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通过大规模平行测序、核糖体展示和亲和筛选快速发现高亲和力抗体。

Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening.

机构信息

MRC Laboratory of Molecular Biology, Cambridge, UK.

UCB, Slough, UK.

出版信息

Nat Biomed Eng. 2024 Mar;8(3):214-232. doi: 10.1038/s41551-023-01093-3. Epub 2023 Oct 9.

Abstract

Developing therapeutic antibodies is laborious and costly. Here we report a method for antibody discovery that leverages the Illumina HiSeq platform to, within 3 days, screen in the order of 10 antibody-antigen interactions. The method, which we named 'deep screening', involves the clustering and sequencing of antibody libraries, the conversion of the DNA clusters into complementary RNA clusters covalently linked to the instrument's flow-cell surface on the same location, the in situ translation of the clusters into antibodies tethered via ribosome display, and their screening via fluorescently labelled antigens. By using deep screening, we discovered low-nanomolar nanobodies to a model antigen using 4 × 10 unique variants from yeast-display-enriched libraries, and high-picomolar single-chain antibody fragment leads for human interleukin-7 directly from unselected synthetic repertoires. We also leveraged deep screening of a library of 2.4 × 10 sequences of the third complementarity-determining region of the heavy chain of an anti-human epidermal growth factor receptor 2 (HER2) antibody as input for a large language model that generated new single-chain antibody fragment sequences with higher affinity for HER2 than those in the original library.

摘要

开发治疗性抗体既费力又昂贵。在这里,我们报告了一种利用 Illumina HiSeq 平台进行抗体发现的方法,该方法可以在 3 天内筛选出 10 种抗体-抗原相互作用。该方法被命名为“深度筛选”,涉及抗体文库的聚类和测序,将 DNA 聚类转化为共价连接到仪器流动池表面同一位置的互补 RNA 聚类,在原位将聚类翻译成通过核糖体展示连接的抗体,并通过荧光标记的抗原进行筛选。通过使用深度筛选,我们使用来自酵母展示富集文库的 4×10 个独特变体,发现了针对模型抗原的低纳摩尔纳米抗体,并且直接从未选择的合成库中发现了针对人白细胞介素 7 的高皮摩尔单链抗体片段配体。我们还利用深度筛选了针对人表皮生长因子受体 2(HER2)抗体重链的第三个互补决定区的 2.4×10 个序列的文库,作为输入输入到一个大型语言模型中,该模型生成了与原始文库相比对 HER2 具有更高亲和力的新单链抗体片段序列。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea95/10963267/d63ce3d74fb0/41551_2023_1093_Fig1_HTML.jpg

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