Nanobody (Nb), the smallest antibody fragments known to bind antigens, is now widely applied to various studies, including protein structure analysis, bioassay, diagnosis, and biomedicine. The traditional approach to generating specific nanobodies involves animal immunization which is time‐consuming and expensive. As the understanding of the antibody repertoire accumulation, the synthetic library, which is devoid of animals, has attracted attention widely in recent years. Here, we describe a synthetic phage display library (S‐Library), designed based on the systematic analysis of the next‐generation sequencing (NGS) of nanobody repertoire. The library consists of a single highly conserved scaffold (IGHV3S65*01‐IGHJ4*01) and complementary determining regions of constrained diversity. The S‐Library containing 2.19 × 108 independent clones was constructed by the one‐step assembly and rapid electro‐transformation. The S‐Library was screened against various targets (Nb G8, fusion protein of Nb G8 and green fluorescent protein, bovine serum albumin, ovalbumin, and acetylcholinesterase). In comparison, a naïve library (N‐Library) from the source of 13 healthy animals was constructed and screened against the same targets as the S‐Library. Binders were isolated from both S‐Library and N‐Library. The dynamic affinity was evaluated by the biolayer interferometry. The data confirms that the feature of the Nb repertoire is conducive to reducing the complexity of library design, thus allowing the S‐Library to be built on conventional reagents and primers.

中文翻译：

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基于序列的合成纳米抗体库的设计和构建

纳米抗体（Nb）是已知结合抗原的最小抗体片段，目前广泛应用于各种研究，包括蛋白质结构分析、生物测定、诊断和生物医学。产生特定纳米抗体的传统方法涉及动物免疫，这是耗时且昂贵的。随着对抗体库积累的理解，缺乏动物的合成库近年来受到广泛关注。在这里，我们描述了一个合成噬菌体展示库（S-Library），该库是基于对纳米抗体库的下一代测序（NGS）的系统分析而设计的。该文库由单个高度保守的支架 (IGHV3S65*01-IGHJ4*01) 和受限多样性的互补决定区域组成。 S-库包含 2.19 × 108通过一步组装和快速电转化构建独立克隆。 S-文库针对各种靶标（Nb G8、Nb G8 和绿色荧光蛋白的融合蛋白、牛血清白蛋白、卵清蛋白和乙酰胆碱酯酶）进行筛选。相比之下，构建了一个来自 13 只健康动物的原始文库（N-Library），并针对与 S-Library 相同的目标进行了筛选。从 S-Library 和 N-Library 中分离出活页夹。通过生物层干涉测量法评估动态亲和力。数据证实，Nb库的特性有利于降低文库设计的复杂性，从而允许在常规试剂和引物上构建S-文库。