免疫分析
重组DNA
抗体
免疫球蛋白轻链
表达式向量
效价
化学
抗原
分子生物学
生物
免疫学
生物化学
基因
作者
Xin Lü,Yongli Ye,Yunyun Wang,Jia Xu,Jiadi Sun,Jian Ji,Yinzhi Zhang,Xiulan Sun
标识
DOI:10.1016/j.jhazmat.2023.131126
摘要
The rapid generation of high-quality target antibodies is essential for research employing immunoassays. The use of recombinant antibody technology that relies on genetic engineering is one such means to produce high-quality antibodies. Obtaining the gene sequence information of immunoglobulin is a prerequisite for the preparation of genetically engineered antibodies. At present, many researchers have shared their amino acid sequence data for various high-performance antibodies and their related properties. In this study, we obtained the protein sequence of a variable region of a 17 β-estradiol (E2) antibody from the Protein Data Bank (PDB) and subsequently constructed heavy (H) and light (L) chain expression vectors through codon optimization. The transient expression, purification, and performance identification of the immunoglobulin G (IgG), antigen-binding fragment (Fab), and single-chain variable fragment (scFv) antibodies were carried out, respectively. The effects of the different expression vectors on the expression yield of the IgG antibody were further compared. Among them, the expression yield based on the pTT5 vector was the highest, reaching 27 mg/L. Based on the expressed IgG and Fab antibodies, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) standard curve of E2 was constructed, and the half-maximal inhibitory concentrations (IC50) for these two antibodies were determined to be 0.129 ng/mL and 0.188 ng/mL, respectively. In addition, an immunochromatographic assay (ICA) based on the IgG antibody was constructed with an IC50 of 3.7 ng/mL. Therefore, in featuring the advantages of simplicity, high efficiency, rapid obtainment, and high titer yield, we propose the system for the rapid generation of high-quality recombinant antibodies by reusing the published antibody information and show that it has good implementation prospects in improving upon existing immunoassay techniques.
科研通智能强力驱动
Strongly Powered by AbleSci AI