Generating High-Accuracy Peptide-Binding Data in High Throughput with Yeast Surface Display and SORTCERY.
Generating High-Accuracy Peptide-Binding Data in High Throughput with Yeast Surface Display and SORTCERY.
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DOI：
10.1007/978-1-4939-3569-7_14
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Keating AE
中科院分区：
文献类型：
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作者：
Reich LL;Dutta S;Keating AE
文库方法广泛用于研究蛋白质-蛋白质相互作用，高通量筛选或选择和测序可以鉴定蛋白质靶标的大量肽配体。在本章中，我们描述了一个称为“SORTCERY”的过程，它可以高精度地对库成员对目标的亲和力进行排名。 SORTCERY 遵循三步协议。首先，荧光激活细胞分选 (FACS) 用于根据酵母展示的肽配体对靶标的亲和力对它们进行分选。其次，所有排序池都是深度排序的。第三，分析所得数据以创建排名。我们展示了 SORTCERY 在抗凋亡调节剂 Bcl-xL 的肽配体排序问题中的应用。
Library methods are widely used to study protein-protein interactions, and high-throughput screening or selection followed by sequencing can identify a large number of peptide ligands for a protein target. In this chapter we describe a procedure called "SORTCERY" that can rank the affinities of library members for a target with high accuracy. SORTCERY follows a three-step protocol. First, fluorescence activated cell sorting (FACS) is used to sort a library of yeast displayed peptide ligands according to their affinities for a target. Second, all sorted pools are deep sequenced. Third, the resulting data are analyzed to create a ranking. We demonstrate an application of SORTCERY to the problem of ranking peptide ligands for the anti-apoptotic regulator Bcl-xL.