摘要
目的 分别构建乙型肝炎病毒截短L蛋白(NL蛋白)和M蛋白目的基因的真核表达质粒,并进行体外表达;运用生物信息学方法分析NL蛋白和S蛋白的生物信息学特征,对比预测分析NL蛋白与S蛋白的免疫原性。方法 以全长L蛋白目的基因为模板,PCR扩增NL蛋白基因和M蛋白基因,分别与根据pCHO1.0改造后的表达载体pCHO1.0-1和pCHO1.0new-1构建重组质粒,共转染CHO-S细胞进行蛋白表达并利用阴离子交换层析方法进行蛋白纯化。通过SDS-PAGE和Western blot分析纯化蛋白的纯度及活性,利用透射电镜观察VLPs形成情况。利用生物信息学软件ProtParam分析NL蛋白和S蛋白的理化性质,利用Signal P5.0 Service和TMPRED分析蛋白的信号肽和跨膜区,利用NPS@SOPMA和Swissmodel分别预测蛋白的二级结构和三级结构,利用IEDB Analysis Resource分析预测蛋白的可及性、亲水性、β转角和线性抗原表位。结果 构建的pCHO1.0-1-NL和pCHO1.0new-1-M重组质粒序列正确,表达蛋白纯化后的纯度可达85%,纯化蛋白可被抗-HBs抗血清识别。透射电镜观察显示形成直径约为22 nm的VLPs。经生物信息学比较分析,NL蛋白为疏水性蛋白,含有3个跨膜结构,无信号肽,二级结构主要为无规则卷曲,结构较为松散。与NL蛋白相比较,单纯S蛋白则含有4个跨膜区域,并含有信号肽。IEDB Analysis Resource预测NL蛋白抗原优势表位,其前S蛋白氨基酸区域较S蛋白含有更多的抗原优势表位。结论 成功构建含前S1和前S2共表达载体,并同时表达含乙型肝炎病毒NL、M和S蛋白的VLPs。生物信息学分析了乙型肝炎病毒抗原优势表位的理论集中区域,为研发含有前S蛋白的新一代乙肝疫苗奠定了基础。
Objective To express HBV envelope proteins in vitro by constructing eukaryotic expression plasmids of truncated L protein(NL protein) and M protein target genes.To Comparatively analyze the immunogenicity of NL protein and S protein and analyzeboth bioinformatics characteristics by using bioinformatics.Methods The NL and M target protein genes was amplified form the full-long L protein target gene by using PCR,and recombinant plasmid was constructed with the expression vector pCHO1.0-1 and pCHO1.0 new-1.Two constructed expression plasmids were co-transfected into CHO cells throughthe transfection reagent.And then screened out stable expression cell lines.The protein expression is detected by ELISA and purified by anion exchange chromatography.The purity and activity of the purified protein were analyzed by SDS-PAGE and Western blot,and the formation of VLPs was observed by transmission electron microscope.Use the bioinformatics software to analyze the NL protein and S protein.Used ProtParam to analyze the physical and chemical properties of the two proteins,Signal P5.0 Service and TMPRED analyze the signal peptide and transmembrane region of the two proteins,NPS@SOPMA and Swiss model respectively predict the secondary structure and tertiary structure of two proteins,using IEDB Analysis Resource predicts the accessibility,hydrophilicity,beta turn and linear epitope of the two proteins.Results The sequencing results of pCHO1.0-1-NL and pCHO1.0 new-1-M recombinant plasmids were correct.The purity of the proteins after expression and purification can reach 85%,and purified proteins can be recognized by anti-HBs antiserum.The result of the SDS-PAGE and Western Blot show that purified proteins contain glycosylated and non-glycosylated NL protein,M protein and S protein.20000 times magnification of electron microscopy result show that VLPs with a diameter of about 22 nm are formed.The analyses of bioinformatics show that the NL protein is a hydrophobic protein with three transmembrane structures and no signal peptide.The secondary structure is mainly random coils,and the structure is relatively loose.Unlike the NL protein,the S protein contains four transmembrane regions and a signal peptide.The comprehensive prediction of the dominant epitope of the NL protein showed that pre-S protein contains three antigenic dominant epitopes and S protein contains only one.One of the antigenic dominant epitopes is composed of pre-S protein and S protein together.Indicating that the amino acid region of the pre-S protein contained more dominant epitopes.Conclusion Successfully constructed a co-expression vector containing pre-S1 and pre-S2,and simultaneously expressed VLPs containing hepatitis B virus NL protein,M protein and S protein.Lay the experimental foundation for the development of a new generation of fhepatitis B vaccine containing pre-S protein.
作者
王炜晓
赵丹莹
陈子杨
常东英
李可新
曹玉锋
常军亮
WANG Wei-xiao;ZHAO Dan-ying;CHEN Zi-yang;CHANG Dong-ying;LI Ke-xin;CAO Yu-feng;CHANG Jun-liang(Changchun Institute of Biological Products Co.,Ltd.,Changchun 130012,China;Yidao Biotechnology (Suzhou) Co.,Ltd.)
出处
《中国病原生物学杂志》
CSCD
北大核心
2022年第4期373-379,387,共8页
Journal of Pathogen Biology
基金
吉林省科技发展计划项目(No.20190404001YY)。
关键词
乙型肝炎病毒
前S蛋白
真核表达
生物信息学
Hepatitis B virus
envelope protein
eukaryotic expression
bioinformatics