ScFv及重组TNF-α双功能抗体融合蛋白的表达及其软件预测

Expression of single-chain bifunctional antibody fusion protein and its forecast with softwares

目的:利用生物信息学网络资源分析融合蛋白的二级结构及其理化性质,以及探讨分泌型抗肝癌单链双功能抗体基因的表达。方法:在构建融合蛋白之前,运用DNA分析软件(DNAssist)和蛋白质分析软件(ANTHEPROTV5)分析融合蛋白的氨基酸序列、二级结构及其理化性质。采用PCR方法将人工合成的抗体分泌信号肽序列加在抗肝癌ScFv基因5′端,其3′与人的肿瘤坏死因子TNFα基因连接构成分泌型单链双功能抗体ScFvTNFα基因,将该基因克隆至逆转录病毒表达载体PLxSN,用NIH3T3测定病毒滴度,将重组病毒感染人肝癌细胞命名为HCC/ScFvTNFα,以PCR,RTPCR以及Westernblotting对ScFvTNFα基因修饰的HCC9204细胞进行鉴定。结果:运用DNAssist核酸序列分析软件分析ScFvTNFαDNA序列翻译并获得了氨基酸序列,运用ANTHEPROTV5蛋白质分析软件分析融合蛋白的二级结构及其理化性质。经酶切及PCR分析鉴定,成功地构建了融合基因表达载体pL(ScFvTNFα)SN,并获得高滴度产毒细胞株,以及Westernblotting分析证明ScFvTNFα基因融合蛋白的表达。结论:利用生物信息学网络资源进行分析预测融合蛋白的性质,为进一步探讨单链双功能抗体基因融合蛋白提供依据。

OBJECTIVE： To analyze secondary structure of the fusion protein with software, and to establish a recombinant secretory anti-hepatocellular carcinoma single-chain hi-functional anti body gene. METHODS： DNAssist and ANTHEPROT V5 were used to analyze their sequence, their secondary structure, and their physical and ehymie character. By the technology of directional cloning, the sequenced genes were subcloned into corresponding restriction sites of PLxSN in turn to generate co-expression vector. PL （ScFv-TNF-α）SN was packaged with PA317 and selected in （3418 to obtain the positive clones, which were able to produce stable retrovirus, and then HCC9204 cells were infected by the recombinant retrovirus. The positive clones were obtained after （3418 selection and were termed HCC/ScFv-TNF-α, and fusion protein were identified by Western blotting in the transfeeted HCC9204 cells. RESULTS： On the fusion genes, the obtained sequence was analyzed with DNA ssist and ANTHEPROT V5. The inserted fragments in all constructed plasmids were structurally confirmed to be consistent with that of the published data. Similarly, the G418 selected vector PL（ScFv-TNF-α）SN was confirmed to be successful in the stable expression of the objective proteins in the target cells, and expression of recombinant protein was confirmed by Western blotting. CONCLUSIONS： The softwares should be fully used to analyze biologic information, and the approach may have application in a gene therapy context.