Almalki Shaia, Beigh Saba, Akhter Naseem, Alharbi Read A
Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Albaha University, Albaha 65431, Saudi Arabia.
Department of Public Health, Faculty of Applied Medical Sciences, Albaha University, Albaha 65431, Saudi Arabia.
Saudi J Biol Sci. 2022 Jun;29(6):103283. doi: 10.1016/j.sjbs.2022.103283. Epub 2022 Apr 21.
Influenza A virus belongs to the most studied virus and its mutant initiates epidemic and pandemics outbreaks. Inoculation is the significant foundation to diminish the risk of infection. To prevent an incidence of influenza from the transmission, various practical approaches require more advancement and progress More efforts and research must take in front to enhance vaccine efficacy.
The present research emphasizes the development and expansion of a universal vaccine for the influenza virus. Research focuses on vaccine design with high efficacy. In this study, numerous computational approaches were used, covering a wide range of elements and ideas in bioinformatics methodology. Various B and T-cell epitopic peptides derived from the Neuraminidase protein N1 are recognized by these approaches. With the implementation of numerous obtained databases and bioinformatics tools, the different immune framework methods of the conserved sequences of N1 neuraminidase were analyzed. NCBI databases were employed to retrieve amino acid sequences. The antigenic nature of the neuraminidase sequence was achieved by the VaxiJen server and Kolaskar and Tongaonkar method. After screening of various B and T cell epitopes, one efficient peptide each from B cell epitope and T cell epitopes was assessed for their antigenic determinant vaccine efficacy. Identical two B cell epitopes were recognized from the N1 protein when analyzed using B-cell epitope prediction servers. The detailed examination of amino acid sequences for interpretation of B and T cell epitopes was achieved with the help of the ABCPred and Immune Epitope Database.
Computational immunology via immunoinformatic study exhibited RPNDKTG as having its high conservancy efficiency and demonstrated as a good antigenic, accessible surface hydrophilic B-cell epitope. Among T cell epitope analysis, YVNISNTNF was selected for being a conserved epitope. T cell epitope was also analyzed for its allergenicity and cytotoxicity evaluation. YVNISNTNF epitope was found to be a non-allergen and not toxic for cells as well. This T-cell epitope with maximum world populace coverages was scrutinized for its association with the HLA-DRB1*0401 molecule. Results from docking simulation analyses showed YVNISNTNF having lower binding energy, the radius of gyration (Rg), RMSD values, and RMSE values which make the protein structure more stable and increase its ability to become an epitopic peptide for influenza virus vaccination.
We propose that this epitope analysis may be successfully used as a measurement tool for the robustness of an antigen-antibody reaction between mutant strains in the annual design of the influenza vaccine.
甲型流感病毒是研究最多的病毒之一，其突变引发疫情和大流行。接种疫苗是降低感染风险的重要基础。为了预防流感传播，各种实用方法需要进一步改进和完善。必须付出更多努力和开展更多研究来提高疫苗效力。
本研究着重于流感病毒通用疫苗的研发和拓展。研究聚焦于高效疫苗设计。在本研究中，使用了多种计算方法，涵盖生物信息学方法中的广泛元素和理念。这些方法识别出了多种源自神经氨酸酶蛋白N1的B细胞和T细胞表位肽。通过运用众多已获得的数据库和生物信息学工具，分析了N1神经氨酸酶保守序列的不同免疫框架方法。利用NCBI数据库检索氨基酸序列。通过VaxiJen服务器以及Kolaskar和Tongaonkar方法确定神经氨酸酶序列的抗原性质。在筛选出各种B细胞和T细胞表位后，评估了来自B细胞表位和T细胞表位的各一种高效肽的抗原决定簇疫苗效力。使用B细胞表位预测服务器分析时，从N1蛋白中识别出了相同的两个B细胞表位。借助ABCPred和免疫表位数据库对氨基酸序列进行详细分析以解读B细胞和T细胞表位。
通过免疫信息学研究的计算免疫学显示，RPNDKTG具有高保守效率，并被证明是一个良好的抗原性、可及表面亲水性B细胞表位。在T细胞表位分析中，YVNISNTNF被选为保守表位。还对T细胞表位进行了致敏性和细胞毒性评估。发现YVNISNTNF表位不是过敏原，对细胞也无毒。对这个覆盖全球大多数人群的T细胞表位与HLA - DRB1*0401分子的关联进行了研究。对接模拟分析结果显示，YVNISNTNF具有较低的结合能、回转半径（Rg）、均方根偏差（RMSD）值和均方根误差（RMSE）值，这使得蛋白质结构更稳定，并增强了其成为流感病毒疫苗表位肽的能力。
我们提出，这种表位分析可成功用作年度流感疫苗设计中突变株之间抗原 - 抗体反应强度的测量工具。
