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川芎α-淀粉酶抑制剂基因的原核表达与条件优化 被引量:1

Prokaryotic expression and condition optimization ofα-amylase inhibitor gene of Ligusticum chuanxiong
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摘要 利用实验室前期已获得的α-淀粉酶抑制剂基因(LASI),将其分别连接到不同载体上构建重组原核表达质粒,在原核表达系统中诱导表达LASI重组蛋白,并优化其诱导条件。通过Western Blot分析鉴定LASI重组蛋白,并采用碘-淀粉呈色法测定LASI对猪源淀粉酶的抑制活性。结果表明:pET28a是LASI的较好表达载体,当诱导时间为8 h,IPTG终浓度为1.0 mmol/L,诱导温度为27℃时,LASI重组蛋白的表达量达到最佳;在最优的诱导表达条件下,表达出的部分LASI重组蛋白以可溶性蛋白的形式存在,并能够与HIS标签单抗杂交;经纯化后获得的LASI重组蛋白对猪源淀粉酶存在抑制作用,终抑制比为34%。研究优化LASI重组蛋白的表达条件,获得足量的LASI重组蛋白,为进一步研究LASI的功能奠定基础。 The LASI gene was ligated into different vectors to construct recombinant prokaryotic expression plasmids.Expressed conditions of LASI gene were subsequently optimized via three aspects inducing time,IPTG concentration and inducing temperature.The results indicated that pET28a was a suitable expression vector for LASI.The optimal conditions for expression of pET28a-LASI were inducing time of 8 hours,IPTG at concentration of 1.0 mmol/L,and inducing temperature of 27℃.The solubility assay indicated that in optimal conditions,part of the LASI recombinant protein exists in soluble form and the recombinant protein could specifically bind to the HIS-tagged monoclonal antibody.The purified LASI was capable of inhibiting the activities of porcine amylase and achieving the ultimate inhibition rate in 34%.In this study,the expression conditions of LASI recombinant protein were optimized,and a amount of LASI recombinant protein was obtained.Therefore,this work laid the foundation for further study on the function of LASI.
作者 邓楷煜 安秋菊 陈安琪 俞继华 张富丽 廖海 DENG Kaiyu;AN Qiuju;CHEN Anqi;YU Jihua;ZHANG Fuli;LIAO Hai(School of Life and Science,Southwest Jiaotong University,Chengdu 610031,China;Analysis and Determination Center,Sichuan Academy of Agricultural Sciences,Chengdu 610066,China)
出处 《生物学杂志》 CAS CSCD 北大核心 2022年第4期7-11,17,共6页 Journal of Biology
基金 国家自然科学基金青年科学基金项目(31500276) 四川省重点研发项目(2018SZ0061) 四川省创新能力提升工程公益深化项目(2016GYSH-032) 成都市科技局重点研发计划(2019-YF05-02257-SN)。
关键词 川芎 Α-淀粉酶抑制剂 原核表达 条件优化 Ligusticum chuanxiong α-amylase inhibitor prokaryotic expression condition optimization
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