Jalalvand Alireza, Fotouhi Fatemeh, Bahramali Golnaz, Bambai Bijan, Farahmand Behrokh
Department of Influenza and Other Respiratory Viruses, Pasteur Institute of Iran, Tehran, Iran.
Department of Hepatitis and AIDS, Pasteur Institute of Iran, Tehran, Iran.
J Biomol Struct Dyn. 2025 Feb;43(3):1522-1538. doi: 10.1080/07391102.2023.2292293. Epub 2023 Dec 13.
Hemagglutinin (HA), a variable viral surface protein, is essential for influenza vaccine development. Annually, traditional trivalent vaccines containing influenza A/H1N1, A/H3N2 and B viruses are administered globally, which are not very effective for the mutations in HA protein. The aim of this study was to design a multi-epitope vaccine containing epitopes of the HA protein of H1N1, H3N2 and B viruses using immunoinformatics methods. The HA protein epitope prediction was performed using Immune Epitope Database. Toxicity, antigenicity and conservancy of the epitopes were evaluated using ToxinPred, VaxiJen and Epitope Conservancy Analysis tools, respectively. Then, nontoxic, antigenic and high conserved epitopes with high prediction scores were selected. Their binding affinity was evaluated against human and mouse MHC class I and II molecules using the HPEPDOCK tool. Physicochemical properties and post-translational modifications were evaluated using ProtParam, SOLpro and MusiteDeep tools, respectively. Top selected epitopes were joined using linkers to produce the best effective recombinant trivalent vaccine candidate to elicit cellular and humoral immune responses in mouse and human host models. These sequences were modeled and verified. By evaluating the results of various analyses of all models and the most similarity to the native HA protein, model 5 was selected as the best model. Finally, cloning of this model as vaccine candidate was performed in pET21. This study was a computer-aided analysis for a multi-epitope trivalent recombinant vaccine candidate against influenza viruses. The efficiency of our best model of vaccine candidates should be validated using and studies. Communicated by Ramaswamy H. Sarma.
血凝素（HA）是一种可变的病毒表面蛋白，对流感疫苗的研发至关重要。每年，全球都会接种包含甲型H1N1、甲型H3N2和乙型流感病毒的传统三价疫苗，但这些疫苗对HA蛋白的突变效果不佳。本研究旨在利用免疫信息学方法设计一种包含H1N1、H3N2和乙型流感病毒HA蛋白表位的多表位疫苗。使用免疫表位数据库进行HA蛋白表位预测。分别使用ToxinPred、VaxiJen和表位保守性分析工具评估表位的毒性、抗原性和保守性。然后，选择预测得分高的无毒、抗原性强且高度保守的表位。使用HPEPDOCK工具评估它们与人及小鼠MHC I类和II类分子的结合亲和力。分别使用ProtParam、SOLpro和MusiteDeep工具评估理化性质和翻译后修饰。使用接头连接顶级选择的表位，以产生最佳有效的重组三价疫苗候选物，从而在小鼠和人类宿主模型中引发细胞免疫和体液免疫反应。对这些序列进行建模和验证。通过评估所有模型的各种分析结果以及与天然HA蛋白的最大相似性，选择模型5作为最佳模型。最后，在pET21中进行该模型作为疫苗候选物的克隆。本研究是针对一种抗流感病毒的多表位三价重组疫苗候选物的计算机辅助分析。我们最佳疫苗候选模型的效率应通过[具体实验]和[具体实验]研究进行验证。由Ramaswamy H. Sarma传达。
