A mathematical framework for interpreting the molecular code of the tumor suppressor p53

点击翻译按钮获取中文摘要

Post-translational modifications (PTMs) of proteins play a fundamental role in cellular signaling and regulation due to their fast dynamics and therefore are essential for dynamic cellular information processing. A single protein molecule can have multiple modification sites and thus can exhibit different patterns of co-occurring PTMs (hereafter called "modforms"). These patterns of PTMs act as a "molecular code" on that protein and confers information about the cellular condition that can be read by downstream processes. The tumor suppressor p53 is a key cellular regulator that harbors over 100 modification sites enabling it to integrate diverse stress signals and in response to activate or suppress hundreds of genes leading to different cell fate decisions. The overall aim of this project is to understand PTM encoding using the transcription factor and tumor suppressor p53 as an important biological example in cellular signaling and regulation.Elucidating PTM encoding and knowing the actual PTM state of a protein is essential to understand cellular information processing, however, the problem becomes more complicated considering that there is not only one but a population of molecules present in a cell leading to a distribution of different modforms. Current mass spectrometry methods that are used to estimate the modform distribution do not provide sufficient data/information to reconstruct the modform distribution of heavily modified proteins such as p53. Assuming, for instance, that p53 has approximately 100 modification sites and that all are binary (such as phosphorylation where an amino acid residue is either phosphorylated or not) then the protein exhibits 2^100 ≈ 10^30 possible modforms. This means that the solution of reconstructing the modform distribution exists in a space of dimension 2^100. I aim to develop a novel method that circumvents the high dimensional solution space to estimate a region in which the modform distribution must lie. Ideally, exploring the modform region will then help to detect which sites are most likely modified. These predictions shall guide future experiments with the goal to identify prevalent modified sites that determine cell fate outcome. Understanding the complicated PTM coding of p53 will have important implications for cell fate decision making and the understanding of cancer development and therapies.