Rawaa AlChalabi, Aya Al-Rahim, Dania Omer, Ahmed AbdulJabbar Suleiman
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As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein-protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the <i>Toll-like receptor 4</i> protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. 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引用次数: 0
摘要
流感嗜血杆菌是一种导致人类侵入性细菌感染的病原体。幼儿和成年人的发病率最高。一般来说,目前还没有针对所有流感嗜血杆菌菌株的疫苗。因此,本研究的目的是利用生物信息学和免疫信息学方法,设计一种利用致病性细胞分裂蛋白 FtsN 的多表位候选疫苗,以特异性对抗所有流感嗜血杆菌菌株。目前的研究重点是开发亚单位疫苗,而不是从整个病原体中提取疫苗。这将通过结合多种生物信息学和免疫信息学方法来实现。因此,我们研究了前瞻性 T 细胞(辅助性 T 淋巴细胞和细胞毒性 T 淋巴细胞)和 B 细胞表位。选择的人类白细胞抗原等位基因与抗原表位和重叠表位有很强的关联性,覆盖全球总人口的 70%。为了构建连接疫苗设计,使用了多种连接剂。为了增加免疫原性,使用 EAAAK 连接剂连接了佐剂。添加佐剂和连接剂后,最终得到了含有 149 个氨基酸的疫苗构建体。所开发的多表位疫苗具有高抗原性和可行的理化特性。利用生物信息学方法建立了三维构象模型,并对其进行了完善和验证。此外,还进行了蛋白质-蛋白质分子对接分析,以预测多表位疫苗与 Toll 样受体 4 蛋白的有效结合位置。此外,疫苗还经过了密码子翻译优化和计算克隆,以验证多位一体疫苗表达的可靠性和正确性。此外,有必要在实验室进行实验和研究,以证明已开发的疫苗具有免疫原性和保护性。
Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein.
Haemophilus influenzae is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of Haemophilus influenzae. Hence, the purpose of this research is to employ bioinformatics and immunoinformatics approaches to design a Multi-Epitope Vaccine candidate employing the pathogenic cell division protein FtsN that specifically combat all the Haemophilus influenzae strains. The current research focuses on developing subunit vaccine in contrast to vaccines generated from the entire pathogen. This will be accomplished by combining multiple bioinformatics and immunoinformatics approaches. As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein-protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the Toll-like receptor 4 protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. In addition, it is necessary to conduct experiments and research in the laboratory to demonstrate that the vaccine that has been developed is immunogenic and protective.
期刊介绍:
NetMAHIB publishes original research articles and reviews reporting how graph theory, statistics, linear algebra and machine learning techniques can be effectively used for modelling and analysis in health informatics and bioinformatics. It aims at creating a synergy between these disciplines by providing a forum for disseminating the latest developments and research findings; hence, results can be shared with readers across institutions, governments, researchers, students, and the industry. The journal emphasizes fundamental contributions on new methodologies, discoveries and techniques that have general applicability and which form the basis for network based modelling, knowledge discovery, knowledge sharing and decision support to the benefit of patients, healthcare professionals and society in traditional and advanced emerging settings, including eHealth and mHealth .