学方法。

IF 3.6 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Nourelislam Awad, Rania Hassan Mohamed, Nehal I Ghoneim, Ahmed O Elmehrath, Nagwa El-Badri
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引用次数: 3

摘要

背景:新型冠状病毒(SARS-CoV-2)在全球范围内造成了前所未有的致命感染。候选病毒表位的鉴定是设计抗病毒感染疫苗的第一步。采用几种免疫信息学方法鉴定了SARS-CoV-2与主要组织相容性分子I类(MHC-I)特异性结合的表位。我们利用免疫信息学工具分析整个病毒蛋白序列,以鉴定埃及人群中负责与最常见的人类白细胞抗原(HLA)等位基因结合的SARS-CoV-2表位。这些等位基因在世界上其他人群中也有很高的频率。结果:分子对接方法显示,使用MHC-I与T细胞受体(TCR)共结晶而不是仅使用MHC-I结构,可显著提高对接评分,稳定鉴定的SARS-CoV-2表位的构象和结合亲和力。我们的方法根据可用的SARS-CoV-2刺突和ORF1ab蛋白序列直接预测7个潜在的疫苗亚基。这一预测已被发表的实验验证和在计算机上预测的刺突表位证实。另一方面,我们预测新的表位(RDLPQGFSA和FCLEASFNY)与MHC-I和TCR均具有高对接评分和抗原性反应。此外,通过最先进的生物信息学方法对预测的SARS-CoV-2表位的抗原性、过敏原性、毒性和物理化学性质进行了评估,表明所提出的表位作为候选疫苗具有很高的功效。结论:我们预测的SARS-CoV-2抗原表位可以促进疫苗的开发,增强对SARS-CoV-2的免疫原性,为进一步的实验验证提供支持数据。我们提出的利用MHC和TCR结构的分子对接方法可用于识别大多数微生物病原体的潜在表位,前提是MHC的晶体结构与TCR共结晶。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Immunoinformatics approach of epitope prediction for SARS-CoV-2.

Immunoinformatics approach of epitope prediction for SARS-CoV-2.

Immunoinformatics approach of epitope prediction for SARS-CoV-2.

Immunoinformatics approach of epitope prediction for SARS-CoV-2.

Background: The novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify the SARS-CoV-2 epitopes that bind specifically with the major histocompatibility molecules class I (MHC-I). We utilized immunoinformatic tools to analyze the whole viral protein sequences, to identify the SARS-CoV-2 epitopes responsible for binding to the most frequent human leukocyte antigen (HLA) alleles in the Egyptian population. These alleles were also found with high frequency in other populations worldwide.

Results: Molecular docking approach showed that using the co-crystallized MHC-I and T cell receptor (TCR) instead of using MHC-I structure only, significantly enhanced docking scores and stabilized the conformation, as well as the binding affinity of the identified SARS-CoV-2 epitopes. Our approach directly predicts 7 potential vaccine subunits from the available SARS-CoV-2 spike and ORF1ab protein sequence. This prediction has been confirmed by published experimentally validated and in silico predicted spike epitope. On the other hand, we predicted novel epitopes (RDLPQGFSA and FCLEASFNY) showing high docking scores and antigenicity response with both MHC-I and TCR. Moreover, antigenicity, allergenicity, toxicity, and physicochemical properties of the predicted SARS-CoV-2 epitopes were evaluated via state-of-the-art bioinformatic approaches, showing high efficacy of the proposed epitopes as a vaccine candidate.

Conclusion: Our predicted SARS-CoV-2 epitopes can facilitate vaccine development to enhance the immunogenicity against SARS-CoV-2 and provide supportive data for further experimental validation. Our proposed molecular docking approach of exploiting both MHC and TCR structures can be used to identify potential epitopes for most microbial pathogens, provided the crystal structure of MHC co-crystallized with TCR.

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