Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.
Department of Biology, Stanford University, Stanford, CA, USA.
Sci Adv. 2022 Nov 11;8(45):eabp9540. doi: 10.1126/sciadv.abp9540.
De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.
从头设计方法有望减少抗体发现的时间和成本,同时能够轻松、精确地针对预定表位。在这里,我们描述了一种基于片段的方法,用于组合设计抗体结合环,并将其嫁接到抗体支架上。我们设计并测试了针对三种抗原上不同表位的六个单域抗体,包括 SARS-CoV-2 刺突蛋白的受体结合域。生物物理特性分析表明,所有设计都很稳定,与预期的靶标结合,亲和力在纳摩尔范围内,无需体外亲和力成熟。我们进一步讨论了为什么不需要高分辨率的输入抗原结构,因为当输入是晶体结构或计算机生成的模型时,也可以得到类似的预测。这个计算程序可以在笔记本电脑上轻松运行,为快速生成与预选表位结合的先导抗体提供了一个起点。