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

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，并利用生物信息学和蛋白质组学方法提高我们在蛋白质形态水平上对蛋白质的功能理解。现在人们普遍认识到，构成生物机制多样性和复杂性的是蛋白质的变异，而不是基因的变异。选择性剪接和翻译、RNA编辑和翻译后修饰（PTM）产生不同的蛋白质分子，称为蛋白质形态（proteoforms）。由同一基因编码的蛋白质形态在结构、功能和定位上可能存在差异。经典的“一个基因、一个蛋白质、一个功能”概念并没有捕捉到蛋白质形态的多样性。当前的基因或规范的以蛋白质为中心的数据库将分子功能分配给基因/蛋白质，而不区分蛋白质形态。这对于基因组和蛋白质组的高通量实验和数据库的自动信息检索来说尤其成问题。显然，我们需要一种算法，能够从组学数据中更全面地识别蛋白质形态及其潜在的翻译后修饰，并需要新的数据模型和数据库，以反映蛋白质形态水平的复杂性和功能关系。在过去的几年里，我们通过开发数据模型，公共数据库以及网络推理算法和数据分析工具来解决这个问题，重点关注一种特定的PTM蛋白酶介导的蛋白质水解过程，将蛋白质转化为稳定的裂解蛋白质形态。拟议的研究计划扩展了我们以前成功的工作，现在涵盖了所有的变形形式。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)数据库和工具来捕获蛋白质形态的功能复杂性(2)统计和机器学习方法来从MS数据中识别蛋白质形态(3)蛋白质形态功能的反卷积和聚类。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日常审问生物系统的方式。它将使高通量数据功能分析的细节、精度和粒度达到一个新的水平，并支持生物技术、健康和农业行业中更具体的标记和化合物的开发。

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 clusteringThe 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.