Bioinformatics Seminar
Time:
Venue: Na
20 August 2019
NaSignalling networks in the Analysis of Phospho/Proteomic Data
Hannah HuckstepWEHI Bioinformatics
Signalling networks have the potential to provide useful insight into mechanisms driving disease progression. Proteomics and phosphoproteomics represent attractive datasets for this work as they directly measure the changes involved in signal propagation. Phosphorylation is one of the most common PTMs involved in regulating biological processes and the phosphorylation (or dephosphorylation) of a protein can have an activating or inhibiting effect. It has been estimated that as much as a third of the eukaryotic proteome is phosphorylated at one time indicating the significance of phosphorylation in modulating cell behaviour. Nevertheless ; the simple identification and quantification of phosphoproteins from different conditions is not sufficient to reconstruct the mechanisms underpinning the observed differences. Understanding how altered proteins cooperate to modulate function requires exploration of the molecular interaction networks underpinning these changes.
An important first step in this process is the derivation of a network capturing known interactions. Most knowledgebases today organise such information into pathways ; which do not properly capture the global flow of information across the entire signalling system. Here we conduct an overlap analysis consisting of a multi-factor comparison of several widely used knowledgebases in order to characterise their coverage of the global signalling network. Using these public signalling data ; we designed a customised network more amenable to mapping of phospho/proteomic data using the flexible graphical database ; neo4J. We explore how peptide level phosphoproteomics measurements can be mapped to this framework to interpret the functional consequences of the observed changes in protein phosphorylation. To facilitate this ; we have developed a suite of methods to interrogate the global signalling network and extract informative subnetworks. This approach will enable a more unbiased and complete analysis to be performed over networks encompassing specific proteins and phosphoproteins of interest.;;;