Automated prediction of protein complex substructure from genomics and proteomics data

根据基因组学和蛋白质组学数据自动预测蛋白质复杂子结构

• 批准号: 341427-2010
• 负责人: Bader, Gary
• 金额: $ 1.97万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572254
• 关键词: Automated; prediction; protein; complex; substructure

Experimental determination of interactions among cellular components, including biochemical reactions, protein-protein and genetic interactions, charts a map of the cell. This map defines gene function and is the basis of our models of biological processes. This knowledge will eventually enable us to predict phenotype from genotype, understand the genetic basis of disease and engineer new biological systems for industrial applications. Recent biotechnology advances and the availability of completely sequenced genomes for hundreds of organisms have led to genome-scale mapping of networks of interacting genes, their RNA and protein products and small molecules. For instance, over 145,000 protein-protein, protein-DNA and genetic interactions for the yeast Saccharomyces cerevisiae have been mapped using techniques such as yeast two-hybrid, chromatin immunoprecipitation on microarrays and immunoprecipitation coupled to mass spectrometry, with significant contributions by Canadian research groups. Further, molecular profiling methods, such as RNA level measurement by microarray or protein quantitation by mass spectrometry, provide insight into the dynamic levels of cellular components. This extensive cellular network information demands the development of powerful computational tools to facilitate data integration and interpretation outside the direct capacity of the human brain. I aim to address this challenge by developing novel biological network visualization and analysis methods to discover new information about protein complexes. These methods will help us, as biologists, effectively navigate the cell map at multiple levels of detail and use it to make new discoveries.

实验测定细胞成分之间的相互作用，包括生化反应，蛋白质-蛋白质和遗传相互作用，绘制细胞图。该图谱定义了基因功能，是我们生物过程模型的基础。这些知识最终将使我们能够从基因型中预测表型，了解疾病的遗传基础，并为工业应用设计新的生物系统。 最近的生物技术进步和数百种生物体基因组的完全测序的可用性导致了相互作用基因、其RNA和蛋白质产物以及小分子网络的基因组规模绘图。例如，在加拿大研究小组的重大贡献下，利用酵母双杂交、微阵列染色质免疫沉淀和免疫沉淀与质谱联用等技术，绘制了酿酒酵母的145 000多个蛋白质-蛋白质、蛋白质-DNA和遗传相互作用图谱。此外，分子谱分析方法，如通过微阵列的RNA水平测量或通过质谱法的蛋白质定量，提供了对细胞组分的动态水平的了解。 这种广泛的细胞网络信息需要开发强大的计算工具，以促进人类大脑直接能力之外的数据整合和解释。我的目标是通过开发新的生物网络可视化和分析方法来解决这一挑战，以发现有关蛋白质复合物的新信息。这些方法将帮助我们，作为生物学家，在多个细节层次上有效地导航细胞图谱，并利用它来做出新的发现。