A novel, data driven-paradigm for the sensitive identification of phosphopeptides

01 January 2018 → 31 December 2021
Research Foundation - Flanders (FWO)
Research disciplines
No data available
Project description

Post-translational modifications (PTMs) are molecular groups that are added to proteins after translation. As is the case throughout most of proteomics research, the method of choice for their analysis is tandem mass spectrometry. However, because the analysis of PTMs is not as straightforward as the analysis of normal proteins, many PTMs remain undetected. One of the most abundant and most studied PTMs is phosphorylation, which plays a key role in signal transduction, and is disrupted in diseases as varied as Alzheimer’s and cancer. Because of this large interest and broad application domain, my research will focus on improving the detection of phosphorylation. My objective is to create a novel method for the identification of phosphorylated proteins, based on the re-use of the large amounts of data in public proteomics databases. Indeed, these data can be used to train machine learning algorithms that can improve the identification of phosphorylated proteins. This can occur because existing data can teach the machine learning algorithms to predict an accurate, theoretical spectral library for all possible phosphorylated peptides, complete with detailed peak intensity information. This should lead to an unbiased search engine with a much higher sensitivity than existing methods. Moreover, this data-driven approach can easily be adapted to all current, and possible future fragmentation techniques.