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一种用于多表位疫苗设计的计算机深度学习方法:以戊型肝炎病毒为例的研究

An In Silico Deep Learning Approach to Multi-Epitope Vaccine Design: A Hepatitis E Virus Case Study.

作者信息

Ikram Aqsa, Alzahrani Badr, Zaheer Tahreem, Sattar Sobia, Rasheed Sidra, Aurangzeb Muhammad, Ishaq Yasmeen

机构信息

Institute of Molecular Biology and Biotechnology (IMBB), University of Lahore (UOL), Lahore 54000, Pakistan.

Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

出版信息

Vaccines (Basel). 2023 Mar 22;11(3):710. doi: 10.3390/vaccines11030710.

DOI:10.3390/vaccines11030710
PMID:36992295
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10059858/
Abstract

Hepatitis E Virus (HEV) is a major cause of acute and chronic hepatitis. The severity of HEV infection increases manyfold in pregnant women and immunocompromised patients. Despite the extensive research on HEV in the last few decades, there is no widely available vaccine yet. In the current study, immunoinformatic analyses were applied to predict a multi-epitope vaccine candidate against HEV. From the ORF2 region, 41 conserved and immunogenic epitopes were prioritized. These epitopes were further analyzed for their probable antigenic and non-allergenic combinations with several linkers. The stability of the vaccine construct was confirmed by molecular dynamic simulations. The vaccine construct is potentially antigenic and docking analysis revealed stable interactions with TLR3. These results suggest that the proposed vaccine can efficiently stimulate both cellular and humoral immune responses. However, further studies are needed to determine the immunogenicity of the vaccine construct.

摘要

戊型肝炎病毒(HEV)是急性和慢性肝炎的主要病因。戊型肝炎病毒感染在孕妇和免疫功能低下患者中的严重程度会增加许多倍。尽管在过去几十年中对戊型肝炎病毒进行了广泛研究,但目前尚无广泛可用的疫苗。在本研究中,应用免疫信息学分析来预测一种针对戊型肝炎病毒的多表位疫苗候选物。从开放阅读框2(ORF2)区域中,确定了41个保守且具有免疫原性的表位。进一步分析了这些表位与几种连接子可能的抗原性和非致敏性组合。通过分子动力学模拟证实了疫苗构建体的稳定性。该疫苗构建体具有潜在的抗原性,对接分析显示其与Toll样受体3(TLR3)有稳定的相互作用。这些结果表明,所提出的疫苗可以有效刺激细胞免疫和体液免疫反应。然而,需要进一步研究来确定该疫苗构建体的免疫原性。

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