Abstract：
BACKGROUND: This immunoinformatic study identified potential epitopes from the envelopment polyprotein (Gn/Gc) of Rift Valley fever virus (RVFV), a pathogenic virus causing severe fever in humans and livestock. Effective vaccination is crucial for controlling RVFV outbreaks. The identification of suitable epitopes is crucial for the development of safe and effective vaccines.
METHODS: Protein sequences were obtained from the UniProt database, and evaluated through VaxiJen v2.0 to predict the B and T-cell epitopes within the RVFV glycoprotein. Gn/Gc protein sequences were analyzed with bioinformatics tools and algorithms. The predicted T-cell and B-cell epitopes were evaluated for antigenicity, allergenicity, and toxicity by the VaxiJen v2.0 system, AllerTop v2.0, and ToxinPred server, respectively.
RESULTS: We employed computational methods to screen the RVFV envelopment polyprotein encompassing N-terminal and C-terminal glycoprotein segments, to discover antigenic T- and B-cell epitopes. Our analysis unveiled multiple potential epitopes within the RVFV glycoprotein, specifically within the Gn/Gc protein sequences. Subsequently, we selected eleven cytotoxic T-lymphocytes (CTL) and four helper T-lymphocytes (HTL) for population coverage analysis, which collectively extended to cover 97.04% of the world\'s population, representing diverse ethnicities and regions. Notably, the CTL epitope VQADLTLMF exhibited binding affinity to numerous human leukocyte antigen (HLA) alleles. The identification of glycoprotein (Gn/Gc) epitopes through this immunoinformatic study bears significant implications for advancing the development of an effective RVFV vaccine.
CONCLUSIONS: These findings provide valuable insights into the immunological aspects of the disease and may contribute towards the development of broad-spectrum antiviral therapies targeting other RNA viruses with similar polymerase enzymes.
METHODS: Protein sequences were obtained from the UniProt database, and evaluated through VaxiJen v2.0 to predict the B and T-cell epitopes within the RVFV glycoprotein. Gn/Gc protein sequences were analyzed with bioinformatics tools and algorithms. The predicted T-cell and B-cell epitopes were evaluated for antigenicity, allergenicity, and toxicity by the VaxiJen v2.0 system, AllerTop v2.0, and ToxinPred server, respectively.
RESULTS: We employed computational methods to screen the RVFV envelopment polyprotein encompassing N-terminal and C-terminal glycoprotein segments, to discover antigenic T- and B-cell epitopes. Our analysis unveiled multiple potential epitopes within the RVFV glycoprotein, specifically within the Gn/Gc protein sequences. Subsequently, we selected eleven cytotoxic T-lymphocytes (CTL) and four helper T-lymphocytes (HTL) for population coverage analysis, which collectively extended to cover 97.04% of the world\'s population, representing diverse ethnicities and regions. Notably, the CTL epitope VQADLTLMF exhibited binding affinity to numerous human leukocyte antigen (HLA) alleles. The identification of glycoprotein (Gn/Gc) epitopes through this immunoinformatic study bears significant implications for advancing the development of an effective RVFV vaccine.
CONCLUSIONS: These findings provide valuable insights into the immunological aspects of the disease and may contribute towards the development of broad-spectrum antiviral therapies targeting other RNA viruses with similar polymerase enzymes.
摘要：
背景：这项免疫信息学研究从裂谷热病毒（RVFV）的包膜多蛋白（Gn/Gc）中鉴定了潜在的表位，引起人类和牲畜严重发烧的致病性病毒。有效的疫苗接种对于控制RVFV爆发至关重要。合适表位的鉴定对于开发安全有效的疫苗至关重要。
方法：从UniProt数据库获得蛋白质序列，并通过VaxiJenv2.0进行评估，以预测RVFV糖蛋白内的B和T细胞表位。使用生物信息学工具和算法分析Gn/Gc蛋白序列。评估预测的T细胞和B细胞表位的抗原性，变应原性,和VaxiJenv2.0系统的毒性，AllerTopv2.0和ToxinPred服务器,分别。
结果：我们采用计算方法来筛选包含N端和C端糖蛋白片段的RVFV包膜多蛋白，发现抗原T细胞和B细胞表位。我们的分析揭示了RVFV糖蛋白中的多个潜在表位，特别是在Gn/Gc蛋白序列内。随后，我们选择了11个细胞毒性T淋巴细胞（CTL）和4个辅助T淋巴细胞（HTL）进行群体覆盖率分析，总共覆盖了世界上97.04%的人口，代表不同的种族和地区。值得注意的是,CTL表位VQADLTLMF表现出对许多人白细胞抗原(HLA)等位基因的结合亲和力。通过该免疫信息学研究鉴定糖蛋白(Gn/Gc)表位对于推进有效的RVFV疫苗的开发具有重要意义。
结论：这些发现为该疾病的免疫学方面提供了有价值的见解，并可能有助于开发针对具有相似聚合酶的其他RNA病毒的广谱抗病毒疗法。
方法：从UniProt数据库获得蛋白质序列，并通过VaxiJenv2.0进行评估，以预测RVFV糖蛋白内的B和T细胞表位。使用生物信息学工具和算法分析Gn/Gc蛋白序列。评估预测的T细胞和B细胞表位的抗原性，变应原性,和VaxiJenv2.0系统的毒性，AllerTopv2.0和ToxinPred服务器,分别。
结果：我们采用计算方法来筛选包含N端和C端糖蛋白片段的RVFV包膜多蛋白，发现抗原T细胞和B细胞表位。我们的分析揭示了RVFV糖蛋白中的多个潜在表位，特别是在Gn/Gc蛋白序列内。随后，我们选择了11个细胞毒性T淋巴细胞（CTL）和4个辅助T淋巴细胞（HTL）进行群体覆盖率分析，总共覆盖了世界上97.04%的人口，代表不同的种族和地区。值得注意的是,CTL表位VQADLTLMF表现出对许多人白细胞抗原(HLA)等位基因的结合亲和力。通过该免疫信息学研究鉴定糖蛋白(Gn/Gc)表位对于推进有效的RVFV疫苗的开发具有重要意义。
结论：这些发现为该疾病的免疫学方面提供了有价值的见解，并可能有助于开发针对具有相似聚合酶的其他RNA病毒的广谱抗病毒疗法。
