SORTCERY-A High-Throughput Method to Affinity Rank Peptide Ligands.
SORTCERY-A High-Throughput Method to Affinity Rank Peptide Ligands.
复制标题
sortcery-a高通量方法的亲和力等级肽配体。
DOI：
10.1016/j.jmb.2014.09.025
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发表时间：
2015-06-05
影响因子：
5.6
通讯作者：
Keating, Amy E.
中科院分区：
文献类型：
--
作者：
Reich, Lothar Luther;Dutta, Sanjib;Keating, Amy E.
揭示多肽和蛋白质序列之间的关系以及结合特性对于成功预测、重新设计和抑制蛋白质之间的相互作用至关重要。系统收集的将蛋白质序列与结合联系起来的数据对于阐明蛋白质相互作用的决定因素很有价值，但在文献中很少，因为这样的数据在实验上很难产生。在这里，我们描述了SORTCERY，这是一种高通量方法，我们已经使用它来根据数百个酵母展示的肽与目标相互作用伙伴的亲和力来对其进行排序。该程序包括文库的荧光激活细胞分选(FAC)、分选池的深度测序和下游计算分析。我们开发了理论模型和统计工具，帮助规划这些阶段。我们通过对1026个BH3多肽与抗凋亡蛋白Bcl-xL的亲和力进行排名，证明了SORTCERY的有效性。我们的结果与19个解离常数从0.1到60 nm的单肽的亲和力测量结果惊人地一致。高分辨率排序可以用来提高我们对序列-功能关系的理解，并支持用于预测和设计新的相互作用的计算模型的开发。
Uncovering the relationships between peptide and protein sequences and binding properties is critical for successfully predicting, re-designing and inhibiting protein-protein interactions. Systematically collected data that link protein sequence to binding are valuable for elucidating determinants of protein interaction, but are rare in the literature because such data are experimentally difficult to generate. Here we describe SORTCERY, a high-throughput method that we have used to rank hundreds of yeast displayed peptides according to their affinities for a target interaction partner. The procedure involves fluorescence-activated cell sorting (FACS) of a library, deep sequencing of sorted pools, and downstream computational analysis. We have developed theoretical models and statistical tools that assist in planning these stages. We demonstrate SORTCERY’s utility by ranking 1026 BH3 peptides with respect to their affinities for the anti-apoptotic protein Bcl-xL. Our results are in striking agreement with measured affinities for 19 individual peptides with dissociation constants ranging from 0.1 to 60 nM. High-resolution ranking can be used to improve our understanding of sequence-function relationships, and to support the development of computational models for predicting and designing novel interactions.
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