Nilvebrant Johan, Berndt Thalén Niklas, Rockberg Johan, Malm Magdalena
School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, AlbaNova University Center, Stockholm, Sweden.
Methods Mol Biol. 2025;2937:1-11. doi: 10.1007/978-1-0716-4591-8_1.
Antibodies are protein molecules indispensable for many therapeutic, diagnostic, and research purposes due to their exquisite ability to selectively recognize and bind a given antigen. The particular area of the antigen recognized by the antibody is called the epitope, and mapping of such epitopes can provide important mechanistic insights and indicate for what applications an antibody might be useful. Even though the nature of epitopes can be complex, epitopes in protein antigens are broadly grouped into linear or discontinuous epitopes depending on the positioning of the epitope residues in the antigen sequence and the requirement of structure. Specialized methods for mapping the two different classes of epitopes have been developed. While different in their detail, all of the experimental methods rely on assessing the binding of the antibody to the antigen or a set of antigen mimics. Classical epitope mapping methods, utilizing truncated proteins, small numbers of synthesized peptides, and structural analyses of antibody-antigen complexes, have been significantly refined. Current state-of-the-art methods involve combinations of mutational scanning, protein display, and high throughput screening in conjunction with bioinformatic analyses of large datasets. In line with the significant development of in silico protein structure prediction in recent years, computerized approaches for epitope prediction have also matured considerably.
抗体是蛋白质分子,由于其具有选择性识别和结合特定抗原的卓越能力,对于许多治疗、诊断和研究目的而言不可或缺。抗体识别的抗原的特定区域称为表位,绘制此类表位图谱可提供重要的机制见解,并指明抗体可能适用于哪些应用。尽管表位的性质可能很复杂,但根据表位残基在抗原序列中的位置和结构要求,蛋白质抗原中的表位大致可分为线性表位或不连续表位。已经开发出用于绘制这两类不同表位的专门方法。尽管细节不同,但所有实验方法都依赖于评估抗体与抗原或一组抗原模拟物的结合。利用截短蛋白、少量合成肽以及抗体 - 抗原复合物的结构分析的经典表位图谱绘制方法已得到显著改进。当前的先进方法涉及突变扫描、蛋白质展示和高通量筛选与大型数据集的生物信息学分析相结合。随着近年来计算机模拟蛋白质结构预测的显著发展,用于表位预测的计算机化方法也已相当成熟。