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针对丙型肝炎病毒的候选疫苗进行组合优化设计的测试。

Testing a vaccine candidate against Hepatitis C virus designed by combinatorial optimization.

机构信息

Department of Mathematics, University of the Basque Country (UPV/EHU), 48080, Bilbao, Spain.

Biocruces Health Research Institute, Bilbao, Spain.

出版信息

Sci Rep. 2023 Dec 8;13(1):21746. doi: 10.1038/s41598-023-48458-x.

Abstract

This paper presents a new procedure for vaccine design against highly variable viruses such as Hepatitis C. The procedure uses an optimization algorithm to design vaccines that maximize the coverage of epitopes across different virus variants. Weighted epitopes based on the success ratio of immunological assays are used to prioritize the selection of epitopes for vaccine design. The procedure was successfully applied to design DC vaccines loaded with two HCV peptides, STG and DYP, which were shown to be safe, immunogenic, and able to induce significant levels of anti-viral cytokines, peptide-specific cellular immune responses and IgG antibodies. The procedure could potentially be applied to other highly variable viruses that currently lack effective vaccines.

摘要

本文提出了一种针对高度变异病毒(如丙型肝炎)的新型疫苗设计方法。该方法使用优化算法来设计疫苗,以最大限度地覆盖不同病毒变异体的表位。基于免疫测定成功率的加权表位用于优先选择用于疫苗设计的表位。该方法成功地应用于设计负载两种 HCV 肽(STG 和 DYP)的 DC 疫苗,结果表明这些疫苗是安全的、免疫原性的,并且能够诱导显著水平的抗病毒细胞因子、肽特异性细胞免疫反应和 IgG 抗体。该方法可能适用于其他目前缺乏有效疫苗的高度变异病毒。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea12/10709393/39273b4731f6/41598_2023_48458_Fig1_HTML.jpg

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