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PPiSeq: High-Throughput Protein-Protein Interaction Sequencing

PPiSeq：高通量蛋白质-蛋白质相互作用测序

基本信息

批准号：
9307578
负责人：
SASHA F LEVY
金额：
$ 42.25万
依托单位：
STATE UNIVERSITY NEW YORK STONY BROOK
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-16 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9307578
关键词：
Alpha Cell Antifungal Agents Biological Assay Buffers Catalogs Cells Cellular biology Chromosomes Chromosomes, Human, Pair 3 Communities Complement DNA Diploidy Drug Targeting Drug resistance Environment Fungal Drug Resistance Future Genes Genotype Global Change Growth Haploidy Left Libraries Measurement Measures Mutation Partner in relationship Pharmaceutical Preparations Pharmacology Phenotype Play Process Protein Engineering Protein Fragment Proteins Resistance Resistance development Resources Role Saccharomyces cerevisiae Stress Structure System Technology Temperature Testing Time Translating Variant Work Yeasts assault cell growth cost drug discovery experimental study fitness genome-wide high throughput screening in vivo innovation insight macromolecule next generation sequencing novel therapeutics protein complex protein protein interaction public health relevance response sequencing platform tool yeast protein

项目摘要

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.