Identification of novel putative immunogenic targets and construction of a multi-epitope vaccine against multidrug-resistant Corynebacterium jeikeium using reverse vaccinology approach.

Microbial pathogenesis(2022)

引用 8|浏览2
暂无评分
摘要
The emergence of multidrug-resistant Corynebacterium jeikeium has limited treatment options and resulted in the inability to treat C. jeikeium infections, especially in immunocompromised patients. To our knowledge, no studies have been conducted to evaluate C. jeikeium antigens for vaccine development. Given the lack of effective treatments against C. jeikeium, this study aimed to identify potential immunogenic targets against C. jeikeium as a nosocomial pathogen using a reverse vaccinology approach. To achieve this goal, we performed several immuninformatics analyses, including antigenicity, allergenicity, PSI-BLAST to the human proteome, physiochemical properties, B-cell and T-cell epitopes, molecular docking, and immunosimulation. In addition, quartile scoring and prevalence assessment were used to select the most abundant immunogenic targets in different C. jeikeium strains. Finally, protein-protein interactions were performed and the multi-epitope vaccine was developed. Five putative immunogenic targets were presented as short-listed proteins in this study, including three enzymatic proteins (WP_011273969.1, WP_041626322.1, and WP_005292204.1), one protein with DUF3235 domain (WP_011273103.1), and one hypothetical protein (WP_005293648.1). Four linear B-cell epitopes of putative immunogenic targets, including WP_011273103.1 (LNSKPTPRNAAAKPKAK), WP_011273969.1 (GEGAQGSAAPADAQATANE), WP_005292204.1 (ASVSAAQKADGIAP), and WP_041626322.1 (YSKKVAEEMGVG) were selected and inserted into the mutant TbpB C-lobe protein. This platform can effectively present multiple epitopes to the immune system. However, experimental in vitro and in vivo analysis is required to confirm the safety, immunoreactivity, and efficacy of these putative immunogenic targets.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要