Bioinformatics resources for genome-wide assessment of protein function at the proteoform level

用于在蛋白质水平水平上对蛋白质功能进行全基因组评估的生物信息学资源

• 批准号: RGPIN-2018-05645
• 负责人: Lange, Philipp
• 金额: $ 2.04万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713759
• 关键词: Bioinformatics; resources; genome; wide; assessment

The long-term objective of my research is to increase our ability to detect post-translational protein modification in high-throughput omics data and improve our functional understanding of proteins at the proteoform level using bioinformatics and proteomics approaches. It is now widely recognized that variation in proteins, rather than of genes, underlies the diversity and complexity of biological machinery. Alternative splicing and translation, RNA editing and post-translational modification (PTM) result in distinct protein molecules called proteoforms'. Proteoforms encoded by the same gene can differ in structure, function and localization. The diversity of proteoforms is not captured by the classic "one geneone proteinone function" concept. Current gene or canonical' protein-centric databases assign a molecular function to a gene / protein and do not distinguish among proteoforms. This becomes particularly problematic for genome- and proteome-wide high-throughput experiments and automated information retrieval from databases. There is a clear need for algorithms enabling more comprehensive identification of proteoforms and their underlying post-translational modifications from omics data and for new data models and databases that reflect the complexity and functional relationships at the proteoform level. Over the past years, we have addressed this by developing data models, a public database as well as network inference algorithms and data analysis tools with a focus on one particular PTM protease mediated proteolytic processing of proteins into stable cleaved proteoforms. The proposed research program expands on our successful previous work to now encompass all proteoforms. We will use an interdisciplinary approach integrating bioinformatics with experimental validation to address three specific aims: (1) A Database and tools to capture the functional complexity of proteoforms (2) Statistical and machine learning approaches to identify proteoforms from MS data (3) Proteoform function de-convolution and clustering The program provides excellent interdisciplinary training for undergraduate and graduate students from computing and biology backgrounds. The work has the potential to transform how we routinely interrogate biological systems. It will enable a new level of detail, precision and granularity in the functional analysis of high-throughput data and support development of more specific markers and compounds in the biotechnology, health and agriculture industries.

我研究的长期目标是提高我们在高通量组学数据中检测翻译后蛋白质修饰的能力，并使用生物信息学和蛋白质组学方法在蛋白质组水平上提高我们对蛋白质功能的理解。 现在人们普遍认识到，蛋白质的变异，而不是基因的变异，是生物机制多样性和复杂性的基础。选择性剪接和翻译、RNA编辑和翻译后修饰（PTM）导致称为蛋白形式的不同蛋白质分子。由相同基因编码的蛋白质型在结构、功能和定位上可以不同。蛋白质型的多样性并不被经典的“一个基因一个蛋白质一个功能”的概念所捕获。目前的基因或典型的蛋白质中心数据库将分子功能分配给基因/蛋白质，并且不区分蛋白质型。这对于基因组和蛋白质组范围的高通量实验和从数据库中自动检索信息变得特别成问题。 有一个明确的需要算法，使更全面的识别蛋白质和其潜在的翻译后修饰组学数据和新的数据模型和数据库，反映在蛋白质水平的复杂性和功能关系。在过去的几年里，我们已经解决了这个问题，开发数据模型，公共数据库以及网络推理算法和数据分析工具，重点是一个特定的PTM蛋白酶介导的蛋白质的蛋白水解加工成稳定的切割proteoforms。 拟议的研究计划扩展了我们以前成功的工作，现在包括所有的蛋白形式。我们将使用跨学科的方法，将生物信息学与实验验证相结合，以解决三个具体目标： (1)一个数据库和工具，以捕捉蛋白质的功能复杂性 (2)从MS数据中识别蛋白质型的统计和机器学习方法 (3)Proteoform函数去卷积和聚类 该计划为来自计算和生物学背景的本科生和研究生提供优秀的跨学科培训。这项工作有可能改变我们常规询问生物系统的方式。它将使高通量数据的功能分析的细节，精度和粒度达到新的水平，并支持生物技术，健康和农业行业中更具体的标记物和化合物的开发。