Dr Isabell Bludau

Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes

Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.

Dr Andreas Damianou

Profiling dynamic K-Ras interactomes via optimised APEX-2 proximity labelling

Several recently developed proximity labelling techniques such as BioID, TurboID and APEX-2 add to the arsenal of methods that enable the capture of protein-protein interactions as well as the exploration of different molecular and cellular environments. A hallmark of APEX-2 proximity labelling is that it occurs at a timescale of seconds, which is advantageous for the capture of transient interactions as well as fast molecular changes. In this study, APEX-2 proximity labelling was applied in conjunction with LC-MS/MS to investigate the dynamic protein-protein interactions of the oncogene K-Ras. K-Ras is a small GTPase which acts as a transductor protein to deliver signals from an external environmental stimulus to the cell nucleus, controlling several essential functions such as progression of cells through the cell cycle, promotion of angiogenesis, cellular adhesion, migration and cell invasion. Ras GTPases have the ability to bind either to GTP or GDP, which is essential for its role as a molecular switch. This role is apparent in the case of serum-starved cells, where it primarily binds to GDP, which inactivates K-Ras and shuts down its signal transduction. K-Ras activating mutations have been previously described as cancer driving mutations that can lead to cellular transformation as well as an increase in chemotherapy resistance. Here, APEX-2 labelling was used to compare wild type K-Ras to three activating mutations, G12D, G13D and Q61H, respectively, under both starvation and FCS stimulation conditions. These conditions allowed for the clear elucidation and comparison of the environment and interacting partners of K-Ras, both in its active and inactive forms. This study gives us the opportunity to evaluate several steps that are critical for a successful proximity labelling experiment such as correct localisation of the bait protein as well as appropriate protein expression levels. Additionally, data were analysed using different methods, including SAINTexpress, to identify which method is more appropriate and reliable. The identification of several known interactors of K-Ras including a-RAF and LZTR1 indicate the ability of this approach to capture interaction partners. We further examine how these protein’s interactions and the K-Ras environment vary with the different mutations, revealing unique protein interactors of different K-Ras mutants under different environmental conditions.