DEEPN strategy for large-scale differential protein interaction studies

 描述（由申请人提供）：了解特定蛋白质如何驱动生物过程的最佳策略之一是首先找到其相互作用的蛋白质。目前鉴定蛋白质相互作用物库的方法受到多种限制。大多数鉴定蛋白质-蛋白质相互作用的方法偏向于支持丰富配偶体之间的高亲和力相互作用。他们也缺乏一种简单的方法来比较两种不同蛋白质或特定蛋白质版本之间的相互作用组。这里的目标是进一步开发一种新的方法，称为DEEPN（动态富集蛋白质网络的评价）作为一种手段来定义稳定和短暂的构象敏感的结合相互作用。我们认为，DEEPN避免了与其他方法相关的许多缺点。DEEPN的创新之处在于它利用下一代高通量DNA测序方法来定量跟踪编码相互作用伴侣的质粒群体的进化， 酵母双杂交（Y2 H）测定形式。我们对DEEPN的初步研究表明，当应用于寻找与特定蛋白质相互作用的因子时，它是强大的，信息丰富的。DEEPN的一个重要优势是它可以发现“差异相互作用组”，即结合蛋白质的一种构象与另一种构象的蛋白质的库。我们建议将DEEPN开发成一个可导出的工作流程，可供需要全面识别低亲和力和高亲和力相互作用的方法的新手实验室使用。DEEPN将是一个布恩的任何研究计划，重点是找出如何激活蛋白质（如激酶，GTP酶，磷酸化效应）差异相互作用与下游蛋白质的合作伙伴。它还将帮助研究人员确定致病点突变的生化基础，其基本原理是其中一些类型的变化可能会改变蛋白质相互作用的库（或这些相互作用的强度）。最终，DEEPN可以解决对一种综合技术的迫切需求，以找到特定的蛋白质相互作用，这种技术不仅便宜，而且可定制，适应性强，并且可供RO 1驱动的实验室使用。

 DESCRIPTION (provided by applicant): One of the best strategies to understand how a particular protein drives a biological process is to first find its interacting proteins. Current methods to identify the repertoire of protein interactors suffer from multiple limitations. Most methods to identify protein-protein interactions are biased in favor of high affinity interactions between abundant partners. They also lack an easy way to compare the interactome between two different proteins or versions of a particular protein. The goal here is to further develop a new method, termed DEEPN (Dynamic Enrichment for Evaluation of Protein Networks) as a means to define stable and transient conformationally sensitive binding interactions. We believe that DEEPN circumvents many of the shortcomings associated with other methods. DEEPN is innovative in that it utilizes next-generation, high- throughput DNA sequencing approaches to quantitatively follow the evolution of a population of plasmids that encode interacting partners in a yeast 2-hybrid (Y2H) assay format. Our preliminary studies with DEEPN showed it to be robust and information rich when applied to finding factors that interact with particular proteins f interest. An important strength of DEEPN is that it can discover "differential interactomes", that being the repertoire of proteins that bind one conformation of a protein vs another. We propose to develop DEEPN into an exportable workflow that can be used by novice labs needing a method for comprehensive identification of low- and high-affinity interactions. DEEPN would be a boon for any research program focused on finding out how activated proteins (e.g. Kinases, GTPases, phosphorylated effectors) differentially interact with downstream protein partners. It would also help researchers determine the biochemical basis of disease- causing point mutations, with the rationale that some of these types of changes might change the repertoire of protein interactions (or the strength of those interactions). Ultimately, DEEPN could address the pressing need for a comprehensive technique to find specific protein interactions that is not only inexpensive but also customizable, adaptable, and accessible to RO1-driven labs.