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

点击翻译按钮获取中文摘要

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.