Wei Yiwen, Qiu Tianyi, Ai Yisi, Zhang Yuxi, Xie Junting, Zhang Dong, Luo Xiaochuan, Sun Xiulan, Wang Xin, Qiu Jingxuan
School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 334, Jungong Road, Yangpu District, Shanghai 200093, China.
Institute of Clinical Science, Zhongshan Hospital; Intelligent Medicine Institute; Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Medical College, Fudan University, No. 180, Fenglin Road, Xuhui Destrict, Shanghai 200032, China.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf055.
Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.
疫苗研发是最具前景的领域之一,多表位疫苗无需繁琐的培养过程,作为传统疫苗的一种有吸引力的替代方案,具有安全性和有效性的优势。算法的快速发展和免疫数据的积累推动了计算机辅助疫苗设计的进步。在此,我们系统地综述了用于计算机辅助疫苗设计不同步骤的计算机模拟数据和算法资源,包括免疫原选择、表位预测、疫苗构建、优化和评估。在基准数据集上测试并比较了不同现有工具在表位预测和免疫原性评估方面的性能。最后,我们讨论了构建多表位疫苗的未来研究方向。