Establishing modular binders as diagnostic detection agents represents a cost- and time-efficient alternative to the commonly used binders that are generated one molecule at a time. In contrast to these conventional approaches, a modular binder can be designed in silico from individual modules to, in principle, recognize any desired linear epitope without going through a selection and hit-validation process, given a set of preexisting, amino acid–specific modules. Designed armadillo repeat proteins (dArmRP) have been developed as modular binder scaffolds, and we report here the generation of highly specific dArmRP modules by yeast surface display selection, performed on a rationally designed dArmRP library. A selection strategy was developed to distinguish the binding difference resulting from a single amino acid mutation in the target peptide. Our reverse-competitor strategy introduced here employs the designated target as a competitor to increase the sensitivity when separating specific from cross-reactive binders that show similar affinities for the target peptide. With this switch in selection focus from affinity to specificity, we found that the enrichment during this specificity sort is indicative of the desired phenotype, regardless of the binder abundance. Hence, deep sequencing of the selection pools allows retrieval of phenotypic hits with only 0.1% abundance in the selectivity sort pool from the next-generation sequencing data alone. In a proof-of-principle study, a binder was created by replacing all corresponding wild-type modules with a newly selected module, yielding a binder with very high affinity for the designated target that has been successfully validated as a detection agent in western blot analysis.

中文翻译：

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模块化结合剂技术，通过 NGS 辅助，在酵母中对设计的犰狳模块进行高分辨率选择


建立模块化结合物作为诊断检测剂代表了一次生成一个分子的常用结合物的一种经济高效的替代方案。与这些传统方法相比，模块化结合剂可以在计算机上从各个模块设计出来，原则上，只要给定一组预先存在的氨基酸特异性模块，就可以识别任何所需的线性表位，而无需经过选择和命中验证过程。设计的犰狳重复蛋白（dArmRP）已被开发为模块化结合物支架，我们在此报告了通过在合理设计的 dArmRP 库上进行的酵母表面展示选择来生成高度特异性的 dArmRP 模块。开发了一种选择策略来区分由目标肽中的单个氨基酸突变引起的结合差异。我们在此介绍的反向竞争策略采用指定的靶标作为竞争剂，以提高在将特异性结合剂与对目标肽显示出相似亲和力的交叉反应性结合剂分离时的灵敏度。随着选择焦点从亲和力到特异性的转变，我们发现，无论结合物丰度如何，这种特异性排序过程中的富集都表明了所需的表型。因此，选择池的深度测序允许仅从下一代测序数据中检索选择性排序池中丰度仅为 0.1% 的表型命中。在一项原理验证研究中，通过用新选择的模块替换所有相应的野生型模块来创建结合物，产生对指定目标具有非常高亲和力的结合物，该结合物已被成功验证为蛋白质印迹中的检测剂分析。