Computational analysis of large-scale proteomics datasets and protein-protein interaction networks

大规模蛋白质组数据集和蛋白质-蛋白质相互作用网络的计算分析

• 批准号: RGPIN-2017-06849
• 负责人: LavalléeAdam, Mathieu
• 金额: $ 2.26万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683785
• 关键词: Computational; analysis; large; scale; proteomics

Proteins are molecules that govern the behaviour of the cell. To accomplish their function, proteins typically interact with each other to form larger macromolecules called protein complexes. Mass spectrometry allows the identification and quantification of proteins and of their interactions. However, currently used software packages struggle to identify and quantify proteins and interactions that have a low abundance. This makes it difficult to obtain a good understanding of the protein interactions that contribute to the assembly and regulation of protein complexes. In our research program, we will design algorithms that will integrate genomics and interactomics data to facilitate the identification of proteins using mass spectrometry. We will also develop software packages that will identify and quantify proteins while they are being analyzed by mass spectrometry instruments. This will allow us to automatically direct the instrument towards the analysis of lower abundance proteins. Finally, protein interactions that are detected with mass spectrometry can be grouped into networks of interactions. We will analyze such networks of interactions to identify protein complexes and understand which interactions are vital for their assembly, their transport to the proper location in the cell, and their regulation. ****** Our research program will produce computational tools that will be designed to be easily integrated with current state-of-the-art mass spectrometry instrumentation and analysis pipelines. They will highlight novel data acquisition strategies for mass spectrometry instruments to identify and quantify more proteins. Our tools will also influence the design of the next generation of instruments. The algorithms we will develop will pave a new way for the design of novel families of computational methods that will use protein interaction networks to gain new biological knowledge. Our approaches will enable biologists and biochemists to get a more comprehensive characterization of the mechanisms at work in the organism under study. Indeed, our software packages will produce the identification and quantification of more proteins and protein interactions. They will allow the mapping of low abundance protein interactions, which are critical to the assembly, transport, and regulation of protein complexes. The characterization of these regulating interactions has a number of applications in many fields related to molecular biology, including agriculture and biofuel production. Mapping such interactions can lead in the future to the discovery of plant resistance pathways to pathogens or frost to increase crop yield in Canada. Similarly, describing such interactions in bacteria could favour a more efficient production of biofuels. Finally, our methods will provide a better understanding of the biology of the cell and of its molecular mechanisms.

蛋白质是控制细胞行为的分子。为了完成它们的功能，蛋白质通常会相互作用，形成更大的大分子，称为蛋白质复合物。质谱法可以对蛋白质及其相互作用进行鉴定和定量。然而，目前使用的软件包难以识别和量化具有低丰度的蛋白质和相互作用。这使得很难很好地理解蛋白质相互作用，这些相互作用有助于蛋白质复合物的组装和调节。在我们的研究项目中，我们将设计集成基因组学和相互作用组学数据的算法，以促进使用质谱法鉴定蛋白质。我们还将开发软件包，在质谱仪器分析蛋白质时识别和量化蛋白质。这将使我们能够自动引导仪器分析低丰度的蛋白质。最后，用质谱法检测到的蛋白质相互作用可以分组成相互作用网络。我们将分析这种相互作用网络，以识别蛋白质复合物，并了解哪些相互作用对它们的组装、它们在细胞中运输到适当位置以及它们的调节至关重要。******我们的研究计划将生产计算工具，这些工具将被设计成与当前最先进的质谱仪器和分析管道轻松集成。他们将重点介绍质谱仪器的新数据采集策略，以识别和量化更多的蛋白质。我们的工具也将影响下一代仪器的设计。我们将开发的算法将为设计新的计算方法家族铺平新的道路，这些方法将使用蛋白质相互作用网络来获得新的生物学知识。我们的方法将使生物学家和生物化学家能够更全面地描述所研究的生物体中起作用的机制。事实上，我们的软件包将产生更多的蛋白质和蛋白质相互作用的鉴定和定量。它们将允许绘制低丰度蛋白质相互作用的图谱，这对蛋白质复合物的组装、运输和调节至关重要。这些调节相互作用的特征在分子生物学相关的许多领域有许多应用，包括农业和生物燃料生产。绘制这种相互作用的地图可以在未来发现植物对病原体或霜冻的抗性途径，从而提高加拿大的作物产量。同样，描述细菌中的这种相互作用可能有助于更有效地生产生物燃料。最后，我们的方法将提供对细胞生物学及其分子机制的更好理解。