Underpinning much of the research at the NSW Systems Biology Initiative (SBI) is a drive to better understand how the dynamics of protein-protein interaction (PPI) networks are regulated. In exploring this question we have devoted particular attention to the study of arginine and lysine methylation – post-translational modifications capable of altering PPIs – in the model organism Saccharomyces cerevisiae. We have created a foundation for this work by producing a detailed map of methyltransferase-substrate interactions embedded within a broader network of PPIs: the yeast intracellular methylation network.
This presentation will describe how we are now studying the dynamics of this network using a technique known as size exclusion chromatography-protein correlation profiling (SEC-PCP). SEC-PCP uses mass spectrometry to detect SEC fractionation profiles for individual proteins in native complexes; similar fractionation profiles are correlated under the assumption that proteins in a common complex co-fractionate.
We have created a reference PCP dataset from 280 SEC fractions of wild-type yeast lysate (70 fractions × 4 biological replicates), identifying >200 core complexes in their native form, including complexes that contain known methyltransferase substrates. We are now in process of producing PCP data from methyltransferase knockout yeast strains to study how altered methyltransferase-substrate interactions affect PPIs. We are also characterising absolute stoichiometries of individual methylation sites in these core complexes using a library of 34 internal standard heavy isotope-labeled synthetic peptides. This has, for example, provided insight into the methyltransferase activity on EF1α – a methyltransferase substrate that we observe in at least 3 distinct core complexes – when EF1α is associated with different PPIs.
This presentation will also reflect more broadly on our efforts to create reference datasets for systems biology model organisms. In particular it will consider how underlying values driving systems biology research relate to data quality, and spread of false positive data.