单克隆抗体
抗体
计算生物学
噬菌体展示
生物
转基因
药物发现
转导(生物物理学)
信号转导
转基因小鼠
细胞生物学
肽库
病毒学
定向进化
细胞
蛋白质工程
免疫学
序列(生物学)
分子生物学
抗体库
作者
Kyung Ho Han,Yi‐Chuan Li,Rabia Parveen,Srimathi Venkataraman,Chih‐Wei Lin
标识
DOI:10.3390/ijms262110470
摘要
Monoclonal antibodies (mAbs) represent one of the most successful classes of biopharmaceuticals, with more than 100 approved for treating oncological, immunological, and infectious diseases. Antibody discovery and development have been driven by diverse methodologies. Classical strategies such as mouse hybridoma technology, phage display, transgenic mouse models, and single B cell isolation have enabled the generation of high-affinity therapeutic antibodies. Beyond binding affinity, recent innovations in combinatorial antibody libraries have facilitated the selection of functional antibodies within cellular environments, revealing their ability to act as agonists or antagonists and influence signal transduction pathways. These insights expand therapeutic applications by enabling modulation of complex cellular responses. Recent breakthroughs in artificial intelligence, involving antibody generation supported by rapidly growing antibody sequence and structure databases, are transforming computational protein design. This review highlights five major approaches (hybridoma technology, phage display, transgenic mouse models, and single B cell isolation, de novo antibody design) for antibody discovery and development. These approaches offer innovative strategies designed to accelerate the discovery process and enhance therapeutic outcomes for human diseases.
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