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基于片段的抗体靶向结构表位的计算设计。

Fragment-based computational design of antibodies targeting structured epitopes.

机构信息

Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.

Department of Biology, Stanford University, Stanford, CA, USA.

出版信息

Sci Adv. 2022 Nov 11;8(45):eabp9540. doi: 10.1126/sciadv.abp9540.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/245f/9651861/37eeb4e3ce2e/sciadv.abp9540-f1.jpg

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