Mapping the first half of the REFERENCE human binary protein interactome

描述（由申请人提供）：复杂的生物系统和细胞网络是大多数基因型与表型关系的基础。在过去的十年中，网络生物学的基本概念已经被描述，强调为什么细胞网络在生物学中是重要的考虑。重要的是，越来越清楚的是，需要更多高质量的经验衍生数据集来更好地描述生物网络和基因型与表型的关系。生物的相互作用组是由其所有大分子之间在生理相关的动态范围内发生的一整套相互作用所形成的网络，包括蛋白质-蛋白质、dna -蛋白质、rna -蛋白质和RNA-RNA相互作用。在本提案中，我们专注于高通量（HT），蛋白质组尺度的映射，我们称之为REFERENCE人类二元蛋白质-蛋白质相互作用组网络图。这个应用程序的主要创新使我们能够在这个十年结束前为完成这个参考地图定义一个清晰的路线图。在即将到来的周期中，我们将把人类HT二元相互作用组图谱从当前周期的里程碑——约15%的覆盖率扩大到约50%。我们还将简要讨论我们如何预见此后进一步扩展到接近完成的程度。DNA测序数据的积累在20世纪90年代为人类基因组测序计划爆炸，当时四个关键要素组装：i)覆盖大部分基因组的cosmids， BAC和YAC克隆资源；ii)自动化激光荧光测序，iii)用于系统评估测序数据质量的PHRED评分，以及iv)开发“不干涉”自动化实验步骤。我们在下面描述人类二元交互组映射项目如何达到类似的爆炸阶段：i)对ORFeome协作（OC）做出了重大贡献，我们现在拥有了几乎完整的蛋白质编码ORF克隆资源，ii)我们开发了一种新的策略，将下一代测序的力量应用于相互作用组制图，iii)我们发布了一个新的经验框架，系统地评估相互作用组制图数据质量，iv)我们将描述新的“不干涉”自动化策略，大大提高了吞吐量并降低了成本。我们的具体目标是：i)将人类二进制相互作用组图谱扩展到OC克隆的蛋白质编码基因的完整补充，ii)使REFERENCE人类二进制相互作用组网络图谱的覆盖率达到50%，以及iii)扩大我们新绘制的人类二进制相互作用组网络的全球网络分析。

DESCRIPTION (provided by applicant): Complex biological systems and cellular networks underlie most genotype to phenotype relationships. In the last decade, basic concepts of network biology have been described, emphasizing why cellular networks are important to consider in biology. Importantly, it is becoming increasingly clear that more high quality empirically derived datasets are needed to better describe biological networks and genotype to phenotype relationships. The interactome of an organism is the network formed by the complete set of interactions that can occur in a physiologically relevant dynamic range between all its macromolecules, including protein-protein, DNA-protein, RNA-protein, and RNA-RNA interactions. In this proposal, we focus on high-throughput (HT), proteome-scale mapping of what we refer to as the REFERENCE human binary protein-protein interactome network map. Major innovations in this application enable to define a clear roadmap for completion of this REFERENCE map by the end of this decade. During this coming cycle, we will expand the human HT binary interactome map from ~15% coverage, which is the milestone of the current cycle, to ~50%. We will also briefly discuss how we foresee further expansion to near completeness thereafter. The accumulation of DNA sequencing data exploded for the Human Genome Sequencing project in the 1990s when four crucial elements were assembled: i) cosmids, BAC, and YAC clone resources covering most of the genome; ii) automated laser-fluorescence sequencing, iii) the PHRED score used to systematically assess sequencing data quality, and iv) the development of "hands-off" automated experimental steps. We describe below how the human binary interactome mapping project is reaching a similarly exploding phase: i) having significantly contributed to the ORFeome Collaboration (OC) we now have a nearly complete protein-coding ORF clone resource, ii) we developed a new strategy to apply the power of next-generation sequencing to interactome mapping, iii) we have published a new empirical framework that systematically assess interactome mapping data quality, and iv) we will describe new "hands-off" automated strategies that greatly increase throughput and decrease cost. Our specific aims are: i) to expand human binary interactome mapping to a full complement of protein-coding genes cloned by OC, ii) to reach ~50% coverage of the REFERENCE human binary interactome network map, and iii) to expand global network analyses of our newly mapped human binary interactome network. PUBLIC HEALTH RELEVANCE: The availability of (nearly) complete genome sequences for several model organisms and for human is changing the way scientists formulate and address biological questions. With large numbers of protein predictions, the traditional one-at-a-time approach can now be complemented by more global strategies that consider all proteins at once. Such approaches, referred to as "systems biology" have the ultimate goal of providing quantitative and dynamic models to describe biological processes. One major impediment to this prospect however is that most predicted proteins have not yet been experimentally characterized in detail. Interactome maps can be used to formulate functional hypotheses for thousands of uncharacterized genes. In addition, global features of the resulting interactome networks have been proposed that provide worthwhile biological insights. From these insights and hypotheses, a better understanding of disease processes and better strategies for therapeutic intervention are anticipated.