PPiSeq: High-Throughput Protein-Protein Interaction Sequencing

 描述(由申请人提供)：大规模蛋白质-蛋白质相互作用分析在蛋白质相互作用网络、药物活性和蛋白质工程的研究中广泛有用。蛋白质片段互补(PCA)分析通过将相互作用的强度转换为相对适合度来筛选酿酒酵母体内蛋白质-蛋白质相互作用(PPI)。对于每个PPI，含有蛋白质片段的单倍体酵母对进行交配，得到的二倍体逐一进行检测。我们开发了三项技术，使PCA具有更高的可重复性和更高的吞吐量：1)随机DNA条码系统，使我们能够构建数百万个唯一的条码酵母菌株；2)双条码系统，它将同源染色体上的两个条码转移到同一染色体上非常接近的位置；3)混合适合度分析，允许通过下一代测序同时准确测量数百万个条码基因的相对适合度。我们建议将主成分分析与上述三种创新相结合，生成一个大规模并行的蛋白质-蛋白质相互作用测序平台(PPiSeq)。随机条形码被插入酵母中，并与现有的PCA菌株交配。条形码单倍体PCA池的交配以及条形码在体内和整体的移位产生二倍体PCA菌株，每个菌株具有代表特定PPI的双重条形码。细胞库的增长和双条形码的测序产生了池中每个双条形码的准确适合度测量，可以转化为每个成对蛋白质组合的相互作用分数。与传统的PCA相比，PPiSeq有几个显著的优势：它快速、廉价、高度可扩展，并且进入门槛低。值得注意的是，PPiSeq的吞吐量与PCA菌株的数量呈二次曲线增长，而其成本则以下一代测序成本的速度下降。在这里，我们将构建一个由~655,000个唯一PPI和~600万个双条码组成的大型二倍体PPiSeq文库，并使用该文库通过池化生长同时测量每个PPI的相互作用分数(AIM 1)。与传统主成分分析相比，PPiSeq的另一个主要优势是跨扰动的可重复性。也就是说，一旦构建，所有的成对相互作用都可以很容易地在新的环境中重新分析。在这里，我们将分析酵母蛋白相互作用组在温度梯度(AIM 2)和三种抗真菌药物(AIM 3)存在下的变化。这项工作将提供第一个基因组规模的视图，了解蛋白质相互作用组如何在扰动中变化。构建的PPiSeq文库将作为未来扰动研究的资源提供给科学界。此外，这项工作将为未来大规模的PPI屏幕奠定基础，例如那些涉及药物发现和蛋白质工程的屏幕。

 DESCRIPTION (provided by applicant): Large-scale protein-protein interaction assays are widely useful in studies of protein interaction networks, drug activity, and protein engineering. The protein-fragment complementation (PCA) assay screens for in vivo protein-protein interactions (PPIs) in Saccharomyces cerevisiae by converting the strength of an interaction to a relative fitness. For each PPI, protein-fragment-containing haploid yeast pairs are mated and the resulting diploids are tested one-at-a-time. We have developed three technologies that will make PCA more repeatable and higher throughput: 1) a random DNA barcode system that allows us to construct millions of uniquely barcoded yeast strains, 2) a double barcode system that translocates two barcodes on homologous chromosomes to close proximity on the same chromosome, and 3) a pooled fitness assay that allows for accurate measurements of the relative fitness of millions of barcoded genotypes simultaneously via next- generation sequencing. We propose to combine PCA with the above three innovations to generate a massively parallel protein-protein interaction sequencing platform (PPiSeq). Random barcodes are inserted into yeast and are mated to existing PCA strains. Mating of barcoded haploid PCA pools and translocation of barcodes in vivo and en masse yields diploid PCA strains, each with a double barcode representing a specific PPI. Growth of cell pools and sequencing of double barcodes yields an accurate fitness measurement of each double barcode in the pool, which can be translated to an interaction score for each pairwise protein combination. PPiSeq will have have several significant advantages over traditional PCA: it is fast, cheap, highly scalable, and has a low barrier to entry. Notably, PPiSeq throughput scales quadratically with the number of PCA strains, while its costs decline at the rate of next generation sequencing costs. Here, we will construct a large diploid PPiSeq library consisting of ~655,000 unique PPIs and ~6 million double barcodes and use this library to measure the interaction score of each PPI simultaneously via pooled growth (AIM 1). One additional major advantage of PPiSeq over traditional PCA is repeatability across perturbations. That is, once constructed, all pairwise interactions can be re-assayed in new environments easily. Here, we will assay how the yeast protein interactome changes in a heat gradient (AIM 2) and in the presence of three antifungal drugs (AIM 3). This work will provide the first genome-scale view of how the protein interactome changes across perturbations. The constructed PPiSeq library will be provided as a resource to the scientific community for future perturbation studies. Additionally, this work will set the stag for future large-scale PPI screens, such as those involved in drug discovery and protein engineering.